
==== Front
PLoS Biol
PLoS Biol
plos
PLOS Biology
1544-9173
1545-7885
Public Library of Science San Francisco, CA USA

39186804
10.1371/journal.pbio.3002660
PBIOLOGY-D-24-01303
Research Article
Biology and Life Sciences
Physiology
Electrophysiology
Membrane Potential
Action Potentials
Biology and Life Sciences
Physiology
Electrophysiology
Neurophysiology
Action Potentials
Biology and Life Sciences
Neuroscience
Neurophysiology
Action Potentials
Biology and Life Sciences
Cell Biology
Cellular Types
Animal Cells
Neurons
Interneurons
Biology and Life Sciences
Neuroscience
Cellular Neuroscience
Neurons
Interneurons
Biology and Life Sciences
Cell Biology
Cellular Types
Animal Cells
Neurons
Neuronal Dendrites
Biology and Life Sciences
Neuroscience
Cellular Neuroscience
Neurons
Neuronal Dendrites
Biology and Life Sciences
Cell Biology
Signal Transduction
Cell Signaling
Signal Inhibition
Biology and Life Sciences
Physiology
Electrophysiology
Membrane Potential
Biology and Life Sciences
Neuroscience
Cognitive Science
Cognitive Psychology
Perception
Sensory Perception
Biology and Life Sciences
Psychology
Cognitive Psychology
Perception
Sensory Perception
Social Sciences
Psychology
Cognitive Psychology
Perception
Sensory Perception
Biology and Life Sciences
Neuroscience
Sensory Perception
Biology and Life Sciences
Anatomy
Brain
Hippocampus
Medicine and Health Sciences
Anatomy
Brain
Hippocampus
Biology and Life Sciences
Physiology
Electrophysiology
Membrane Potential
Depolarization
Fast-spiking interneuron detonation drives high-fidelity inhibition in the olfactory bulb
Fast-spiking interneuron function in the olfactory bulb
https://orcid.org/0000-0002-8907-6487
Burton Shawn D. Conceptualization Data curation Formal analysis Funding acquisition Investigation Project administration Supervision Writing – original draft Writing – review & editing *
Malyshko Christina M. Data curation Formal analysis Investigation Writing – original draft Writing – review & editing
Urban Nathaniel N. Conceptualization Formal analysis Funding acquisition Supervision Writing – original draft Writing – review & editing
Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania, United States of America
Fukunaga Izumi Academic Editor
Okinawa Institute of Science and Technology Graduate University: Okinawa Kagaku Gijustu Daigakuin Daigaku, JAPAN
The authors have declared that no competing interests exist.

* E-mail: shb420@lehigh.edu
26 8 2024
8 2024
26 8 2024
22 8 e300266030 4 2024
26 7 2024
© 2024 Burton et al
2024
Burton et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Inhibitory circuits in the mammalian olfactory bulb (OB) dynamically reformat olfactory information as it propagates from peripheral receptors to downstream cortex. To gain mechanistic insight into how specific OB interneuron types support this sensory processing, we examine unitary synaptic interactions between excitatory mitral and tufted cells (MTCs), the OB projection neurons, and a conserved population of anaxonic external plexiform layer interneurons (EPL-INs) using pair and quartet whole-cell recordings in acute mouse brain slices. Physiological, morphological, neurochemical, and synaptic analyses divide EPL-INs into distinct subtypes and reveal that parvalbumin-expressing fast-spiking EPL-INs (FSIs) perisomatically innervate MTCs with release-competent dendrites and synaptically detonate to mediate fast, short-latency recurrent and lateral inhibition. Sparse MTC synchronization supralinearly increases this high-fidelity inhibition, while sensory afferent activation combined with single-cell silencing reveals that individual FSIs account for a substantial fraction of total network-driven MTC lateral inhibition. OB output is thus powerfully shaped by detonation-driven high-fidelity perisomatic inhibition.

Inhibitory circuits in the mammalian olfactory bulb shape information as it propagates from peripheral receptors to the downstream cortex. This study reveals that fast-spiking interneurons perisomatically inhibit projection neurons in the mammalian olfactory bulb via synaptic detonation.

http://dx.doi.org/10.13039/100000055 National Institute on Deafness and Other Communication Disorders R01DC016560 Urban Nathaniel N. http://dx.doi.org/10.13039/100000055 National Institute on Deafness and Other Communication Disorders R01DC021296 https://orcid.org/0000-0002-8907-6487
Burton Shawn D. This work was supported by National Institute on Deafness and Other Communication Disorders (https://www.nidcd.nih.gov/) grants R01DC016560 to N.N.U. and R01DC021296 to S.D.B. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. PLOS Publication Stagevor-update-to-uncorrected-proof
Publication Update2024-09-06
Data AvailabilityIndividual data points are available as downloadable Supporting Information files with legends.
Data Availability

Individual data points are available as downloadable Supporting Information files with legends.
==== Body
pmcIntroduction

Circuit operations underlying mammalian brain function critically depend on the diverse structural and functional features of distinct inhibitory interneuron types, such as the dynamic modulation of projection neuron gain, spike-time patterning, and synchronization by fast-spiking perisomatic-innervating basket cells in neocortex and hippocampus [1–3]. Resolving the functions of interneurons in these circuits through cell type-specific recordings of unitary synaptic interactions has dramatically advanced our understanding of brain function across learning and disease [4]. In the olfactory bulb (OB), the first processing station of the main olfactory system, inhibitory interneurons likewise exhibit pronounced diversity [5,6] and are similarly central to OB circuit operations, with perturbations of synaptic inhibition significantly disrupting projection mitral and tufted cell (MTC) spike-time patterning and olfactory-guided behavior [7–9]. Compared to the extensive progress made in linking diverse interneuron types to specific functions elsewhere in the brain, however, understanding how specific inhibitory interneurons support OB circuit operations remains more limited.

Central olfactory processing begins with transmission of sensory information from peripheral sensory neuron terminals to MTCs within odorant receptor-specific glomeruli in the OB (Fig 1A). Diverse juxtaglomerular interneurons, including large populations of inhibitory periglomerular cells [10], modulate this transmission to collectively support sniff-frequency coupling, gain control, and intraglomerular contrast enhancement of MTC odor responses [5,11,12]. Other circuit operations critical to olfaction, such as fast timescale spike-time patterning and synchronization of MTC activity, emerge deeper in the OB, within the external plexiform layer (EPL) [13], where conceptual models focus exclusively on the contribution of inhibitory granule cells (GCs) to recurrent and lateral MTC inhibition. Despite theoretical and histological evidence of widespread MTC–GC connections [14,15], however, functional unitary MTC–GC connectivity appears exceedingly sparse, with <5% of pair recordings exhibiting MTC-to-GC excitation and zero exhibiting GC-to-MTC inhibition [16–18]. Investigation of MTC inhibition in the EPL has consequently relied on non-cell type-specific measures of total recurrent inhibition evoked by prolonged MTC activation, assumed to originate from GCs, and often performed in low Mg2+ to augment GC excitation [5,19]. These measures have led to the consensus that MTC inhibition is slow and low-fidelity [20,21], features difficult to reconcile with functions such as the precise regulation of MTC spike timing.

Fundamental understanding of the circuit operations underlying olfaction will thus ultimately require greater cell type-specific knowledge of unitary synaptic interactions in the OB. Of paramount interest, the EPL contains a conserved and neurochemically diverse population of anaxonic interneurons (EPL-INs) that, while less abundant than GCs, can mediate unitary MTC inhibition [17,22]. Population-level manipulations further suggest that such inhibition powerfully influences MTC activity [17,22–24]. However, interpretation of these genetically targeted approaches remains constrained by disruption of endogenous expression in at least some mouse lines [23,25,26], nonselective perturbation of other neuron types [17,23,27,28], incomplete mapping of neurochemical identity across EPL-IN subtypes, and limited mechanistic insight into unitary interactions, leaving the overall function of EPL-INs still unclear.

To advance our fundamental understanding of circuit operations in the OB, we have therefore performed a systematic investigation of unitary synaptic interactions between MTCs and EPL-INs in acute slices using simultaneous whole-cell recordings in physiological Mg2+. Our results reveal that fast-spiking EPL-INs perisomatically innervate MTCs with noncanonical architecture and mediate a substantial fraction of total MTC inhibition through fast, synchronous synaptic release. This release, supporting high-fidelity recurrent and lateral inhibition, is driven by both unusually prevalent synaptic detonation and high sensitivity to sparse MTC synchronization. Collectively, these findings challenge multiple conceptual paradigms of OB circuit operation and provide new insight into key modes of inhibitory signaling supporting olfaction.

Results

EPL-INs comprise 2 subtypes with distinct circuit functions

To systematically investigate unitary MTC–EPL-IN interactions independent of neurochemical identity and molecular lineage, we targeted MTCs and nearby small somata of putative EPL-INs for whole-cell pair recordings in acute OB slices prepared from wild-type mice (see Materials and methods). Despite wide neurochemical heterogeneity noted across EPL-IN subsets (e.g., [22,29]), analysis of intrinsic biophysical properties across 145 MTC–EPL-IN pairs surprisingly revealed only 2 major EPL-IN subtypes: (1) fast-spiking interneurons (FSIs) with non-adapting spike trains, high instantaneous firing rates, and frequent spike clustering; and (2) regular-spiking interneurons (RSIs) with regular, adapting, and comparatively slower firing (Fig 1B–1E). Unbiased hierarchical clustering (Fig 1F) and principal component analysis (S1 Fig) of 23 intrinsic biophysical properties directly supported this classification, with FSIs and RSIs exhibiting stark differences across the majority of properties (S2 Fig and S1 Table).

10.1371/journal.pbio.3002660.g001 Fig 1 EPL-INs subdivide into fast- and regular-spiking subtypes.

(A) Schematic of major OB components supporting feedforward, recurrent, and lateral MTC inhibition. Within glomeruli (dashed circles), peripheral sensory neuron terminals excite both MTCs and PGCs, driving intraglomerular feedforward inhibition of MTCs. Within the EPL, both EPL-INs and GC apical dendrites can form dendrodendritic synapses with MTCs to support potentially distinct modes of recurrent inhibition and lateral inhibition on interglomerular scales. Glutamatergic neurons shown in black; GABAergic neurons shown in red. See also [5] for a more comprehensive version containing other circuit components not directly studied here. (B, C) Example MTC–FSI pairs filled with NB (left) and interneuron fast-spiking physiology (right) in response to step current injections (dashed lines). Magnified regions here and throughout other figures shows single optical planes of example putative MTC–FSI synaptic contact (open arrowheads), except where noted. Schematic of recording configuration (inset) shown here and throughout other figures when configuration changes. (D, E) Example MTC–RSI pairs and regular-spiking physiology. Magnified region: long spiny RSI dendrites (open arrowheads). Filled arrowheads: interneuron somata. (F) Hierarchical clustering of 104 EPL-INs by intrinsic biophysical properties into FSIs (green) and RSIs (orange). (G) Post hoc PV staining of an example FSI. Insets: magnified region centered on soma, showing NB (upper) and PV (lower). (H) NB signal from G, inverted to show morphology. Inset: spiking physiology (scale: 20 mV/0.5 nA, 0.2 s). (I, J) Same as G and H for an RSI. (K) Sholl analysis. Inset: FSIs were PV+, while RSIs were PV– (***p = 1.6 × 10–6, χ2[1] = 23, χ2 test). (L–O) Unitary synaptic interactions from the pairs in B–E. Asterisks mark significant postsynaptic responses. (P, Q) Distribution of excitatory (P) (***p = 7.3 × 10–10, χ2[1] = 37.9, χ2 test) and inhibitory (Q) (***p = 2.2 × 10–5, χ2[1] = 18.0, χ2 test) unitary connections among MTC–FSI and MTC–RSI pairs. Simplified schematic here and elsewhere indicates neuron types being plotted by color (MTC: magenta; FSI: green; RSI: orange) and shape (MTC: pentagon with triangular terminal; EPL-IN: circle with circular terminal), as well as presence vs. absence of significant postsynaptic response (solid connection and coloration vs. dotted connection and no coloration). (R) Voltage-clamp (VC) detected unitary inhibitory connections more sensitively than current-clamp (IC) (***p = 5.6 × 10–5, χ2[1] = 16.2, χ2 test). (S) Distribution of inhibitory, excitatory, reciprocal, or no unitary connectivity within the subset of MTC–FSI pairs recorded in VC and IC, respectively. Source data for panels F, K, and P–S are provided in Supporting information, S1 Data. EPL-IN, external plexiform layer interneuron; FSI, fast-spiking interneuron; GC, granule cell; MTC, mitral and tufted cell; OB, olfactory bulb; PGC, periglomerular cell; PV, parvalbumin; RSI, regular-spiking interneuron.

As a caveat, such hierarchical clustering approaches can be sensitive to the specific algorithms and parameter spaces used. Reinforcing the division of EPL-INs into 2 major subtypes, however, FSIs and RSIs also exhibited clear morphological differences: FSIs extended complex and beaded dendritic arbors while RSIs extended less branched and long, spiny dendrites that occasionally entered the glomerular layer (Figs 1B–1E, 1G–1K, S3, S4, and S5 and S2 Table). Neither FSIs nor RSIs extended visible axons. Morphological properties of FSIs thus broadly matched those previously noted for several neurochemical EPL-IN subsets [17,22,29,30], while RSIs did not obviously correspond to any previously characterized EPL-INs. Consistent with these parallels, post hoc immunostaining confirmed parvalbumin (PV) expression in 100% of FSIs and 0% of RSIs (Figs 1G, 1I, 1K, and S3). Likewise, neither FSIs nor RSIs expressed tyrosine hydroxylase (TH), a marker of sparse, tonically active short-axon cells in the EPL (S4 Fig) [31–33]. How other neurochemicals map to FSIs versus RSIs remains unclear, however, with only 27% of FSIs exhibiting weakly-detectable vasoactive intestinal peptide (VIP) expression compared to 0% of RSIs (S5 Fig).

Physiological and morphological EPL-IN differences also mapped directly onto unitary synaptic connectivity evoked by single spikes or brief voltage steps (see Materials and methods): MTC activation triggered robust postsynaptic depolarization of FSIs but not RSIs, while FSI but not RSI spiking evoked inhibitory postsynaptic currents (IPSCs) in voltage-clamped MTCs and inhibitory postsynaptic potentials (IPSPs) in current-clamped MTCs (Fig 1L–1O). Across 145 pairs examined, MTCs were exclusively inhibited by FSIs (Fig 1Q) while 69% of FSIs exhibited postsynaptic excitation (Fig 1P) and only 1 of 27 RSIs exhibited significant excitation that was orders of magnitude weaker (S6 Fig). Lack of MTC–RSI connectivity could not be attributed to any distance-dependent slicing artifact, as MTC–RSI pairs exhibited modestly shorter intersomatic distances than MTC–FSI pairs (S7 Fig). Glutamate receptor antagonists NBQX and AP5 reversibly blocked unitary MTC-to-FSI excitation, while additional application of GABAA receptor (GABAAR) antagonist gabazine reversibly blocked unitary FSI-to-MTC inhibition (S8 Fig), confirming that unitary MTC–FSI interactions are mediated by direct glutamatergic and GABAergic transmission. In total, physiological, morphological, neurochemical, and synaptic differences thus subdivide EPL-INs into 2 major subtypes with profoundly distinct circuit functions.

The majority of MTC–FSI pairs with any connection proved reciprocally connected (Fig 1S). Unitary connectivity could further be detected between FSIs and MTCs with apical dendrites truncated at the glomerular layer (S9 Fig), confirming that MTC–FSI connectivity is localized to infraglomerular layers. No electrical coupling was detected between MTCs and FSIs (see Materials and methods). Across the EPL, unitary connection probability and strength did not differ between FSIs and mitral cells (MCs) versus tufted cells (TCs) (S10 Fig). Likewise, MCs and TCs interacted with FSIs spanning comparable positions throughout the EPL (S10 Fig). FSIs are thus capable of influencing both streams of olfactory processing separately supported by TCs and MCs [9,34–36], and results are thus reported as they apply to the collective MTC population.

Notably, the unitary synaptic output of both MTCs and FSIs exhibited short latencies, low jitter, and high release probability (S11 Fig), signatures of high-fidelity, synchronous synaptic release that markedly differ from the low-fidelity, asynchronous release predicted from GCs [37]. Further of note, unitary FSI-to-MTC inhibition exhibited consistently shorter latencies than unitary MTC-to-FSI excitation (S11 Fig), potentially indicating distinct presynaptic Ca2+ channel expression [38]. Unitary MTC-to-FSI excitation also exhibited higher release probabilities on average than unitary FSI-to-MTC inhibition (S11 Fig), though prodigious spontaneous EPSP (sEPSP) rates in FSIs (S1 Table) likely masked some MTC release failures. Unitary EPSP (uEPSP) and unitary IPSC (uIPSC) amplitudes also positively correlated among connected MTC–FSI pairs (S11 Fig), suggesting coordinated scaling of synaptic strength.

In total, our results thus identify FSIs as a major EPL-IN subtype with strong potential to directly influence OB output through prevalent, reciprocal, and fast unitary synaptic interactions with MTCs. We therefore focused on MTC–FSI interactions throughout the remainder of the study.

FSIs perisomatically innervate MTCs with noncanonical architecture

Post hoc inspection of reciprocally connected MTC–FSI pairs filled with Neurobiotin (NB) revealed FSI dendritic varicosities apposed to MTC somata, proximal apical dendrites, and axon hillocks (Fig 1B and 1C)—subcellular domains matching the description of perisomatic innervation of projection neurons elsewhere in the brain [39]. Post hoc multicolor confocal microscopy of an additional subset of reciprocally connected MTC–FSI pairs filled with Lucifer Yellow (LY) and NB, respectively, likewise resolved putative contact between FSI dendrites and MTC perisomatic domains (Fig 2A and 2B). These patterns directly complement previous ultrastructural investigations of PV+ EPL-INs [40–42], suggesting that FSIs perisomatically innervate MTCs to support similar circuit operations as perisomatic-innervating interneurons elsewhere in the brain, such as PV+ basket cells in neocortex and hippocampus. Two lines of evidence further suggest that FSIs preferentially innervate perisomatic over distal MTC domains.

10.1371/journal.pbio.3002660.g002 Fig 2 FSIs mediate fast MTC inhibition through perisomatic innervation lacking basket-like structures.

(A) Example reciprocally coupled MTC–FSI pair filled with LY and NB. (B) Magnified region from A, showing single optical plane (B). (C) MTC-to-FSI uEPSPs (n = 14 event waveforms) exhibited comparable rise times as sEPSPs (n = 3,965 event waveforms) in the pair in A and B (left; p = 0.8, r.s. = 7889511, Wilcoxon rank-sum test with Bonferroni-corrected significance level of 0.05/78), while FSI-to-MTC uIPSCs (n = 23 event waveforms) exhibited faster rise times than sIPSCs (n = 613 event waveforms) (right; ***p = 5.3 × 10–11, r.s. = 200918.5, Wilcoxon rank-sum test with Bonferroni-corrected significance level of 0.05/52). (D) Comparison of unitary and spontaneous synaptic event kinetics as in C (only median rise times shown for visual clarity) for 78 pairs exhibiting unitary MTC-to-FSI excitation (left) and 52 pairs exhibiting unitary FSI-to-MTC inhibition (right). (E, F) Single optical confocal planes of layer 5 (L5) primary visual neocortex (E) and hippocampal CA1 (F) revealing colocalization of Syb1 and PV in basket-like outlines of putative pyramidal cells. (G, H) Single optical confocal plane (G) and maximum-intensity projection (approximately 50 μm depth) (H) of Syb1 and PV in the OB, revealing a consistent absence of basket-like outlines of MTCs. (I–L) Same as E–H for Syt2 and PV. (M–T) Same as E–H for PV and Cre-dependent expression of either cytosolic tdTomato (M–P) or membrane-localized mCitrine (Q–T) in PV-IRES-Cre mice. Source data for panels C and D are provided in Supporting information, S2 Data. FSI, fast-spiking interneuron; LY, Lucifer Yellow; MTC, mitral and tufted cell; OB, olfactory bulb; PV, parvalbumin; sIPSC, spontaneous IPSC.

First, connected MTC–FSI pairs exhibited significantly shorter intersomatic distances than unconnected pairs (S7 Fig), with connected distances within the range for compact FSI dendrites (radially extending approximately 100 μm; Fig 1K) to contact MTC somata. This distance-dependence would be unlikely to emerge from our data if FSIs innervated distal and proximal MTC domains equally. Further, neither uEPSP nor uIPSC amplitudes correlated with intersomatic distance (S7 Fig), suggesting that the greater connectivity observed at shorter distances did not reflect a technical inability to resolve potentially weaker distal connections. While additional pair recordings will ultimately be required to assess the distance-dependence of MTC–FSI connectivity across intersomatic distances encompassing the full ~1 mm extent of MTC lateral dendrites (see Discussion), the high rates of connectivity between nearby MTCs and FSIs, combined with the compact dendritic arbors of FSIs, provides a morphological foundation for preferential FSI innervation of perisomatic MTC domains.

Second, unitary FSI-to-MTC inhibitory events exhibited significantly faster rise times than spontaneous events in 52% of connected pairs and significantly slower rise times in 0% (Fig 2C and 2D), consistent with FSIs innervating MTCs at more electrotonically proximal domains than inhibition from other interneurons [43], such as GCs and juxtaglomerular interneurons. In comparison, MTC-to-FSI uEPSP rise times were significantly faster than sEPSPs in only 1% of connected pairs and significantly slower in only 6% (Fig 2C and 2D), suggesting that nearby MTCs comparably innervate proximal and distal domains of FSIs. While other factors may contribute to faster MTC inhibition by FSIs than other interneurons (see Discussion), the combined observation of faster events, MTC–FSI connectivity at short intersomatic distances, and apposition of FSI dendritic varicosities to postsynaptic MTC somata, proximal apical dendrites, and axon hillocks complements prior ultrastructural findings to suggest that FSIs preferentially (though not exclusively) mediate perisomatic MTC inhibition.

To gain further structural insight into perisomatic MTC inhibition, we next examined whether FSIs, which share several neurochemical and physiological properties with PV+ fast-spiking basket cells throughout several regions of the brain, including neocortex and hippocampus, also innervate MTCs with canonical patterns of profuse basket-like somatic wrapping similar to basket cell innervation of pyramidal cells. Toward this end, we first immunostained for Synaptobrevin-1 (Syb1; also known as VAMP1) and Synaptotagmin-2 (Syt2), 2 proteins co-expressed with PV in neocortical and hippocampal basket cell axonal boutons [44,45]. Consistent with prior results, Syb1 and Syt2 colocalized with PV in canonical basket-like outlines of PV– putative pyramidal cells in single optical confocal planes of both neocortex and hippocampus (Fig 2E, 2F, 2I, and 2J). In the same sections, we detected clear expression of both Syt2 and Syb1 in the OB, in contrast to reports suggesting negligible OB expression [46,47]. However, while Syb1 colocalized with PV throughout the EPL, particularly within large PV+ somata likely corresponding to a sparse subset of short-axon cells [48], no basket-like innervation of MTCs by PV/Syb1-coexpressing processes was evident in either single optical confocal planes or maximum-intensity projections (Fig 2G and 2H). Syt2, in turn, was also found throughout the EPL, as well as in prominent clusters throughout the mitral cell and internal plexiform layers (Fig 2K and 2L), potentially suggesting perisomatic MTC innervation via Chandelier-like structures [49]. However, these Syt2 structures not only failed to clearly associated with MTC axon initial segments (S12 Fig) but also further exhibited even more limited colocalization with PV than did Syb1 (Fig 2K and 2L).

While immunostaining for PV and presynaptic basket cell proteins Syt2 and Syb1 thus failed to reveal canonical basket-like innervation patterns, FSIs may innervate MTC somata with alternative presynaptic proteins and fine dendritic processes that stain poorly for PV. Indeed, comparison of intracellular NB and post hoc PV immunostaining revealed incomplete PV staining of fine FSI dendrites (Figs 1G and S3). We therefore additionally explored whether genetic labeling of PV+ interneurons using crosses of PV-IRES-Cre mice and Cre-dependent cytosolic tdTomato or membrane-localized mCitrine reporter mice may reveal basket-like innervation of MTCs. In neocortex and hippocampus, cytosolic tdTomato brightly labeled PV+ somata and, in hippocampus, also outlined PV– putative pyramidal cell somata (Fig 2M and 2N), while membrane-localized mCitrine could not clearly be visualized in PV+ somata but extensively colocalized with PV in basket-like innervation structures (Fig 2Q and 2R). Surprisingly, PV-IRES-Cre mice crossed to either reporter only sparsely labeled PV+ EPL-INs in the OB, in addition to PV+ periglomerular cells and deep short-axon cells (Fig 2O, 2P, 2S, and 2T)—a level of labeling inefficiency similar to some other brain regions [50]. Nevertheless, both reporters revealed fine dendritic processes of the sparsely labeled EPL-INs, but still failed to uncover signs of even partial basket-like MTC innervation.

Collectively, our results thus reveal kinetically fast perisomatic inhibition of MTCs by FSIs as a prominent feature of the mouse OB, uncovering new potential for functional parallels between OB and cortical circuits. This common functional feature surprisingly emerged through noncanonical architecture void of basket-like somatic innervation, however, highlighting key structural differences between basket cells and anaxonic FSIs that may augment established computational roles of perisomatic inhibition with the potential for independent subcellular processing within release-competent FSI dendrites (see Discussion).

FSI detonation mediates high-fidelity inhibition

Recurrent MTC inhibition powerfully regulates odorant discrimination and olfactory perception [51] and is widely accepted to be slow and asynchronous. This low-fidelity signaling has been traced to both intrinsic and synaptic properties of GCs [5], including asynchronous GC output onto distal MTC lateral dendrites [37] as well as weak MTC excitation of GCs, with GC spiking requiring temporal summation across prolonged MTC spike trains or the synchronous spiking of 9 to 30 MTCs [18,52,53]. In contrast to such weak GC excitation, unitary MTC-to-FSI excitation triggered FSI spiking with short latency (2.20 ± 0.08 ms) and low jitter (0.36 ± 0.06 ms, n = 12) in 23% of connected pairs (Fig 3A and 3D; spike probability: 0.43 ± 0.08 [n = 19]). Identical activity was further observed spontaneously in cell-attached recordings preceding whole-cell access, with MTC spiking typical of glomerulus-wide long-lasting depolarizations [54] reliably followed by short-latency FSI spiking (Fig 3B and 3C), thus excluding the possibility that intracellular dialysis may have artificially elevated FSI excitability.

10.1371/journal.pbio.3002660.g003 Fig 3 FSI detonation mediates high-fidelity recurrent MTC inhibition.

(A–C) Cell-attached recording (B) preceding whole-cell access of an example MTC–FSI pair (A) showing FSI spikes reliably following spontaneous MTC spikes by approximately 2 ms (B, C insets). (D) Unitary MTC release triggered FSI detonation (light green) in the pair in A. Negative current injection (dark green) on interleaved trials blocked detonation, revealing an underlying uEPSP. Inset: proportion of MTC–FSI pair recordings showing FSI detonation, uEPSPs, or no excitation. (E) Subtraction of mean MTC currents across FSI detonation vs. uEPSP trials from D isolates IPSCs time-locked to FSI detonation. Voltage-step currents blanked for visual clarity. (F) Unitary FSI-to-MTC inhibition from the pair in A. Asterisks in D and F mark significant postsynaptic responses. (G) Comparison of mean MTC-to-FSI uEPSP amplitudes with ΔVthresh. across 72 FSIs with detonation probability <1. (H) Estimated number of synchronously-spiking MTCs needed to activate each FSI (ratio of values in G for FSIs with detonation probability <0.5; value set to 1 for FSIs with detonation probability ≥0.5). Source data for panel D inset and panels G and H are provided in Supporting information, S3 Data. FSI, fast-spiking interneuron; IPSC, inhibitory postsynaptic current; MTC, mitral and tufted cell.

Such rapid unitary postsynaptic activation, or detonation [55], combined with the synchronous release properties of FSIs suggests that unitary MTC release under physiological conditions may trigger high-fidelity recurrent inhibition. To investigate this, we hyperpolarized FSIs in reciprocally connected MTC–FSI pairs through negative current injection on interleaved trials, blocking detonation and revealing massive underlying uEPSPs (Fig 3D), and then examined the difference in mean MTC currents across detonation versus uEPSP trials. Of note, each MTC can interact via reciprocal dendrodendritic synapses with up to 104 GCs [56,57] as well as a large population of periglomerular cells [10], suggesting that such an attempt to resolve the recurrent inhibition mediated by any one interneuron through somatic recording may prove exceedingly difficult unless such inhibition is particularly strong and reliable.

Strikingly, subtracting the mean MTC current recorded during FSI uEPSP trials from the mean MTC current recorded during detonation trials indeed isolated clear recurrent IPSCs strongly timelocked to FSI detonation latencies (Fig 3E). Subtraction-isolated MTC inhibition was further comparable to the unitary FSI-to-MTC inhibition measured separately using precisely timed FSI current injection (Fig 3F), confirming that FSI detonation alone could fully account for the difference in mean MTC currents across detonation versus uEPSP trials. Equivalent results were further observed when comparing spontaneously alternating detonation versus uEPSP trials for FSIs with detonation probability <1 (S13 Fig). FSI detonation is thus widespread throughout the OB and mediates strong, high-fidelity recurrent MTC inhibition.

While FSI detonation was surprisingly prevalent, many FSIs still responded to unitary MTC release with subthreshold excitation (Fig 3D). To comprehensively understand how the total population of FSIs contributes to MTC inhibition, we therefore divided ΔVthresh. (the depolarization required to reach spike threshold; see Materials and methods) by the mean uEPSP amplitude for each FSI (Fig 3G) to estimate how many synchronously spiking MTCs would be needed to activate the FSI (assuming identical unitary strength and passive EPSP summation) (Fig 3H). Mean uEPSP amplitudes exceeded ΔVthresh. in some FSIs (Fig 3G), consistent with a subset of FSIs with detonation probability <1, as well as potential differences in FSI excitation by synaptic input versus somatic current injection. Strikingly, the majority of FSIs were estimated to activate following synchronous unitary release from only 1 to 4 MTCs (Fig 3H), suggesting that FSIs prominently contribute to OB inhibition following even sparse synchronization of MTCs.

Similar to recurrent inhibition, lateral inhibition between heterotypic MTCs (i.e., connected to distinct glomeruli) is believed to support olfactory perception through key circuit operations, such as the binding of distinct percepts through the synchronization of MTC spiking [58]. While theoretical studies specifically point to fast perisomatic lateral inhibition as a powerful synchronizing force in the OB [59,60] as in other circuits [1,2], the few studies directly recording heterotypic MTC pairs have consistently reported lateral inhibition to be slow and asynchronous [20,21,61,62]. As with measures of recurrent inhibition, however, these studies evoked lateral inhibition with prolonged MTC activation, often in low Mg2+.

Strong, high-fidelilty FSI inhibition of MTCs combined with widespread FSI detonation in physiological Mg2+ suggests that single MTC spikes should instead evoke detectable fast lateral inhibition in nearby MTCs. Consistent with this hypothesis, re-examination of heterotypic MTC pair recordings from a recent data set [63] indeed revealed that single MTC spikes reliably evoked single, short-latency IPSPs (<10 ms) in a large proportion of nearby MTCs, including both uni- and bidirectional connections (Fig 4A–4E).

10.1371/journal.pbio.3002660.g004 Fig 4 FSIs mediate fast lateral inhibition between nearby heterotypic MTCs.

(A-D) Lateral synaptic interactions evoked by single spikes within example heterotypic MTC pairs (A, C) from [63], revealing bidirectional (B) and unidirectional (D) fast lateral inhibition (asterisks). Insets: mean postsynaptic voltages. Single widefield images shown. (E) Distribution of bidirectional, unidirectional, or no fast lateral inhibitory connectivity among heterotypic MTCs recorded in current-clamp (IC). (F, G) Example MTC–FSI pair (F) showing detonation occurrence and MTC-to-FSI uEPSP amplitudes before and after bath application of PhTx-74 (10 μM) (G). Inset: PhTx-74 significantly reduced uEPSP amplitudes (***p = 3.4 × 10–5, t6 = 11.0, t test) across 7 MTC–FSI pairs. Filled symbol corresponds to example pair. (H) Postsynaptic FSI voltages from the pair in F. Light green and black traces correspond to bracketed trials (uEPSP and detonation, respectively) before PhTx-74 application in G; dark green traces correspond to bracketed trials after PhTx-74 application. Asterisk marks significant unitary postsynaptic response. (I, J) Example MTC pair (I) (recorded in voltage-clamp to enhance the detection of inhibition), showing short-latency (<10 ms) lateral IPSC amplitudes evoked by 2-ms presynaptic depolarization before and after PhTx-74 application. Inset in I: sub-projection of dashed box in I across different depths, showing apical dendrites innervating distinct glomeruli. Inset in J: PhTx-74 significantly reduced fast lateral IPSC amplitudes (***p = 4.6 × 10–4, t3 = 16.8, t test) across 4 connections. Filled symbol corresponds to example pair. (K) Postsynaptic MTC currents from the pair in I. Dark and light traces correspond to the bracketed trials before and after PhTx-74 application in J, respectively. Asterisk marks significant fast lateral inhibition measured during pre-PhTx-74 trials. Source data for panels E, G, and J are provided in Supporting information, S4 Data. FSI, fast-spiking interneuron; IPSC, inhibitory postsynaptic current; MTC, mitral and tufted cell.

Such fast lateral inhibition is inconsistent with the weak excitation and projected asynchronous and NMDAR-dependent output of GCs [51,64]. GCs strongly outnumber EPL-INs, however, and are widely accepted to be capable of subthreshold GABA release following even weak excitation [5], and thus may concievably still drive fast lateral inhibition. Therefore, to provide a complementary test of whether FSIs versus GCs mediate fast lateral inhibition, we capitalized on the established presence versus absence of philanthotoxin-sensitive GluA2-lacking AMPARs in PV+ EPL-INs versus mature GCs, respectively [16,17,65–70]. Philanthotoxin-7,4 (PhTx-74) indeed markedly reduced MTC-to-FSI uEPSP amplitudes, abolished FSI detonation (Fig 4F–4H), and disrupted fast lateral inhibition in MTC pairs (Fig 4I–4K). Our pharmacological results thus provide complementary evidence that FSI detonation plays a central role in mediating fast lateral inhibition. Further supporting this finding, we detected fast lateral inhibition in both TC pairs and MC pairs, arguing against possible contributions to fast lateral inhibition by superficial and deep short-axon cells, which selectively target TCs and not MCs [71–74].

The degree to which FSI-mediated fast lateral inhibition shapes OB activity, and in particular synchronization of MTC spiking, will critically depend on its prevalence relative to established forms of slow, presumably GC-mediated lateral inhibition following prolonged MTC activation. While our results so far suggest that fast lateral inhibition may be as prevalent as the approximately 10% of nearby MC pairs exhibiting slow lateral inhhibition [61], these data were collected under distinct conditions. We therefore next tested for the occurrence of fast versus slow lateral inhibition within heterotypic MTC pairs using short (2 ms) versus long (100 ms) presynaptic voltage steps to 0 mV to trigger single spike- versus spike train-equivalent MTC output, respectively. As slow lateral inhibition is believed to be mediated by at least partially independent TC- and MC-innervating GC subpopulations [35] and has not been reported within mixed TC–MC pairs, we specifically targeted TC pairs and MC pairs, and not TC–MC pairs, for comparison of fast versus slow lateral inhibition prevalence. We defined fast lateral inhibition as a significant increase in IPSC probability within 10 ms following presynaptic MTC activation, while slow lateral inhibition was defined as a significant increase in IPSC probability in at least three 10 ms-bins within 250 ms following presynaptic MTC activation, reflecting an asynchronous barrage of inhibition. This approach resolved clear instances of both fast and slow lateral inhibition (Fig 5A–5C and 5D–5F, respectively), and across the population of pairs tested, fast lateral inhibition indeed proved equally as prevalent as slow lateral inhibition (Fig 5G).

10.1371/journal.pbio.3002660.g005 Fig 5 Fast lateral inhibition is a prevalent signaling mode in the OB.

(A–C) Example MTC pair (A) exhibiting unidirectional fast lateral inhibition in response to 2-ms presynaptic depolarization (B) and bidirectional fast lateral inhibition in response to 100-ms presynaptic depolarization (C). Insets: enlargement of bracketed times in C. (D–F) Same as A–C for an example MTC pair (D) exhibiting unidirectional slow lateral inhibition (F) but no fast lateral inhibition (E). Asterisks mark significant fast (B, C) and slow (F) lateral inhibition. (G) MTC pairs exhibiting fast lateral inhibition were equally as prevalent as pairs exhibiting slow lateral inhibition (p = 0.84, χ2[1] = 4.1 × 10–2, χ2 test). (H) Distribution of fast, slow, both, or no lateral inhibition within heterotypic MTC pairs recorded in voltage-clamp (VC). Pairs exhibiting fast lateral inhibition only at the start of 100-ms presynaptic depolarization (fast#) shown in hatched bars in G and H. Source data for panels G and H are provided in Supporting information, S5 Data. MTC, mitral and tufted cell; OB, olfactory bulb.

Within individual MTC pairs, fast and slow lateral inhibition appeared to occur independently (though not mutually exclusively). Specifically, MTC pairs exhibiting slow lateral inhibition in response to long voltage steps typically failed to exhibit fast lateral inhibition in response to short voltage steps (Fig 5D–5F), while MTC pairs exhibiting fast lateral inhibition in response to short voltage steps typically continued to exhibit only fast lateral inhibition in response to long voltage steps (Fig 5A–5C). Our results are thus consistent with the 2 lateral inhibitory signaling modes emerging through distinct circuit mechanisms: FSI-mediated fast lateral inhibition, and presumably GC-mediated slow lateral inhibition. One MTC pair exhibited both fast and slow lateral inhibition (Fig 5H), a prevalence in line with independent occurrence of the 2 signaling modes (i.e., equal to the product of fast and slow lateral inhibitory prevalences) and suggesting that FSIs and GCs do not innervate distinct MTC subpopulations. Also of note, in a subset of connections (Fig 5G and 5H), fast lateral inhibition was only detected at the beginning of long presynaptic voltage steps (cf. Fig 5B and 5C, right); additional experiments are needed to determine whether such signaling reflects FSI activation by rapidly summating excitatory input. In total, our results thus reveal FSI-mediated fast lateral inhibition to be highly prevalent throughout the OB in both absolute terms and relative to presumably GC-mediated slow lateral inhibition.

Post hoc morphological inspection of the above MTC pairs revealed that both fast and slow lateral inhibition occurred independent of whether MTCs connected to directly neighboring glomeruli or glomeruli separated by 1 or ≥2 other glomeruli (S14 Fig). Moreover, intersomatic distance did not significantly differ between MTC pairs exhibiting fast versus slow lateral inhibition. Examination of each mode of inhibition in isolation, however, revealed that fast lateral inhibition was preferentially detected between nearby MTCs, while no difference in intersomatic distance was found between pairs with versus without slow lateral inhibition (S14 Fig). This difference arose largely due to a high prevalence of fast, but not slow, lateral inhibition specifically between directly neighboring MTCs (c.f. Figs 4A, 4C, 4I, and 5A). Indeed, the median intersomatic distance between MTCs exhibiting fast lateral inhibition was zero (S14 Fig). Inspection of an additional cohort of MTC pairs tested for fast lateral inhibition (see below) directly reinforced this pattern, with the probability of detecting fast, but not slow, lateral inhibition exhibiting a pronounced peak (45% of pairs) at zero intersomatic distance (S14 Fig). These results thus further reinforce the conclusion that distinct EPL circuits support fast versus slow lateral inhibition between heterotypic MTCs, with the high probability for fast lateral inhibition to link directly neighboring MTCs (in addition to other nearby MTCs at lower probability) in good agreement with the spatial dimensions of perisomatic MTC innervation by compact FSI dendritic arbors.

Odorants evoke prominent gamma-frequency oscillations throughout the OB [13], reflecting synchronization of MTC spiking [58,63,75]. The fast lateral inhibition identified in our current results represents a previously unrecognized signaling mode that may support such MTC synchronization. In turn, our estimates of MTC synchronization required to activate FSIs (Fig 3H) further suggests that synchronization of only a few MTCs should activate the majority of postsynaptic FSIs and, consequently, trigger fast lateral inhibition onto an increasing fraction of nearby MTCs to potentially spread network synchronization.

To directly test the sensitivity of FSI signaling to such MTC synchronization, we therefore next recorded MTC quartets and examined the dependence of fast lateral inhibition on the number of synchronously activated MTCs. To exclude the potential contribution of intraglomerular forms of lateral inhibition to the recorded currents [76], analysis was confined to postsynaptic responses in MTCs heterotypic to all other MTCs of the quartet (as in above MTC pair recordings). We likewise focused recordings on quartets of MCs or TCs to further parallel our MTC pair recordings above.

In some MTCs, fast lateral inhibition occurred upon activation of one presynaptic MTC regardless of whether other MTCs of the quartet were coactivated (e.g., Fig 6B, upper), a pattern consistent with FSI detonation and no additional FSI activation with MTC synchronization. In such instances, quantification of the mean lateral IPSC amplitude as a function of the number of synchronously activated MTCs closely followed linear predictions (i.e., multiplying the mean singular response by the number of MTCs coactivated) (Fig 6C, left). Other MTCs, however, exhibited supralinear increases in fast lateral inhibition, reflecting either greater-than-predicted increases in IPSC amplitude (e.g., Fig 6B, lower and 6C right) or the occurrence of fast lateral inhibition only following coactivation of 2 or 3 MTCs (e.g., Fig 6E). Across all possible combinations, both the strength and prevalence of fast lateral inhibition increased supralinearly (Fig 6F and 6G), consistent with activation of most FSIs by synchronization of just a few MTCs. Our results thus point toward a positive feedback loop between MTC synchronization and fast lateral inhibition as a potential circuit operation supported by FSIs.

10.1371/journal.pbio.3002660.g006 Fig 6 Sparse MTC synchrony supralinearly enhances fast lateral inhibition strength and prevalence.

(A, B) Example MTC quartet (A; inset: sub-projection of dashed box across different depths, showing MTCs [B] and [D] innervating distinct glomeruli), showing postsynaptic currents (B) in MTCs [D] (upper) and [B] (lower) following singular or synchronous activation of all other quartet MTCs. Asterisks mark significant fast lateral inhibition. (C) Lateral IPSC amplitudes in MTCs [D] (left) and [B] (right) from the quartet in A as a function of the number of synchronously activated presynaptic MTCs. Lines: mean across all combinations of each presynaptic population size. (D, E) Same as A and B for a second MTC quartet. Presynaptic voltage-step artifacts are visible in postsynaptic currents of some MTCs but terminate prior to IPSC onset. (F) Lateral IPSC amplitudes grew supralinearly with synchronous activation of additional presynaptic MTCs (two-way ANOVA, experiment/linear prediction × # MTCs activated; ***p = 9.3 × 10–5, F1,42 = 18.7, experiment vs. linear prediction; ***p = 1.0 × 10–8, F2,42 = 29.4, # MTCs activated; ***p = 2.4 × 10–4, F2,42 = 10.2, interaction). Values normalized within each MTC by response to synchronous activation of 3 presynaptic MTCs and averaged across 8 MTCs exhibiting fast lateral inhibition (of 19 MTCs with no homotypic partners possible from 7 quartets; other MTCs were homotypic to ≥1 MTC of the quartet and excluded from analysis). (G) Fast lateral inhibition was detected across a higher proportion of combinations as more presynaptic MTCs were synchronously activated (*p = 1.0 × 10–2, χ2[2] = 9.2, χ2 test). Source data for panels C, F, and G are provided in Supporting information, S6 Data. IPSC, inhibitory postsynaptic current; MTC, mitral and tufted cell.

Understanding the net contribution of FSIs to sensory processing in the OB will further require assessing the strength of FSI-to-MTC inhibition relative to total MTC inhibition following sensory activation of the OB circuit, particularly given that each MTC receives inhibitory input from an estimated 104 GCs [56,57] in addition to other interneuron types [5]. In our final experiment, we therefore combined MTC–FSI pair recordings with activation of glomerular sensory input to assess the contribution of single FSIs to total network-driven MTC lateral inhibition. Specifically, in acute slices prepared from olfactory marker protein (OMP)-channelrhodopsin-2 (ChR2) mice, which express ChR2 in all mature olfactory sensory neurons, including their terminals within OB glomeruli [77], we recorded MTC inhibition evoked by brief photostimulation of a single nearby lateral glomerulus while, on interleaved trials, hyperpolarizing a synaptically coupled FSI to block its feedforward excitation-evoked spiking (Fig 7A and 7B). Subtraction of mean MTC currents across FSI spiking versus EPSP trials consistently revealed robust inhibitory currents time-locked to FSI spiking (Fig 7C), regardless of whether the FSI exhibited phasic or sustained activity. Across 7 MTC–FSI pairs with suprathreshold FSI responses, activation of a single FSI remarkably accounted for 47.7 ± 12.5% of the total inhibitory charge within 250 ms of photostimulation—a typical sniff duration [78] (Fig 7D). Thus, at least under conditions physiologically mimicking single glomerulus activation of the OB with low-concentration odorants [79], high-fidelity FSI signaling accounts for a substantial fraction of total network-driven MTC lateral inhibition, suggesting that FSIs likely also play a key role in sculpting MTC tuning [80,81].

10.1371/journal.pbio.3002660.g007 Fig 7 Single FSIs drive a large fraction of total network-driven lateral inhibition.

(A, B) Example MTC–FSI pair (A) with suprathreshold FSI response (light green) to optogenetic photostimulation of a single lateral glomerulus (B). Negative current injection (dark green) on interleaved trials blocked suprathreshold FSI responses. (C) Subtraction of mean MTC currents across FSI suprathreshold vs. EPSP trials from B isolated lateral inhibitory currents timelocked to FSI spiking (LIFSI; dark magenta). Mean MTC currents across FSI suprathreshold trials, reflecting total lateral inhibition under baseline conditions (LItotal; light magenta) shown for comparison. (D) Subtraction-isolated lateral inhibitory currents as in C, normalized within each MTC by peak LItotal current, and averaged across MTCs of 7 MTC–FSI pairs, including 2 MTCs with distal apical dendrites truncated at the glomerular layer (S9 Fig) ensuring absence of photostimulation-evoked excitatory currents. FSI, fast-spiking interneuron; MTC, mitral and tufted cell.

Discussion

To advance a foundational understanding of circuit operations in the OB supporting olfactory perception, we have systematically examined unitary synaptic interactions between MTCs and EPL-INs, a highly conserved population of anaxonic OB interneurons. Challenging the consensus that MTC inhibition is slow, low-fidelity, and primarily shaped by distributed changes in activity across large populations of GCs and juxtaglomerular interneurons innervating distal MTC domains, we found that PV+ fast-spiking EPL-INs perisomatically inhibit MTCs with release-competent dendrites and, through synaptic detonation and supralinear recruitment by sparse MTC synchronization, singularly mediate a substantial fraction of total MTC inhibition via strong, high-fidelity recurrent and lateral inhibition (Fig 8). These core results thus stand to reconfigure our fundamental understanding of how the OB transforms sensory input to encode olfactory information.

10.1371/journal.pbio.3002660.g008 Fig 8 FSI detonation drives high-fidelity perisomatic recurrent and lateral MTC inhibition.

Schematic of cellular voltages and synaptic events (dashed circles) underlying prominent contributions of FSIs to OB sensory processing. (A) MTC spiking following odor-evoked sensory input triggers strong unitary synaptic excitation and detonation of reciprocally connected FSIs, leading to fast perisomatic recurrent inhibition. (B) Propagation of FSI detonation drives fast perisomatic lateral inhibition of nearby MTCs via dendrosomatic synapses. (C) In contrast, lateral dendritic propagation of odor-evoked MTC spiking drives weak subthreshold excitation of GCs at reciprocal dendrodendritic synapses and no detectable unitary recurrent inhibition. (D) Weak excitation fails to propagate throughout the GC dendritic arbor, and lateral inhibition of other reciprocally connected MTCs remains gated. How distal MTCs synaptically interact with FSIs, as well as how RSIs contribute to OB circuit operation, remain open questions. FSI, fast-spiking interneuron; GC, granule cell; MTC, mitral and tufted cell; OB, olfactory bulb; RSI, regular-spiking interneuron.

EPL-IN diversity

Classic Golgi staining has established that EPL-INs are highly conserved across mammals, including at least carnivores, omnivores, and insectivores [82–85]. Lacking functional comparison, however, these earlier studies also highlighted modest morphological differences among EPL-IN subsets of so-called “Van Gehuchten,” “satellite,” and “horizontal” cells. Immunohistochemistry has reinforced this apparent diversity, identifying expression of a wide variety of neurochemicals, with coexpression systematically mapped for only certain subsets (e.g., [22,29]). Collectively, these results have yielded a fairly nuanced view of EPL-INs as boutique neurons omitted from nearly all conceptual OB models.

Complementing these earlier histological approaches, we performed the first extensive physiological, morphological, neurochemical, and synaptic investigation of EPL-INs, paralleling foundational investigation of neocortical and hippocampal interneuronal diversity [86,87]. We surprisingly observed only 2 major subtypes, FSIs and RSIs, suggesting that EPL-INs are more unified in function than previously recognized. As a caveat, more subtle heterogeneity is certain to exist. Further investigation comprehensively mapping neurochemicals across all EPL-INs together with interneuron pair recordings and more extensive post hoc staining is thus poised to uncover further contributions of EPL-INs to circuit operations in the OB, in addition to potentially overcoming limitations in genetically targeting EPL-INs (e.g., Fig 2) complicating interpretation of population-level EPL-IN manipulations.

Such investigation is likely also to establish whether the greater prevalence of FSIs than RSIs in our recordings represents a true difference in cell densities or a manifestation of recording biases. In particular, to avoid superficial granule cells or deep periglomerular cells, our recordings did not sample uniformly across the full EPL (S2 Table). Moreover, we routinely avoided targeting the largest or smallest somata for recording, as the former were likely to include at least some of the sparse EPL short-axon cells [32,33,48,88,89] while the latter proved difficult to differentiate from resealed blebs of severed MTC dendrites. Finally, for this data set we abandoned recordings from a small subset of interneurons with unstable resting membrane potential. This precaution enabled us to avoid EPL-INs damaged during slice preparation as well as the sparse subset of tonically active TH+ short-axon cells (S4 Fig) [31–33], but may have also excluded some tonically active EPL-INs.

Perisomatic innervation and implications of noncanonical architecture

Previous ultrastructural investigation has identified the conserved existence of perisomatic MTC innervation by certain EPL-IN subsets, including most prominently PV+ EPL-INs [40–42]. Lacking functional confirmation, however, such targeting has not been integrated into current conceptual OB models. Here, we measured synaptic rise times and consistently found unitary FSI-to-MTC inhibition to be faster than spontaneous inhibition from other sources, which, together with post hoc structural analyses of synaptically coupled pairs, compellingly argues that FSIs perisomatically inhibit MTCs.

As a caveat, differences in GABAAR subunit composition may alternatively underlie the observed differences in event kinetics [90]. In the OB, faster α1 subunits distribute equally throughout the EPL while slower α3 subunits concentrate in the upper EPL [90–94], and α1 knockout slows miniature IPSC decay (though not rise times) in MTCs [94]. At least 2 points suggest that the faster rise times of FSI-mediated versus spontaneous inhibition are not due to differential GABAAR subunit composition, however. First, if faster α1 subunits selectively concentrated postsynaptic to FSIs, then the noted laminar difference in subunit expression would suggest that FSIs preferentially innervate MCs instead of TCs, which we did not observe. Second, differences in subunit expression arise predominantly across cells and not across synapses within the same cell [90,93,94]. Selective localization of faster α1 subunits postsynaptic to FSIs would thus further suggest that FSIs preferentially innervate only subsets of MTCs, which was not supported by the high connectivity rates observed.

While our results suggest that FSIs may preferentially inhibit perisomatic MTC domains, they do not exclude FSI interactions with lateral dendrites of distal MTCs. Indeed, monosynaptic rabies viral tracing has revealed broad connectivity of PV+ EPL-INs with MTCs, well exceeding MTC–GC connectivity ranges and consistent with broader odor-tuning of PV+ EPL-INs than GCs [17,95]. Interpretation of these results has further led to the hypothesis that proximal MTC inhibition may be primarily mediated by GCs and not by PV+ EPL-INs [64,95,96], seemingly in contrast to our present findings. As a caveat, however, the efficiency, directionality, and even specificity with which rabies transmits across dendrodendritic and dendrosomatic synapses in the OB (and how that relates to synaptic strength) remains partially unclear. For example, rabies transmits between GCs and PV+ EPL-INs [95,97,98] despite no synaptic connections [99]. Moreover, and of particular note, while viral tracing uncovered broad MTC–PV+ EPL-IN connectivity, the greatest density of connections was still observed among nearby MTCs and PV+ EPL-INs [95], where we have shown that FSIs perisomatically inhibit MTCs. Further, despite GC densities overall far exceeding PV+ EPL-IN densities [5], the number of virally traced short-range MTC–PV+ EPL-IN connections well exceeded the number of short-range MTC–GC connections [95], further suggesting that proximal MTC inhibition may in fact be primarily mediated by PV+ EPL-INs and not GCs. Our present results, revealing prominent perisomatic inhibition of MTCs by nearby FSIs, align well with this latter model. Understanding how such short-range connectivity complements long-range MTC–FSI connectivity stands as an important direction of future research (Fig 8). Given that only approximately 50% of PV+ EPL-IN synapses are reciprocal [40,41], key questions going forward include: (1) how common is distal MTC-to-FSI excitation; (2) how does such distal excitation compare in strength to proximal excitation; and (3) is such distal excitation matched by reciprocal FSI-to-MTC lateral dendrite inhibition? Our simultaneous whole-cell recording approach, combined with precise post hoc morphological reconstruction, is ideally suited to answer these questions.

Despite structural and functional evidence of perisomatic MTC inhibition by FSIs, we failed to detect canonical basket-like innervation patterns using several complementary strategies. Absence of such profuse perisomatic innervation may reflect 2 unique features of MTC–FSI signaling. First, distinct from projection neurons elsewhere in the brain such as neocortex and hippocampus, both somata and dendrites of MTCs are fully excitable and release-competent [100]. Inhibition targeted exclusively to the soma would thus be less effective in controlling MTC signaling than in the case of basket innervation of pyramidal cell somata, through which all synaptic inputs must sum in order to trigger axonal output. Second, distinct from fast-spiking basket cells, FSIs in the OB are anaxonic and inhibit MTCs through dendritic GABA release. Distinct metabolic and trafficking requirements of axons versus dendrites thus limit the total cable length available for FSIs to innervate MTCs, prohibiting profuse somatic wrapping. Moreover, by housing both pre- and postsynaptic machinery, FSI dendrites must distribute in a manner facilitating both GABA release onto MTCs as well as integration of glutamatergic input from the same and other MTCs. By avoiding profuse basket innervation of individual MTC somata, FSI dendrites can likely reciprocally communicate with a larger ensemble of MTCs.

While lacking canonical basket-like structure, perisomatic inhibition of MTCs by FSIs nevertheless proved surprisingly strong. Unitary FSI-to-MTC inhibition hyperpolarized MTCs by 0.46 ± 0.45 mV from resting potentials of –53.0 ± 3.3 mV (n = 8), comparable to unitary basket cell hyperpolarization of hippocampal and neocortical pyramidal cells by 0.45 and 1.2 mV, respectively, from resting potentials of –55 to –60 mV in adult rats [101,102] and far exceeding the predicted unitary GC-to-MTC IPSP amplitude of 0.01 to 0.03 mV [57]. FSIs in the OB may thus achieve comparable function as cortical basket cells through strikingly distinct synaptic architecture.

Close proximity between pre- and postsynaptic machinery at reciprocal dendrodendritic and dendrosomatic synapses between FSIs and MTCs further suggests that FSIs may also support parallel subcellular processing, with excitatory input depolarizing nearby active zones to trigger local GABA release in the absence of a global spike. Such local subthreshold GABA release would not only enable FSIs to dynamically shift the balance between recurrent and lateral inhibition among MTCs, but further regulate flexible ensembles of MTCs, dramatically augmenting the computational power of each individual FSI. At least 2 additional factors may further enhance parallel subcellular processing within FSIs. First, FSI expression of philanthotoxin-sensitive Ca2+-permeable AMPARs suggests that local FSI release may be dynamically enhanced by coincident Ca2+ influx through neighboring AMPARs and Ca2+ channels during MTC–FSI synchronization. Second, multiple dendritic branches of each PV+ EPL-IN strikingly harbor clusters of Na+ channels that colocalize with some features of conventional axon initial segments [48,88], suggesting that FSIs may additionally be capable of generating local dendritic spikes.

Mechanisms and implications of high-fidelity recurrent and lateral inhibition

Our results show that FSIs drive powerful recurrent and lateral MTC inhibition. That contribution of single FSIs to these signaling modes was not only detectable, but constituted a large fraction of the total inhibition recorded at the soma under our experimental conditions was remarkable given that inhibition onto any MTC should conceivably also reflect the summed input from 104 GCs [56,57] as well as a complex array of glomerular layer circuits [5,11,103]. Our results thus suggest that OB output, specifically reflecting MTC somatic spiking, may be dominantly shaped by the comparatively small population of FSIs. A key component underlying this outsized contribution is the surprising finding that 23% of FSIs responded to unitary MTC release with suprathreshold activation. Such prevalent synaptic detonation under baseline conditions not only distinguishes FSIs from GCs as well as rodent neocortical and hippocampal basket cells [104,105], but is further uncommon throughout the entire rodent brain, typically manifesting at specialized connections crucial for high-fidelilty circuit operation [106] and learning [107,108]. Understanding how the structure, function, and plasticity of MTC–FSI synapses may relate to these specialized detonating connections represents a key area of future investigation.

Other functional differences between FSIs and GCs beyond postsynaptic excitation further motivate reevaluation of key tenets of recurrent and lateral MTC inhibition in the EPL and OB sensory processing overall. In particular, while GC output can be strongly gated by centrifugal cortical input [109], FSIs readily mediate both recurrent and lateral inhibition in acute slices with disrupted corticobulbar communication. FSIs and GCs may concordantly regulate OB sensory processing in context-independent and -dependent forms, respectively, paralleling complementary intrinsic versus extrinsic modulation of hippocampal activity by PV+ versus PV– basket cells [39]. Alternatively, FSIs may also integrate various cortical and/or neuromodulatory centrifugal input to influence OB sensory processing in a context-dependent manner distinct from that of GCs. Indeed, recent investigation by Fukunaga and colleagues suggests that perisomatic inhibition, as we show here to be a prominent signaling mode in the OB mediated by FSIs, may be pivotal in reward modulation of MC sensory responses [110]. Direct investigation of whether FSIs receive centrifugal cortical and/or neuromodulatory inputs thus stands as an important next step in evaluating contextual modulation of OB sensory processing.

Of further interest, GCs subdivide into superficial- and deep-branching subpopulations selectively innervating TCs and MCs, respectively, to support distinct modes of slow lateral inhibition and contrast enhancement within the 2 projection neuron populations [35]. While we did not directly test whether individual FSIs can innervate both TCs and MCs, the lack of difference in EPL position between FSIs of synaptically coupled TC–FSI and MC–FSI pairs, together with the equally high TC and MC connectivity rates observed across the FSI population, intriguingly suggests that FSIs may not only account for the synchronous spontaneous inhibition observed within nearby TC–MC pairs [111], but may also mediate fast lateral inhibition between TCs and MCs. Such a link, while speculative, could potentially coordinate fast timescale spike-time patterning across the parallel streams of OB output, and strongly motivates future TC–FSI–MC triplet recordings.

Network simulations have demonstrated that high-fidelity perisomatic lateral inhibition, as mediated by FSIs, is likely to promote MTC synchronization [59,60], consistent with mechanisms well established in neocortical and hippocampal circuits [1,2]. This, together with the demonstration that sparse MTC synchronization is sufficient to activate the majority of postsynaptic FSIs, suggests that MTC–FSI interactions may play a lead role in promoting the fast-timescale MTC synchronization driving gamma-frequency OB oscillations. Further experiments are needed to evaluate this hypothesis, however, particularly given the concurrent occurrence of presumably GC-mediated slow lateral inhibition, which itself is capable of promoting synchronization of resonant gamma-frequency MTC spiking [20,63,112]. Integration of the present unitary synaptic and physiological FSI data into advanced biophysical OB simulations (e.g., [113]) will provide a powerful platform for further investigating precisely when and to what degree FSIs synchronize OB output.

Materials and methods

Animals

All experiments were completed in compliance with the guidelines established by the Institutional Animal Care and Use Committee of Lehigh University (protocol 1580421). OMP-ChR2 mice were maintained on an albino C57BL/6J background and used as heterozygotes on C57BL/6J or albino C57BL/6J background to minimize olfactory sensory neuron signaling deficits, as previously described [114]. All other electrophysiological experiments used multiple strains of mice on the C57BL/6J background and lacking genetic labeling of interneuron populations, and are thus considered wild type; strains included: M71-IRES-Cre [115], M72-IRES-ChR2:EYFP [116], M72-IRES-tauCherry [117], and Tbet-Cre [118], with no difference in results between strains. Immunohistochemical experiments used identical strains of mice, in addition to a subset of experiments (Fig 2M–T) using compound heterozygous crosses of gene-targeted PV-IRES-Cre mice [119] to either RCL-tdTomato mice [120] or RCL-ReaChR:mCitrine mice [121] maintained on the C57BL/6J background. Mice were socially housed when possible and maintained on a 12 h light/dark cycle with ad libitum access to food and water.

Slice preparation

Experiments were performed in acute slices prepared from P21-28 mice (n = 70), consistent with the full maturation of MTC intrinsic and synaptic properties [122–124] and PV+/CRH-Cre+ EPL-IN densities [125–127]. Mice of both sexes were used. For slice preparation, mice were anesthetized with isoflurane and decapitated into ice-cold oxygenated dissection solution containing the following (in mM): 125 NaCl, 25 glucose, 2.5 KCl, 25 NaHCO3, 1.25 NaH2PO4, 3 MgSO4, and 1 CaCl2. Brains were isolated and acute horizontal slices (310 μm thick) were prepared using a vibratome (VT1200S, Leica Biosystems). Slices recovered for 30 min in approximately 37°C oxygenated Ringer’s solution that was identical to the dissection solution except for lower Mg2+ concentrations (1 mM MgSO4) and higher Ca2+ concentrations (2 mM CaCl2). Slices were then stored at room temperature until recording. Slices were prepared at approximately the same time each day relative to the animal facility light/dark cycle.

Electrophysiology

Slices were continuously superfused with warmed oxygenated Ringer’s solution (temperature measured in bath: 30 to 32°C). Tissue was visualized using infrared differential interference contrast (IR-DIC) video microscopy. Recordings were targeted to the medial OB, where the MCL reliably appears as a uniformly compact cell layer, facilitating the differentiation of cell types. MTC cell types were identified as previously [63,128]. Specifically, MTCs with ≥50% of their soma displaced above the outer edge of the MCL border were classified as TCs; remaining MTCs within the MCL were classified as MCs. TCs with somata still contacting the MCL border were classified as deep TCs (dTCs); those with somata separated from the MCL and within the lower half of the EPL were classified as middle TCs (mTCs); remaining TCs with somata separated from the MCL and within the upper half of the EPL were classified as superficial TCs (sTCs). MTC cell type was assessed and classified during live IR-DIC imaging and further verified in post hoc inspection of intracellular Neurobiotin (NB; Vector Laboratories) or Lucifer Yellow CH (LY; Thermo Fisher Scientific). All recorded MTCs were confirmed to have intact apical dendrites and glomerular tufts (except where noted to exclude potential glomerular layer circuit interactions), as assessed through live imaging, post hoc inspection of intracellular NB or LY, and occurrence of spontaneous long-lasting depolarizations (or associated currents) reflecting glomerulus-wide activation [129,130]. Frequent long-lasting depolarizations led to greater variability in baseline membrane potential in some MTC recordings (e.g., Fig 1M and 1O). For MTC pair and quartet recordings, homotypic versus heterotypic glomerular association was determined by combined assessment of: spontaneous long-lasting depolarization and/or inward current correlation [129,130], comparatively slow lateral excitation among homotypic MCs [62,63], and post hoc inspection of intracellular NB.

Current-clamp data were recorded using electrodes filled with (in mM): 135 K-gluconate 1.8 KCl, 8.8 HEPES, 10 Na-phosphocreatine, 4 Mg-ATP, 0.3 Na-GTP, and 0.2 EGTA. Voltage-clamp data were recorded using electrodes filled with (in mM): 131 CsCl, 3.8 K-gluconate, 0.05 KCl, 8.8 HEPES, 10 Na-phosphocreatine, 4 Mg-ATP, and 0.3 Na-GTP. Electrode solutions additionally contained 8.8 mM GABA or glutamate (for EPL-IN or MTC recordings, respectively) to preclude rundown of dendritic release [54,76,131–133], as well as either 0.025 mM Alexa Fluor 488 or 594 hydrazide to permit live visualization and 0.2% NB to permit post hoc inspection. For a subset of recordings, voltage-clamp data were recorded using electrodes filled with (in mM): 66 CsCl, 61 Cs-gluconate, 3.8 K-gluconate, 0.9 KCl, 8.8 HEPES, 8.8 glutamate, 10 Na-phosphocreatine, 4 Mg-ATP, 0.3 Na-GTP, and 0.1% LY; these recordings were excluded from other voltage-clamp data for comparisons of absolute IPSC amplitudes across cells (S7, S10, and S11 Figs). Electrode resistances were 3 to 10 MΩ. Current-clamped cells were held at their resting membrane potential (i.e., 0 pA holding current); voltage-clamped cells were held at –70 mV. In current-clamp recordings, pipette capacitance was neutralized and series resistance (EPL-IN: 37.0 ± 0.9 MΩ [n = 145]; MTC: 18.8 ± 0.9 MΩ [n = 54]) was compensated using the MultiClamp Bridge Balance operation. In voltage-clamp recordings, series resistance (MTC: 20.5 ± 0.5 MΩ [n = 235]) was not compensated but was monitored continuously to ensure adequate electrode access and recording quality. Current-clamp recordings with unstable and/or depolarized resting membrane potential (EPL-INs: >–50 mV; MTCs: >–45 mV) were abandoned to exclude damaged or otherwise unhealthy cells from our data set. Cell-attached data were recorded prior to obtaining whole-cell access, using the same electrodes as used for subsequent current- and voltage-clamp recordings. Data were low-pass filtered at 4 kHz and digitized at 10 kHz using MultiClamp 700A and 700B amplifiers and an ITC-18 acquisition board controlled by custom software written in IGOR Pro.

To probe unitary synaptic output of EPL-INs onto MTCs, we monitored MTC voltage (n = 54 pairs) or current (n = 91 pairs) while injecting 1-ms suprathreshold current pulses (1.5 to 2.5 nA) into the EPL-IN to trigger single spikes. Voltage-clamped MTCs were recorded with a high-Cl– internal solution to reverse and amplify the GABAergic driving force, enabling IPSCs to be detected at hyperpolarized potentials. In the same pairs, reciprocal unitary synaptic output of the MTC onto the EPL-IN was investigated by monitoring EPL-IN voltage while injecting a 1-ms suprathreshold current pulse (1.5 to 2.5 nA) or a 2-ms voltage step to 0 mV (approximating the depolarization of a single spike) in the current- or voltage-clamped MTCs, respectively. Synaptic pharmacology was assessed using bath application of DL-AP5 (50 μM; Tocris, 3693), NBQX (10 μM; Tocris, 1262), gabazine (10 μM; Tocris, 1262), and PhTx-74 (10 μM; Alomone Labs, P-120).

To evaluate whether MTCs and FSIs were also linked by electrical coupling, we injected hyperpolarizing step currents sequentially into each cell of a subset of synaptically coupled MTC–FSI pairs recorded in current-clamp mode and calculated coupling coefficients. MTC step current injection (–407 ± 64 pA) strongly hyperpolarized MTC membrane potentials (ΔV = –32.9 ± 2.0 mV) but caused no significant change in FSI membrane potentials (ΔV = –0.02 ± 0.08 mV; p = 0.8, t6 = –0.3, n = 7, t test), yielding an MTC-to-FSI coupling coefficient not significantly different from zero (0.13 ± 0.26%, p = 0.7, t6 = 0.5, n = 7, t test). Similarly, FSI step current injection (–220 ± 58 pA) strongly hyperpolarized FSI membrane potentials (ΔV = –19.3 ± 1.4 mV) but caused no significant change in MTC membrane potentials (ΔV = –0.03 ± 0.09 mV; p = 0.7, t4 = –0.4, n = 5, t test), yielding an FSI-to-MTC coupling coefficient also not significantly different from zero (0.18 ± 0.47%, p = 0.7, t4 = 0.4, n = 5, t test).

For MTC–FSI pair recordings combined with optogenetic photostimulation, slices were illuminated by a 75 W xenon arc lamp passed through a YFP filter set and 60× water-immersion objective centered on a nearby lateral glomerulus (approximately 2 to 3 glomeruli away from the recorded MTC glomerulus), with field-stop closed to achieve single glomerulus activation, as previously performed [114]. Photostimulation consisted of a 10-ms light pulse.

Histology

For fluorescent NB labeling with post hoc immunohistochemistry, acute slices containing NB-filled cells were fixed with paraformaldehyde (PFA; 4%) in phosphate buffer (PB; 0.1 M) for >24 h at 4°C, washed, and then incubated in blocking solution (2% normal serum and 0.4% Triton X-100, in PB). Slices were then incubated in blocking solution containing Alexa Fluor 488 or 594 Streptavidin (1 μg/ml) and primary antibodies, washed, incubated in blocking solution containing secondary antibodies and Hoechst 33342 (0.25 μg/ml), washed, and mounted with Fluoromount G (SouthernBiotech). For fluorescent NB labeling without post hoc immunohistochemistry, antibodies were omitted. Non-fluorescent chromogenic NB labeling was performed as previously described [71].

For immunohistochemistry, adult mice were anesthetized with intraperitoneal injection of ketamine (200 mg/kg) and xylazine (20 mg/kg) and then transcardially perfused with 0.1 M phosphate-buffered saline followed by PFA (4%) in PB. Brains were extracted, postfixed overnight, and then sectioned with a vibratome (Leica, VT1000S). Free-floating 50-μm sagittal sections were then incubated in blocking solution (2% normal serum and 0.1% Triton X-100, in PB), washed, and incubated in antibody solution (2% normal serum and 0.05% Tween 20, in PB) containing combinations of the following primary antibodies: guinea pig anti-parvalbumin (1:2,000; Synaptic Systems, 195 004), mouse anti-synaptotagmin-2 (1:200; Zebrafish International Resource Center, znp-1), rabbit anti-TRIM-46 (1:1,000; Synaptic Systems, 377 008), rabbit anti-Lucifer Yellow (1:1,000; Thermo Fisher Scientific, A-5750), rabbit anti-parvalbumin (1:2,000; Synaptic Systems, 195 002), rabbit anti-synaptobrevin-1 (1:500; Synaptic Systems, 104 002), rabbit anti-vasoactive intestinal peptide (1:4,000; ImmunoStar, 20077), and sheep anti-tyrosine hydroxylase (1:1,000; Millipore, AB1542). Sections were then washed, incubated in antibody solution with fluorescent secondary antibodies and Hoechst 33342, washed, and mounted with Fluoromount G. All washes were performed with PB. All incubation steps took place for 1 to 3 h at room temperature or overnight at 4°C. Secondary antibodies were used at 1:600. At least 2 sections from each of 2 male and 2 female mice were examined for each experiment; no gross sex-dependent differences were observed.

Brightfield and fluorescent widefield images were collected on an upright Nikon Eclipse E1000 microscope using 20× air and 100× oil-immersion objectives. Fluorescent confocal z-stacks were collected on an inverted Zeiss LSM 880 confocal microscope using a 20× air objective and 25× and 63× oil-immersion objectives, 0.5-μm z-steps, 2,048 × 2,048 resolution, and 3-pixel median filter to reduce noise. Morphological images included in figures show maximum- or minimum-intensity projections of fluorescent confocal z-stacks or chromogenic widefield z-stacks, respectively, except where noted. Neuron morphologies were reconstructed from confocal z-stacks and analyzed using the SNT plugin in ImageJ (Fiji) [134]. Intersomatic distances represent the shortest distance between somata irrespective of direction (zero for directly contacting somata) and were measured from single widefield fluorescent or brightfield images of intracellular NB and LY, and thus do not account for potential differences in cell depth in the tissue.

Data analysis

Values reported are either mean ± SEM or median (first quartile [Q1], third quartile [Q3]) for normally or non-normally distributed data, respectively. Line plots with thin and thick lines denote individual trials and mean, respectively. Line plots with shading denote mean ± SEM. Single, double, and triple asterisks in figures and tables denote statistical significance at p < 0.05, p < 0.01, and p < 0.001 levels, respectively. For each statistical test, data normality was first determined by the Shapiro–Wilk test, and nonparametric tests applied where appropriate. For visual comparison of normally distributed data, all individual data points are displayed in addition to sample mean and SEM. For visual comparison of non-normally distributed data, data are displayed as standard boxplots, with data points denoting sample outliers. Significance thresholds were corrected for multiple comparisons where indicated. All recorded traces, spikes, and potential unitary synaptic events were visually inspected for detection accuracy. No differences in synaptic connectivity or intrinsic biophysical properties were detected between sex, and data were therefore pooled across male and female mice.

EPL-INs were classified as FSIs or RSIs using step current-evoked spiking responses, as shown in Fig 1B–1E and reinforced by unbiased agglomerative hierarchical clustering (Fig 1F) and principal component analysis (S1 Fig). Hierarchical clustering was performed on z-scored intrinsic biophysical properties using Ward’s method, with properties ordered as in S1 Fig, beginning with AHP 50% decay. Given the binary connectivity profile observed among MTCs and FSIs versus RSIs, for analysis of unitary synaptic properties the presence of a unitary connection was used as a complementary criterion to identify an additional 10 FSIs from pair recordings for which step current-evoked spiking responses were not obtained. Results did not differ if these 10 FSIs were omitted.

Resting membrane potential was recorded immediately after obtaining whole-cell access, and for EPL-INs was defined as the 10th percentile of voltage recorded for each cell to limit the contribution of prodigious sEPSP rates (e.g., S2 Fig). Membrane time constant, input resistance, and capacitance were calculated using the voltage trajectory and maximum voltage change evoked by a 50-pA, 100-ms hyperpolarizing step current injection (e.g., S2 Fig). Spontaneous firing rates were defined as the spike-count firing rate calculated from the total number of spikes not driven by evoked unitary synaptic input recorded over 151.6 s (103.2, 184.0) (n = 145) for each cell.

Firing rate-current (FI) curves were examined using 500-ms depolarizing step current injections ranging from 50 to 600 pA in steps of 50 pA for FSIs and less excitable RSIs, while RSIs that readily underwent depolarization block were examined using step currents ranging from 10 to 100 pA in steps of 10 pA (e.g., S2 Fig). FI curve spike times were detected using a voltage derivative threshold of 15 mV/ms. FI curve rates were calculated from the median inverse interspike interval (ISI) evoked by each step current. Spike properties (with the exception of afterdepolarization [ADP] amplitudes) were calculated from the first spike evoked by the weakest suprathreshold step current (i.e., rheobase). Spike amplitude was calculated as the difference between spike threshold (i.e., the voltage at spike onset) and spike peak. Maximum and minimum spike slopes were calculated as the maximum and minimum voltage derivatives. Spike width was calculated as the full-width at half-maximum spike amplitude. Spike afterhyperpolarization (AHP) amplitude was calculated as the difference in spike threshold and the minimum voltage reached within 10 ms of spike onset. AHP 50% decay was calculated as the latency from AHP onset (i.e., spike falling phase matching spike threshold) to decay of the AHP to 50% of its amplitude. Maximum FI curve gain was calculated as the maximum FI curve derivative. Maximum FI curve rate was calculated as the inverse of the minimum ISI detected. Relative and absolute spiking adaptation were calculated from the response to the weakest suprathreshold step current evoking sustained activity (≥5 spikes) and from the first evoked spike cluster within that response for FSIs exhibiting clustered spiking (e.g., Fig 1B and 1C).

Spike times evoked by 1-ms suprathreshold current pulses (1.5 to 2.5 nA) or unitary MTC release were calculated as the time at which membrane potentials (upsampled 100-fold) exceeded –30 mV. EPL-IN spike waveforms evoked by such current pulses typically lacked an AHP and instead displayed an ADP (e.g., S2 Fig). ADP amplitudes were calculated as the difference between the minimum and maximum post-spike voltage (occurring within 6 ms of spike falling phases) for spike waveforms lacking an AHP.

Postsynaptic events were detected using a standard template-matching function in Axograph [135] with double-exponential template (S3 Table). For analysis of unitary MTC–EPL-IN synaptic connectivity, presynaptic activation (either pulse-evoked spikes or brief voltage-steps) was triggered every 8 to 22 s and peristimulus time histograms of postsynaptic events were calculated across all trials. Unitary connectivity was classified as significant if the probability of a postsynaptic event within the first time bin following presynaptic spike time or voltage step onset exceeded the mean + the standard deviation × multiplication factor of the event probability across the 1 s preceding presynaptic spike time or voltage step onset. For tests of unitary EPL-IN excitation, 5 ms time bins were used (accommodating longer latencies following presynaptic voltage steps—see below) and the significance multiplication factor was set to 2.75; suprathreshold postsynaptic responses occurring within 5 ms of presynaptic activation (i.e., detonation) were additionally used to classify a unitary connection as significant. For tests of unitary MTC inhibition, 2.5 ms time bins were used and the significance multiplication factor was set to 3. Unitary postsynaptic event latency was calculated as the latency from presynaptic spike onset to the time at which the postsynaptic response (upsampled 20-fold) reached 5% of its amplitude. Unitary FSI excitation latencies were significantly longer (2.36 ± 0.06 ms versus 1.17 ± 0.04 ms; p = 6.6 × 10–25, t75 = 15.4, t test) and had higher jitter (0.47 [0.35, 0.62] versus 0.24 [0.12, 0.26]; p = 2.7 × 10–7, r.s. = 2,290, Wilcoxon rank-sum test) when measured from MTC depolarization onset in voltage-clamp mode (n = 46) than from MTC spike onset in current-clamp mode (n = 31), reflecting lower precision in identifying MTC activation timing in voltage-clamp mode. All unitary FSI excitation latencies reported in the main text, including uEPSP and detonation latencies, are thus restricted to pairs recorded in current-clamp mode with precisely measured presynaptic spike onsets. Postsynaptic rise times were calculated as the duration for the postsynaptic response (upsampled 20-fold) to increase from 20% to 80% of its amplitude. sEPSP rates were calculated for each EPL-IN from the total number of EPSPs occurring in the 1 s preceding presynaptic activation across all trials, encompassing 39.9 ± 1.3 s (n = 145) total recording for each cell. Median sEPSP half-widths were calculated as the full-width at half-maximum amplitude of the median sEPSP waveform. For FSIs exhibiting an uEPSP in MTC–FSI recordings, ΔVthresh. was calculated as the difference in rheobase spike threshold and median membrane potential prior to unitary excitatory input.

For analysis of cell-attached spontaneous spike-time synchrony (Fig 3C), spike times were detected using a current threshold of 18 pA and then convolved with a Gaussian kernel (1 ms standard deviation) to account for slight differences in spike waveforms. Trains of convolved spike times were then mean-subtracted and the cross-correlogram calculated.

For analysis of lateral inhibition among MTCs, peristimulus time histogram analysis was used as above to classify lateral inhibition as significant, with 10 ms time bins (accommodating disynaptic latencies) and a significance multiplication factor of 2 for current-clamped MTCs and 3 for voltage-clamped MTCs.

Supporting information

S1 Fig Principal component analysis reinforces subdivision of EPL-INs into FSIs and RSIs.

(A) Projection of 104 EPL-INs onto the first 2 principal components (PC1 and PC2) defined by principal component analysis of z-scored intrinsic biophysical properties, revealing 2 major clusters matching FSI (green) and RSI (orange) subtypes. (B) Decomposition of PC1 and PC2 loading by each intrinsic biophysical property. Source data for panels A and B are provided in Supporting information, S7 Data.

(TIF)

S2 Fig EPL-IN subtypes exhibit stark differences in most intrinsic biophysical properties.

(A–E) Diverse responses used to calculate intrinsic biophysical properties for the example FSI from Fig 1B, including: spontaneous activity at resting membrane potential (A), mean response to negative step current injection, with single-exponential fit (dashed black line) (B), mean spike waveform evoked by 1-ms suprathreshold current injection (C), firing rate-current relationship (D), and interspike interval (ISI) coefficient of variation evoked by positive step current injection (E). Spontaneous spike in A truncated to better visualize synaptic activity. Inset in A: enlargement of boxed region. F–T: Same as A–E for the example FSI and RSIs from Fig 1C–1E. Insets in A, F, K, P are identically scaled. Source data for panels D, E, I, J, N, O, S, and T are provided in Supporting information, S8 Data.

(TIF)

S3 Fig PV expression distinguishes FSIs from RSIs.

Intracellular NB and post hoc PV staining with 50-μm magnified region centered on somata (left), inverted NB (middle), and step current-evoked spiking (right) of a panel of EPL-INs. Spiking responses are color-coded to reflect FSI vs. RSI physiology, as in Fig 1.

(TIF)

S4 Fig Neither FSIs nor RSIs are dopaminergic.

Intracellular NB and post hoc TH staining with 50-μm magnified region centered on somata (left), inverted NB (middle), and step current-evoked spiking (right) of a panel of EPL-INs. Spiking responses are color-coded to reflect FSI vs. RSI physiology, as in Fig 1. An example TH+ short-axon cell (SAC) exhibiting tonic spontaneous firing is additionally included as positive control for TH staining.

(TIF)

S5 Fig VIP expression poorly distinguishes FSIs and RSIs.

Intracellular NB and post hoc VIP staining with 50-μm magnified region centered on somata (left; single optical confocal planes), inverted NB (middle; maximum-intensity confocal projection), and step current-evoked spiking (right) of a panel of EPL-INs. Spiking responses are color-coded to reflect FSI vs. RSI physiology, as in Fig 1.

(TIF)

S6 Fig Unitary MTC-to-RSI excitation in a solitary example was distinctly weaker than FSI excitation.

(A, B) Step current-evoked spiking response (A) and unitary synaptic interactions (B) for the solitary MTC–RSI pair exhibiting significant unitary MTC-to-RSI excitation (morphology not recovered). Asterisk marks significant postsynaptic response. Inset: mean postsynaptic RSI voltage. (C) The MTC-to-RSI uEPSP amplitude was markedly weaker than FSI uEPSPs (n = 69). Source data for panel C are provided in Supporting information, S9 Data.

(TIF)

S7 Fig Connected MTC–FSI pairs exhibit shorter intersomatic distances than unconnected pairs.

(A) MTC–RSI pairs (n = 19) exhibited modestly shorter intersomatic distances than MTC–FSI pairs (n = 97) (*p = 0.02, r.s. = 808, Wilcoxon rank-sum test). (B) Among MTC–FSI pairs, reciprocally connected pairs exhibited shorter intersomatic distances than unconnected pairs (p = 0.02, F3,55 = 3.4, one-way ANOVA; reciprocal (n = 32) vs. excitation only (n = 9): p = 0.3, reciprocal vs. inhibition only (n = 4): p = 0.4, reciprocal vs. unconnected (n = 14): p = 0.03, excitation only vs. inhibition only: p = 1.0, excitation only vs. unconnected: p = 0.9, inhibition only vs. unconnected: p = 1.0, post hoc Tukey–Kramer test). (C) MTC–FSI pairs with significant unitary MTC-to-FSI excitation (n = 71) exhibited shorter intersomatic distances than pairs with no excitatory connectivity (n = 26) (**p = 2.5 × 10–3, r.s. = 3,108, Wilcoxon rank-sum test). (D) MTC–FSI pairs with significant unitary FSI-to-MTC inhibition (n = 36) exhibited shorter intersomatic distances than pairs with no inhibitory connectivity (n = 23) (**p = 9.7 × 10–3, t57 = 2.7, two-sample t test). Analysis restricted to pairs with voltage-clamped MTCs (and therefore sensitive detection of unitary inhibition). (E, F) Neither MTC-to-FSI uEPSP amplitudes (E) nor FSI-to-MTC uIPSC amplitudes (F) correlated with intersomatic distance (uEPSP: n = 77; p = 0.1, t75 = 1.7, linear regression, slope not significantly different from 0; uIPSC: n = 29; p = 0.4, t27 = 0.8, linear regression, slope not significantly different from 0). Pairs lacking connectivity (i.e., uEPSP or uIPSC amplitude of zero) not included in analysis. Source data for all panels are provided in Supporting information, S10 Data.

(TIF)

S8 Fig Unitary MTC–FSI synaptic pharmacology.

(A) Recording from an example MTC–FSI pair (morphology not recovered) showing MTC-to-FSI uEPSP amplitudes before and after combined bath application of glutamatergic antagonists NBQX (10 μM) and AP5 (50 μM) and subsequent application of GABAAR antagonist gabazine (10 μM). (B) Postsynaptic FSI voltages from the pair in A. Traces in each subplot correspond to the bracketed trials in A. Asterisk marks significant unitary postsynaptic response measured during control trials. (C) Unitary MTC-to-FSI excitation was blocked by combined application of NBQX and AP5 and partially recovered upon wash-out in 4 MTC–FSI pairs (p = 2.2 × 10–12, F3,12 = 414.9, one-way ANOVA; ctrl vs. N/A: ***p = 5.5 × 10–9, ctrl vs. N/A/G: ***p = 5.5 × 10–9, ctrl vs. wash: ***p = 5.5 × 10–9, N/A vs. N/A/G: p = 0.9, N/A vs. wash: *p = 0.03, N/A/G vs. wash: **p = 0.01, post hoc Tukey–Kramer test). (D, E) Same as A and B for FSI-to-MTC uIPSCs recorded in the same example pair. (F) Unitary FSI-to-MTC inhibition was blocked by application of gabazine and partially recovered upon wash-out in the same 4 MTC–FSI pairs as C (p = 1.7 × 10–3, F3,12 = 9.5, one-way ANOVA; ctrl vs. N/A: p = 0.33, ctrl vs. N/A/G: **p = 2.1 × 10–3, ctrl vs. wash: **p = 8.3 × 10–3, N/A vs. N/A/G: *p = 0.046, N/A vs. wash: p = 0.17, N/A/G vs. wash: p = 0.85, post hoc Tukey–Kramer test). Filled symbols in C and F correspond to the example pair shown. Source data for panels A, C, D, and F are provided in Supporting information, S11 Data.

(TIF)

S9 Fig MTC–FSI connectivity is restricted to infraglomerular layers.

(A) Example MTC–FSI pair with MTC apical dendrite truncated prior to entering glomerular layer (open arrowhead). (B, C) FSI fast-spiking response to step current injection (B) and unitary synaptic connectivity with MTC (C). Asterisks mark significant unitary postsynaptic responses. (D–F) Same as A–C for a second example MTC–FSI pair.

(TIF)

S10 Fig MCs and TCs exhibit similar unitary connectivity with FSIs.

(A) Detection of unitary FSI excitation did not significantly differ between MC–FSI and TC–FSI pairs (upper; p = 0.3, χ2[1] = 1.0, χ2 test) even when considering only middle and superficial TCs (m/sTCs) to exclude potential misclassification of deep TCs (lower; p = 0.2, χ2[1] = 1.3, χ2 test). Likewise, the proportion of FSIs responding to unitary MTC release with detonation did not significantly differ between MC–FSI and TC–FSI pairs (upper; p = 0.3, χ2[1] = 1.2, χ2 test) or between MC–FSI and m/sTC–FSI pairs (lower; p = 0.1, χ2[1] = 2.3, χ2 test). (B) FSI uEPSP amplitudes did not significantly differ between MC-FSI (n = 21) and TC-FSI pairs (n = 56) (upper; p = 1.0, r.s. = 820, Wilcoxon rank-sum test) or between MC–FSI and m/sTC–FSI pairs (n = 46) (lower, p = 1.0, r.s. = 711, Wilcoxon rank-sum test). (C) Across MTC–FSI pairs with significant unitary MTC-to-FSI excitation, FSI somatic depth throughout the EPL did not differ between MC–FSI (n = 19) and TC–FSI pairs (n = 53) (upper; p = 0.5, r.s. = 646, Wilcoxon rank-sum test) or between MC–FSI and m/sTC–FSI pairs (n = 43) (lower, p = 0.1, t60 = 1.5, two-sample t test). (D) Detection of unitary MTC inhibition did not significantly differ between MC–FSI and TC–FSI pairs (upper; p = 0.2, χ2[1] = 1.9, χ2 test) or between MC–FSI and m/sTC–FSI pairs (lower; p = 0.1, χ2[1] = 2.4, χ2 test); only voltage-clamped MTCs were considered for peak detection sensitivity. (E) MTC uIPSC amplitudes did not significantly differ between MC–FSI (n = 5) and TC–FSI pairs (n = 29) (upper; p = 0.6, r.s. = 100, Wilcoxon rank-sum test) or between MC–FSI and m/sTC–FSI pairs (n = 26) (lower; p = 0.6, r.s. = 90, Wilcoxon rank-sum test). (F) Across MTC–FSI pairs with significant unitary FSI-to-MTC inhibition, FSI somatic depth throughout the EPL did not differ between MC–FSI (n = 11) and TC–FSI pairs (n = 33) (p = 0.8, t42 = 0.3, two-sample t test) or between MC–FSI and m/sTC–FSI pairs (n = 30) (lower, p = 0.9, t39 = 0.2, two-sample t test). (G) The proportion of MTC–FSI pairs exhibiting reciprocal unitary connectivity did not differ between MC–FSI and TC–FSI pairs (upper; p = 0.3, χ2[1] =1.0, χ2 test) or between MC–FSI and m/sTC–FSI pairs (lower; p = 0.2, χ2[1] = 1.4, χ2 test). Source data for all panels are provided in Supporting information, S12 Data.

(TIF)

S11 Fig Comparison of unitary FSI-to-MTC and MTC-to-FSI synaptic transmission properties.

(A) FSI-to-MTC uIPSC latency (n = 44) was significantly shorter than MTC-to-FSI uEPSP latency (n = 31) (***p = 8.0 × 10–5, r.s. = 1,545, Wilcoxon rank-sum test). (B) FSI-to-MTC uIPSC jitter and MTC-to-FSI uEPSP jitter were equivalent (p = 0.5, r.s. = 1,117, Wilcoxon rank-sum test). (C) Trial-to-trial FSI-to-MTC uIPSC event probability (n = 44) was significantly lower than trial-to-trial MTC-to-FSI uEPSP event probability (n = 79) (***p = 5.4 × 10–8, r.s. = 5,838, Wilcoxon rank-sum test). Unitary FSI-to-MTC IPSP latency, jitter, and probability not included in comparisons due to limited unitary IPSP detection sensitivity (Fig 1R). (D) Across all MTC–FSI pairs with at least 1 direction of unitary connectivity, FSI-to-MTC uIPSC amplitude positively correlated with MTC-to-FSI uEPSP amplitude (n = 41; **p = 1.9 × 10–3, t39 = 3.3, R2 = 0.22, linear regression, slope significantly different from 0). For pairs exhibiting exclusive FSI detonation, uEPSP amplitudes were estimated as the difference between resting membrane potential and spike threshold (ΔVthresh.), as in Fig 3G. Shading denotes 95% confidence interval. Source data for all panels are provided in Supporting information, S13 Data.

(TIF)

S12 Fig Syt2 clusters do not selectively target MTC axon initial segments.

Single confocal optical plane (left) and maximum-intensity projection (right; approximately 50 μm depth) of Syt2 and axon initial segment component TRIM-46 in the OB, revealing an absence of clear Chandelier-like innervation of MTCs.

(TIF)

S13 Fig Comparison of spontaneously alternating FSI detonation vs. uEPSP trials reveals high-fidelity recurrent MTC inhibition.

(A, B) Example MTC–FSI pair (A) in which unitary MTC release triggers FSI detonation on some trials (light green) and uEPSPs on other trials (dark green) (B). Asterisk marks significant unitary postsynaptic response. (C) Subtraction of mean MTC currents across FSI detonation vs. uEPSP trials from B isolates IPSC waveforms time-locked to FSI detonation. (D–F) Same as A–C for an example MTC-FSI pair recorded in current-clamp, revealing isolation of an IPSP waveform time-locked to FSI detonation.

(TIF)

S14 Fig Directly neighboring heterotypic MTCs are preferentially linked by fast lateral inhibition.

(A) Among heterotypic MTC pairs tested for both fast and slow lateral inhibition (Fig 5), pairs with fast lateral inhibition (n = 9) exhibited shorter intersomatic distances than pairs without fast lateral inhibition (n = 34) (**p = 8.3 × 10–3, r.s. = 111, Wilcoxon rank-sum test) (upper). No difference was observed in the number of glomeruli separating the apical dendrites of MTCs with vs. without fast lateral inhibition (p = 0.4, χ2[2] = 1.6, χ2 test) (lower). (B) Pairs with slow lateral inhibition (n = 9) exhibited comparable intersomatic distances as pairs without slow lateral inhibition (n = 34) (p = 0.3, r.s. = 166, Wilcoxon rank-sum test) (upper). No difference was observed in glomerular separation (p = 0.2, χ2[2] = 3.4, χ2 test) (lower). (C) Pairs with fast lateral inhibition (n = 9) exhibited comparable intersomatic distances as pairs with slow lateral inhibition (n = 9) (p = 0.2, r.s. = 70.5, Wilcoxon rank-sum test) (upper). No difference was observed in glomerular separation (p = 0.1, χ2[2] = 2.5, χ2 test) (lower). (D, E) Same as A and C, but including heterotypic MTC pairs tested for both fast and slow lateral inhibition (Fig 5) as well as heterotypic MTC pairs comprising each quartet tested for fast lateral inhibition (Fig 6; only single presynaptic MTC activation results included). Pairs with fast lateral inhibition (n = 17) exhibited shorter intersomatic distances than pairs without fast lateral inhibition (n = 60) (**p = 9.6 × 10–3, r.s. = 453.5, Wilcoxon rank-sum test) (D upper) but comparable intersomatic distances as pairs with slow lateral inhibition (n = 9) (p = 0.9, r.s. = 227.5, Wilcoxon rank-sum test) (E upper); no difference was observed in glomerular separation as a function of fast lateral inhibition (p = 0.07, χ2[2] = 5.3, χ2 test) (D lower) or fast vs. slow lateral inhibition (p = 0.07, χ2[2] = 3.6, χ2 test) (E lower). (F) Probability of detecting fast lateral inhibition (upper; pairs from Figs 5 and 6) or slow lateral inhibition (lower; pairs form Fig 5) plotted as a function of intersomatic distance starting with 0 μm and then binned in 20-μm increments (exclusive lower bound and inclusive upper bound). Number of pairs in each bin noted next to each marker. Source data for all panels are provided in Supporting information, S14 Data.

(TIF)

S1 Table EPL-IN intrinsic biophysical properties.

Source data provided in Supporting information, S15 Data.

(DOCX)

S2 Table EPL-IN anatomical and morphometric properties.

Source data provided in Supporting information, S16 Data.

(DOCX)

S3 Table Template parameters for postsynaptic event detection.

(DOCX)

S1 Data Source data for Fig 1.

Spreadsheet of data for hierarchical clustering, Sholl analysis, and unitary synaptic connectivity analysis.

(XLS)

S2 Data Source data for Fig 2.

Spreadsheet of data for synaptic event rise time analysis.

(XLS)

S3 Data Source data for Fig 3.

Spreadsheet of data for unitary FSI excitation and threshold presynaptic MTC population size analysis.

(XLS)

S4 Data Source data for Fig 4.

Spreadsheet of data for fast lateral inhibition prevalence and PhTx-74 sensitivity analysis.

(XLS)

S5 Data Source data for Fig 5.

Spreadsheet of data for fast and slow lateral inhibition prevalence analysis.

(XLS)

S6 Data Source data for Fig 6.

Spreadsheet of data for MTC quartet fast lateral inhibition amplitude and prevalence analysis.

(XLS)

S7 Data Source data for S1 Fig.

Spreadsheet of data for principal component analysis.

(XLS)

S8 Data Source data for S2 Fig.

Spreadsheet of data for example firing rate-current and firing irregularity-current plots.

(XLS)

S9 Data Source data for S6 Fig.

Spreadsheet of data for comparing unitary excitation strength between FSIs and the singularly excited RSI.

(XLS)

S10 Data Source data for S7 Fig.

Spreadsheet of data for analysis of unitary connectivity as a function of intersomatic distance.

(XLS)

S11 Data Source data for S8 Fig.

Spreadsheet of data for unitary synaptic pharmacology analysis.

(XLS)

S12 Data Source data for S10 Fig.

Spreadsheet of data for analysis of unitary connectivity as a function of MTC subtype.

(XLS)

S13 Data Source data for S11 Fig.

Spreadsheet of data for comparing unitary FSI excitation and MTC inhibition.

(XLS)

S14 Data Source data for S14 Fig.

Spreadsheet of data for analysis of MTC lateral inhibition as a function of intersomatic distance and glomerular separation.

(XLS)

S15 Data Source data for S1 Table.

Spreadsheet of data for EPL-IN intrinsic biophysical property analysis.

(XLS)

S16 Data Source data for S2 Table.

Spreadsheet of data for EPL-IN anatomical and morphometric property analysis.

(XLS)

We thank members of the Cheetham and Haas laboratories for helpful discussion.

Abbreviations

ADP afterdepolarization

AHP afterhyperpolarization

dTC deep tufted cell

EPL external plexiform layer

EPL-IN external plexiform layer interneuron

FSI fast-spiking interneuron

GC granule cell

IPSC inhibitory postsynaptic current

IPSP inhibitory postsynaptic potential

IR-DIC infrared differential interference contrast

ISI interspike interval

LY Lucifer Yellow

MC mitral cell

MTC mitral and tufted cell

OB olfactory bulb

OMP olfactory marker protein

PB phosphate buffer

PFA paraformaldehyde

PV parvalbumin

RSI regular-spiking interneuron

sTC superficial tufted cell

TC tufted cell

VIP vasoactive intestinal peptide

10.1371/journal.pbio.3002660.r001
Decision Letter 0
Schnell, PhD Christian Senior Editor
© 2024 Christian Schnell, PhD
2024
Christian Schnell, PhD
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
3 May 2024

Dear Dr Burton,

Thank you for submitting your manuscript entitled "Fast-spiking interneuron detonation drives high-fidelity inhibition in the olfactory bulb" for consideration as a Research Article by PLOS Biology.

Your manuscript has now been evaluated by the PLOS Biology editorial staff as well as by an academic editor with relevant expertise and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. After your manuscript has passed the checks it will be sent out for review. To provide the metadata for your submission, please Login to Editorial Manager (https://www.editorialmanager.com/pbiology) within two working days, i.e. by May 05 2024 11:59PM.

If your manuscript has been previously peer-reviewed at another journal, PLOS Biology is willing to work with those reviews in order to avoid re-starting the process. Submission of the previous reviews is entirely optional and our ability to use them effectively will depend on the willingness of the previous journal to confirm the content of the reports and share the reviewer identities. Please note that we reserve the right to invite additional reviewers if we consider that additional/independent reviewers are needed, although we aim to avoid this as far as possible. In our experience, working with previous reviews does save time.

If you would like us to consider previous reviewer reports, please edit your cover letter to let us know and include the name of the journal where the work was previously considered and the manuscript ID it was given. In addition, please upload a response to the reviews as a 'Prior Peer Review' file type, which should include the reports in full and a point-by-point reply detailing how you have or plan to address the reviewers' concerns.

During the process of completing your manuscript submission, you will be invited to opt-in to posting your pre-review manuscript as a bioRxiv preprint. Visit http://journals.plos.org/plosbiology/s/preprints for full details. If you consent to posting your current manuscript as a preprint, please upload a single Preprint PDF.

Feel free to email us at plosbiology@plos.org if you have any queries relating to your submission.

Kind regards,

Christian

Christian Schnell, PhD

Senior Editor

PLOS Biology

cschnell@plos.org

10.1371/journal.pbio.3002660.r002
Decision Letter 1
Schnell, PhD Christian Senior Editor
© 2024 Christian Schnell, PhD
2024
Christian Schnell, PhD
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
13 Jun 2024

Dear Dr Burton,

Thank you for your patience while your manuscript "Fast-spiking interneuron detonation drives high-fidelity inhibition in the olfactory bulb" went through peer-review at PLOS Biology. Your manuscript has now been evaluated by the PLOS Biology editors, an Academic Editor with relevant expertise, and by several independent reviewers.

In light of the reviews, which you will find at the end of this email, we are pleased to offer you the opportunity to address the comments from the reviewers in a revision that we anticipate should not take you very long. We will then assess your revised manuscript and your response to the reviewers' comments with our Academic Editor aiming to avoid further rounds of peer-review, although might need to consult with the reviewers, depending on the nature of the revisions.

We expect to receive your revised manuscript within 1 month. Please email us (plosbiology@plos.org) if you have any questions or concerns, or would like to request an extension.

At this stage, your manuscript remains formally under active consideration at our journal; please notify us by email if you do not intend to submit a revision so that we withdraw the manuscript.

**IMPORTANT - SUBMITTING YOUR REVISION**

Your revisions should address the specific points made by each reviewer. Please submit the following files along with your revised manuscript:

1. A 'Response to Reviewers' file - this should detail your responses to the editorial requests, present a point-by-point response to all of the reviewers' comments, and indicate the changes made to the manuscript.

*NOTE: In your point-by-point response to the reviewers, please provide the full context of each review. Do not selectively quote paragraphs or sentences to reply to. The entire set of reviewer comments should be present in full and each specific point should be responded to individually.

You should also cite any additional relevant literature that has been published since the original submission and mention any additional citations in your response.

2. In addition to a clean copy of the manuscript, please also upload a 'track-changes' version of your manuscript that specifies the edits made. This should be uploaded as a "Revised Article with Changes Highlighted " file type.

*Resubmission Checklist*

When you are ready to resubmit your revised manuscript, please refer to this resubmission checklist: https://plos.io/Biology_Checklist

To submit a revised version of your manuscript, please go to https://www.editorialmanager.com/pbiology/ and log in as an Author. Click the link labelled 'Submissions Needing Revision' where you will find your submission record.

Please make sure to read the following important policies and guidelines while preparing your revision:

*Published Peer Review*

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. Please see here for more details:

https://blogs.plos.org/plos/2019/05/plos-journals-now-open-for-published-peer-review/

*PLOS Data Policy*

Please note that as a condition of publication PLOS' data policy (http://journals.plos.org/plosbiology/s/data-availability) requires that you make available all data used to draw the conclusions arrived at in your manuscript. If you have not already done so, you must include any data used in your manuscript either in appropriate repositories, within the body of the manuscript, or as supporting information (N.B. this includes any numerical values that were used to generate graphs, histograms etc.). For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5

*Blot and Gel Data Policy*

We require the original, uncropped and minimally adjusted images supporting all blot and gel results reported in an article's figures or Supporting Information files. We will require these files before a manuscript can be accepted so please prepare them now, if you have not already uploaded them. Please carefully read our guidelines for how to prepare and upload this data: https://journals.plos.org/plosbiology/s/figures#loc-blot-and-gel-reporting-requirements

*Protocols deposition*

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for your submission to our journal. We hope that our editorial process has been constructive thus far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Christian

Christian Schnell, PhD

Senior Editor

PLOS Biology

cschnell@plos.org

----------------------------------------------------------------

REVIEWS:

Reviewer #1: Review of PBIOLOGY-D-24-01303R1, "Fast-spiking interneuron detonation drives high-fidelity inhibition in the olfactory bulb." This study by Burton and Colleagues uses whole-cell patch-clamp physiology and some immunohistochemistry to identify the source of temporally fast and precise inhibition of olfactory bulb output neurons. The study is noted for its extremely detailed and careful electrophysiology, which appears to resolve the conflict between previously observed M/T cell synchrony and the temporally distributed nature of inhibition they receive from granule cells. The experiments are technically demanding, and the results are convincing. The attention to detail and thoroughness are commendable; however, this is also somewhat of a weakness of the manuscript. The details and figures are rather dense, and, in several places, the overall message becomes obscured. This does not dampen my enthusiasm for the study. Still, I recommend that the authors revise their text to make it more accessible for general biologists (PLOS Biology) and not only those with a deep understanding of electrophysiological methods. Overall, this is a strong study with a remarkably detailed investigation. The authors should be able to address each of the points below without the need for further experimentation.

1) A circuit schematic to accompany the introduction would be helpful and serve as a touchstone for the rest of the study. As a related note, the schematics used in Figure 1S-U are helpful and reappear throughout the study, but a legend or description of what the shapes, colors, and lines represent would be helpful.

2) In Figure 1 part P/V, why are the voltage recordings from the MT cells so much more variable than those from the FSI or RSIs? Is this meaningful in some way? Can the axes be scaled similarly to the pannels on the left?

3) Line 202: the conceptual jump to cortex/hippocampus is abrupt and needs more of a transition. The authors are encouraged to explain why looking at cortical/hippocampal basket cells is analogous to looking at FSIs in the bulb. The following sentence approaches to explain the connection but is insufficient to establish the concept. "To investigate whether the evidence of perisomatic MTC innervation by single PV+ FSIs presented above collectively manifests in similar canonical patterns of basket-like innervation," Why would one expect to see patterns in the OB similar to the cortex/hippocampus? Is there previous evidence to suggest similar structures to the cortex/ hippocampus exist in the OB?

4) Line 344: typo "enhance"

5) Line 281: "Strikingly, our subtraction procedure indeed isolated clear recurrent IPSCs strongly timelocked to FSI detonation latencies (Fig 3E)." Please explain in sufficient detail what the "subtraction procedure" involves.

6) While the methods section provides sufficient detail, the results section should have some more text on the optogenetic experimental design (i.e. what mice and how was stimulation achieved).

7) The legend of Figure 7 does not patch part A. The legend states MTC-FSI pair, while the panel A label is "sTC-FSI." This type of nomenclature mismatch comes up in several of the figures. While I understand the authors are using precise descriptors of TC cell types (dTC, mTC, sTC), the distinctions are confusing when the results are generalized to MTCs. Unless there are differences found between the TC subtypes with respect to fast inhibition, I recommend simplifying the nomenclature.

Overall, this is an excellent study and will be of interest to a wide range of systems neurobiologists.

Reviewer #2: This work systemically characterized the interneurons particularly the fast spiking (FSIs) type expressing parvalbumin (PV) in the external plexiform layer (EPL) of the olfactory bulb and their functional impact on output neurons with anatomical and physiological approaches. The main conclusion is that the EPL-FSIs powerfully regulate OB output via their perisomatic synapses with OB output neurons MTCs. Although morphology and physiology of the PV-expressing OB EPL interneurons were reported by a several studies, this study revealed new features of these inhibitory INs including the noncanonical perisomatic detonation, advanced our understanding their functional roles in mediating fast lateral inhibition, is especially of interest to the field of olfactory neuroscience. The manuscript is well organized with most conclusions appropriately drawn based on evidence from well-designed experiments. The Methodology has details sufficient to allow experimental reproducibility. The reviewer has only a couple concerns as listed below.

1. When the authors describe or discuss inhibition in the olfactory bulb, only GCs and EPL INs were considered, totally ignoring the inhibitory glomerular neurons. Anatomical evidence shows comparable or higher number of INs in the glomerular layer than that of GCs (Parrish-Aungst et al, 2007). The functional impact of glomerular INs on OB output was also consistently reported, especially in the past decade or so. The glomerular short axon cells are also supposed to contribute to lateral inhibition but was not mentioned in this manuscript. This treatment of the literature seems to be unfair and needs correction.

2. While the authors tried to emphasize perisomatic inhibition involving somata, proximal apical dendrites and axon hillocks, synaptic interactions between EPL FSIs and lateral dendrites of MTCs cannot be completely excluded, especially for lateral inhibition. Thus, this may also need to be discussed in the text.

3. Minor: cited references need to be verified. At least the following three look incomplete:

Line 1208: Marín O (2012) Interneuron dysfunction in psychiatric disorders. 13:107-120.

Line 1209-1210: Markram H, Toledo-Rodriguez M, Wang Y, Gupta A, Silberberg G, Wu C (2004) Interneurons of the neocortical inhibitory system. 5:793-807.

Line 1271: Silver RA (2010) Neuronal arithmetic. 11:474-489.

Reviewer #3: Effective decision making necessitates efficient processing of sensory information at behaviorally relevant time scales. Numerous studies in the rodent olfactory system over the years have conclusively shown the time scale for such processing to be on the order of 100-200 ms. In this period, recurrent circuit computations with the olfactory bulb (OB) are thought to play an important role. However, paradoxically, most of the electrophysiological studies to date have provided evidence for recurrent inhibition to be very delayed, very long lasting and low fidelity. This current work provides evidence for a form of inhibition (short latency, low jitter and high probability) that could play an essential role in bridging the gap between these two phenomena. The discovery and characterization of a population of interneurons that mediates rapid, reliable and significant lateral (and auto) inhibition of principal cells in the OB is highly significant. The experiments are extensive and extremely well done. The discovery is likely to be foundational for olfactory neuroscience. There is very little to quibble. Below are some specific comments that might be helpful.

Introduction, Line 40: To be fair here, the authors may want to acknowledge all the hard work on glomerular layer interneurons by people like Shipley.

Line 76: The phrasing feels clunky - just say "wild-type mice" or such?

Line 79: While the general idea of 2 big classes seems reasonable, in reality, I suspect that the number of classes is a bit dependent on hyperparameters used in any classification algorithm.

Line 88 and other places further down: Instead of "Magnification", use some phrase such as "Magnified region"

Line 135: Are the authors able to offer any information on differences in the physical location of FSIs connected to TCs vs MCs?

Similarly, the authors show that the connection probability and strength did not differ between FSIs and MCs vs. tufted cells, did they observe any fast lateral inhibition between the two populations?

There are many instances of "Error! Reference source not found" - please fix

In the Discussion section, I wonder if the authors could discuss or speculate on what makes the fast (FSI mediated) and slow lateral inhibition to be mutually exclusive. Were there any specific features (~ distance, location, connected to same glomerulus or different glomeruli) that determine the type of lateral inhibition between MTC pairs

10.1371/journal.pbio.3002660.r003
Author response to Decision Letter 1
Submission Version2
10 Jul 2024

Attachment Submitted filename: Burton Malyshko Urban 2024 response to reviewers.pdf

10.1371/journal.pbio.3002660.r004
Decision Letter 2
Schnell, PhD Christian Senior Editor
© 2024 Christian Schnell, PhD
2024
Christian Schnell, PhD
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version2
17 Jul 2024

Dear Dr Burton,

Thank you for your patience while we considered your revised manuscript "Fast-spiking interneuron detonation drives high-fidelity inhibition in the olfactory bulb" for publication as a Research Article at PLOS Biology. This revised version of your manuscript has been evaluated by the PLOS Biology editors and the Academic Editor.

Based on our Academic Editor's assessment of your revision, we are likely to accept this manuscript for publication, provided you satisfactorily address the following data and other policy-related requests:

* Please move the S1 Figure to the main part, it could even be the first main figure as the current Figure 1 is already very dense. Reviewer 1 makes a valid case about accessibility and we think that having the clear and easily understandable scheme will clarify the context.

* DATA POLICY:

You may be aware of the PLOS Data Policy, which requires that all data be made available without restriction: http://journals.plos.org/plosbiology/s/data-availability. For more information, please also see this editorial: http://dx.doi.org/10.1371/journal.pbio.1001797

Note that we do not require all raw data. Rather, we ask that all individual quantitative observations that underlie the data summarized in the figures and results of your paper be made available in one of the following forms:

1) Supplementary files (e.g., excel). Please ensure that all data files are uploaded as 'Supporting Information' and are invariably referred to (in the manuscript, figure legends, and the Description field when uploading your files) using the following format verbatim: S1 Data, S2 Data, etc. Multiple panels of a single or even several figures can be included as multiple sheets in one excel file that is saved using exactly the following convention: S1_Data.xlsx (using an underscore).

2) Deposition in a publicly available repository. Please also provide the accession code or a reviewer link so that we may view your data before publication.

Regardless of the method selected, please ensure that you provide the individual numerical values that underlie the summary data displayed in the following figure panels as they are essential for readers to assess your analysis and to reproduce it: 1NSTU, 2C, 3G, 5G, 6G and similar panels in the supplementary information.

NOTE: the numerical data provided should include all replicates AND the way in which the plotted mean and errors were derived (it should not present only the mean/average values).

Please also ensure that figure legends in your manuscript include information on where the underlying data can be found, and ensure your supplemental data file/s has a legend.

Please ensure that your Data Statement in the submission system accurately describes where your data can be found.

* CODE POLICY

Per journal policy, if you have generated any custom code during the course of this investigation, please make it available without restrictions. Please ensure that the code is sufficiently well documented and reusable, and that your Data Statement in the Editorial Manager submission system accurately describes where your code can be found.

Please note that we cannot accept sole deposition of code in GitHub, as this could be changed after publication. However, you can archive this version of your publicly available GitHub code to Zenodo. Once you do this, it will generate a DOI number, which you will need to provide in the Data Accessibility Statement (you are welcome to also provide the GitHub access information). See the process for doing this here: https://docs.github.com/en/repositories/archiving-a-github-repository/referencing-and-citing-content

* Please note that per journal policy, we do not allow the mention of "data not shown", "personal communication", "manuscript in preparation" or other references to data that is not publicly available or contained within this manuscript. Please either remove mention of these data or provide figures presenting the results and the data underlying the figure(s).

As you address these items, please take this last chance to review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the cover letter that accompanies your revised manuscript.

We expect to receive your revised manuscript within two weeks.

To submit your revision, please go to https://www.editorialmanager.com/pbiology/ and log in as an Author. Click the link labelled 'Submissions Needing Revision' to find your submission record. Your revised submission must include the following:

- a cover letter that should detail your responses to any editorial requests, if applicable, and whether changes have been made to the reference list

- a Response to Reviewers file that provides a detailed response to the reviewers' comments (if applicable, if not applicable please do not delete your existing 'Response to Reviewers' file.)

- a track-changes file indicating any changes that you have made to the manuscript.

NOTE: If Supporting Information files are included with your article, note that these are not copyedited and will be published as they are submitted. Please ensure that these files are legible and of high quality (at least 300 dpi) in an easily accessible file format. For this reason, please be aware that any references listed in an SI file will not be indexed. For more information, see our Supporting Information guidelines:

https://journals.plos.org/plosbiology/s/supporting-information

*Published Peer Review History*

Please note that you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. Please see here for more details:

https://blogs.plos.org/plos/2019/05/plos-journals-now-open-for-published-peer-review/

*Press*

Should you, your institution's press office or the journal office choose to press release your paper, please ensure you have opted out of Early Article Posting on the submission form. We ask that you notify us as soon as possible if you or your institution is planning to press release the article.

*Protocols deposition*

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please do not hesitate to contact me should you have any questions.

Sincerely,

Christian

Christian Schnell, PhD

Senior Editor

cschnell@plos.org

PLOS Biology

10.1371/journal.pbio.3002660.r005
Author response to Decision Letter 2
Submission Version3
19 Jul 2024

Attachment Submitted filename: Burton Malyshko Urban 2024 2nd revision reviewer response.pdf

10.1371/journal.pbio.3002660.r006
Decision Letter 3
Schnell, PhD Christian Senior Editor
© 2024 Christian Schnell, PhD
2024
Christian Schnell, PhD
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version3
26 Jul 2024

Dear Dr Burton,

Thank you for the submission of your revised Research Article "Fast-spiking interneuron detonation drives high-fidelity inhibition in the olfactory bulb" for publication in PLOS Biology. On behalf of my colleagues and the Academic Editor, Izumi Fukunaga, I am pleased to say that we can in principle accept your manuscript for publication, provided you address any remaining formatting and reporting issues. These will be detailed in an email you should receive within 2-3 business days from our colleagues in the journal operations team; no action is required from you until then. Please note that we will not be able to formally accept your manuscript and schedule it for publication until you have completed any requested changes.

Please take a minute to log into Editorial Manager at http://www.editorialmanager.com/pbiology/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with biologypress@plos.org. If you have previously opted in to the early version process, we ask that you notify us immediately of any press plans so that we may opt out on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for choosing PLOS Biology for publication and supporting Open Access publishing. We look forward to publishing your study. 

Sincerely, 

Christian

Christian Schnell, PhD

Senior Editor

PLOS Biology

cschnell@plos.org
==== Refs
References

1 Hu H , Gan J , Jonas P . Interneurons. Fast-spiking, parvalbumin+ GABAergic interneurons: from cellular design to microcircuit function. Science. 2014;345 :1255263. doi: 10.1126/science.1255263 25082707
2 Tiesinga P , Fellous J- M , Sejnowski TJ . Regulation of spike timing in visual cortical circuits. Nat Rev Neurosci Nature Publishing Group.; 2008;9 :97–107. doi: 10.1038/nrn2315 18200026
3 Silver RA . Neuronal arithmetic. Nat Rev Neurosci. 2010;11 :474–489. doi: 10.1038/nrn2864 20531421
4 Marín O. Interneuron dysfunction in psychiatric disorders. Nat Rev Neurosci. 2012;13 :107–120. doi: 10.1038/nrn3155 22251963
5 Burton SD . Inhibitory circuits of the mammalian main olfactory bulb. J Neurophysiol. 2017;118 :2034–2051. doi: 10.1152/jn.00109.2017 28724776
6 Nagayama S , Homma R , Imamura F . Neuronal organization of olfactory bulb circuits. Front Neural Circuits. 2014;8 :98. doi: 10.3389/fncir.2014.00098 25232305
7 Lepousez G , Lledo P-M . Odor discrimination requires proper olfactory fast oscillations in awake mice. Neuron. 2013;80 :1010–1024. doi: 10.1016/j.neuron.2013.07.025 24139818
8 Gödde K , Gschwend O , Puchkov D , Pfeffer CK , Carleton A , Jentsch TJ . Disruption of Kcc2-dependent inhibition of olfactory bulb output neurons suggests its importance in odour discrimination. Nat Commun. 2016;7 :12043. doi: 10.1038/ncomms12043 27389623
9 Fukunaga I , Berning M , Kollo M , Schmaltz A , Schaefer AT . Two distinct channels of olfactory bulb output. Neuron. 2012;75 :320–329. doi: 10.1016/j.neuron.2012.05.017 22841316
10 Parrish-Aungst S , Shipley MT , Erdelyi F , Szabo G , Puche AC . Quantitative analysis of neuronal diversity in the mouse olfactory bulb. J Comp Neurol. 2007;501 :825–836. doi: 10.1002/cne.21205 17311323
11 Wachowiak M , Shipley MT . Coding and synaptic processing of sensory information in the glomerular layer of the olfactory bulb. Semin Cell Dev Biol. 2006;17 :411–423. doi: 10.1016/j.semcdb.2006.04.007 16765614
12 Cleland TA . Construction of odor representations by olfactory bulb microcircuits. Prog Brain Res Elsevier. 2014;208 :177–203. doi: 10.1016/B978-0-444-63350-7.00007-3 24767483
13 Kay LM . Circuit oscillations in odor perception and memory. Prog Brain Res Elsevier. 2014;208 :223–251. doi: 10.1016/B978-0-444-63350-7.00009-7 24767485
14 Rall W , Shepherd GM , Reese TS , Brightman MW . Dendrodendritic synaptic pathway for inhibition in the olfactory bulb. Exp Neurol. 1966;14 :44–56. doi: 10.1016/0014-4886(66)90023-9 5900523
15 Rall W , Shepherd GM . Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. J Neurophysiol. 1968;31 :884–915. doi: 10.1152/jn.1968.31.6.884 5710539
16 Isaacson JS . Mechanisms governing dendritic gamma-aminobutyric acid (GABA) release in the rat olfactory bulb. Proc Natl Acad Sci U S A. 2001;98 :337–342. doi: 10.1073/pnas.98.1.337 11120892
17 Kato HK , Gillet SN , Peters AJ , Isaacson JS , Komiyama T . Parvalbumin-expressing interneurons linearly control olfactory bulb output. Neuron. 2013;80 :1218–1231. doi: 10.1016/j.neuron.2013.08.036 24239124
18 Pressler RT , Strowbridge BW . Direct Recording of Dendrodendritic Excitation in the Olfactory Bulb: Divergent Properties of Local and External Glutamatergic Inputs Govern Synaptic Integration in Granule Cells. J Neurosci. 2017;37 :11774–11788. doi: 10.1523/JNEUROSCI.2033-17.2017 29066560
19 Schoppa NE , Urban NN . Dendritic processing within olfactory bulb circuits. Trends Neurosci. 2003;26 :501–506. doi: 10.1016/S0166-2236(03)00228-5 12948662
20 Galán RF , Fourcaud-Trocmé N , Ermentrout GB , Urban NN . Correlation-induced synchronization of oscillations in olfactory bulb neurons. J Neurosci. 2006;26 :3646–3655. doi: 10.1523/JNEUROSCI.4605-05.2006 16597718
21 Kapoor V , Urban NN . Glomerulus-Specific, Long-Latency Activity in the Olfactory Bulb Granule Cell Network. J Neurosci. 2006;26 :11709–11719. doi: 10.1523/JNEUROSCI.3371-06.2006 17093092
22 Huang L , Garcia I , Jen H- I , Arenkiel BR . Reciprocal connectivity between mitral cells and external plexiform layer interneurons in the mouse olfactory bulb. Front Neural Circuits. 2013;7 :32. doi: 10.3389/fncir.2013.00032 23459611
23 Wang D , Wu J , Liu P , Li X , Li J , He M , et al . VIP interneurons regulate olfactory bulb output and contribute to odor detection and discrimination. Cell Rep. 2022;38 :110383. doi: 10.1016/j.celrep.2022.110383 35172159
24 Liu G , Froudarakis E , Patel JM , Kochukov MY , Pekarek B , Hunt PJ , et al . Target specific functions of EPL interneurons in olfactory circuits. Nat Commun. Springer US; 2019;10 :1–14. doi: 10.1038/s41467-019-11354-y 30602773
25 Viollet C , Simon A , Tolle V , Labarthe A , Grouselle D , Loe-Mie Y , et al . Somatostatin-IRES-Cre Mice: Between Knockout and Wild-Type? Front Endocrinol (Lausanne). 2017;8 :131. doi: 10.3389/fendo.2017.00131 28674519
26 Joye DAM , Rohr KE , Keller D , Inda T , Telega A , Pancholi H , et al . Reduced VIP Expression Affects Circadian Clock Function in VIP-IRES-CRE Mice (JAX 010908). J Biol Rhythms. 2020;35 :340–352. doi: 10.1177/0748730420925573 32460660
27 Peng J , Long B , Yuan J , Peng X , Ni H , Li X , et al . A Quantitative Analysis of the Distribution of CRH Neurons in Whole Mouse Brain. Front Neuroanat. 2017;11 :63. doi: 10.3389/fnana.2017.00063 28790896
28 Wang Y , Hu P , Shan Q , Huang C , Huang Z , Chen P , et al . Single-cell morphological characterization of CRH neurons throughout the whole mouse brain. BMC Biol. 2021;19 :47. doi: 10.1186/s12915-021-00973-x 33722214
29 Lepousez G , Csaba Z , Bernard V , Loudes C , Videau C , Lacombe J , et al . Somatostatin interneurons delineate the inner part of the external plexiform layer in the mouse main olfactory bulb. J Comp Neurol. 2010;518 :1976–1994. doi: 10.1002/cne.22317 20394054
30 Crespo C , Liberia T , Blasco-Ibáñez JM , Nácher J , Varea E . The circuits of the olfactory bulb. The exception as a rule. Anat Rec (Hoboken). 2013;296 :1401–1412. doi: 10.1002/ar.22732 23907743
31 Pignatelli A , Ackman JB , Vigetti D , Beltrami AP , Zucchini S , Belluzzi O . A potential reservoir of immature dopaminergic replacement neurons in the adult mammalian olfactory bulb. Pflugers Arch. 2009;457 :899–915. doi: 10.1007/s00424-008-0535-0 19011893
32 Liberia T , Blasco-Ibáñez JM , Nácher J , Varea E , Zwafink V , Crespo C . Characterization of a population of tyrosine hydroxylase-containing interneurons in the external plexiform layer of the rat olfactory bulb. Neuroscience. 2012;217 :140–153. doi: 10.1016/j.neuroscience.2012.05.007 22583798
33 Kosaka T , Pignatelli A , Kosaka K . Heterogeneity of tyrosine hydroxylase expressing neurons in the main olfactory bulb of the mouse. Neurosci Res. 2020;157 :15–33. doi: 10.1016/j.neures.2019.10.004 31629793
34 Igarashi KM , Ieki N , An M , Yamaguchi Y , Nagayama S , Kobayakawa K , et al . Parallel mitral and tufted cell pathways route distinct odor information to different targets in the olfactory cortex. J Neurosci. 2012;32 :7970–7985. doi: 10.1523/JNEUROSCI.0154-12.2012 22674272
35 Geramita MA , Burton SD , Urban NN . Distinct lateral inhibitory circuits drive parallel processing of sensory information in the mammalian olfactory bulb. Elife. 2016;5 . doi: 10.7554/eLife.16039 27351103
36 Chae H , Banerjee A , Dussauze M , Albeanu DF . Long-range functional loops in the mouse olfactory system and their roles in computing odor identity. Neuron. 2022;110 :3970–3985.e7. doi: 10.1016/j.neuron.2022.09.005 36174573
37 Ona-Jodar T , Lage-Rupprecht V , Abraham NM , Rose CR , Egger V . Local Postsynaptic Signaling on Slow Time Scales in Reciprocal Olfactory Bulb Granule Cell Spines Matches Asynchronous Release. Front Synaptic Neurosci. 2020;12 :551691. doi: 10.3389/fnsyn.2020.551691 33304264
38 Hefft S , Jonas P . Asynchronous GABA release generates long-lasting inhibition at a hippocampal interneuron-principal neuron synapse. Nat Neurosci. 2005;8 :1319–1328. doi: 10.1038/nn1542 16158066
39 Freund TF , Katona I . Perisomatic inhibition. Neuron. 2007;56 :33–42. doi: 10.1016/j.neuron.2007.09.012 17920013
40 Toida K , Kosaka K , Heizmann CW , Kosaka T . Synaptic contacts between mitral/tufted cells and GABAergic neurons containing calcium-binding protein parvalbumin in the rat olfactory bulb, with special reference to reciprocal synapses between them. Brain Res. 1994;650 :347–352. doi: 10.1016/0006-8993(94)91804-x 7953704
41 Toida K , Kosaka K , Heizmann CW , Kosaka T . Electron microscopic serial-sectioning/reconstruction study of parvalbumin-containing neurons in the external plexiform layer of the rat olfactory bulb. Neuroscience. 1996;72 :449–466. doi: 10.1016/0306-4522(95)00521-8 8737415
42 Crespo C , Blasco-Ibáñez JM , Marqués-Marí AI , Alonso JR , Briñón JG , Martínez-Guijarro FJ . Vasoactive intestinal polypeptide-containing elements in the olfactory bulb of the hedgehog (Erinaceus europaeus). J Chem Neuroanat. 2002;24 :49–63. doi: 10.1016/s0891-0618(02)00018-2 12084411
43 Rall W. Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. J Neurophysiol. 1967;30 :1138–1168. doi: 10.1152/jn.1967.30.5.1138 6055351
44 Sommeijer J- P , Levelt CN . Synaptotagmin-2 is a reliable marker for parvalbumin positive inhibitory boutons in the mouse visual cortex. PLoS ONE. Public Library of Science; 2012;7 :e35323. doi: 10.1371/journal.pone.0035323 22539967
45 Vuong CK , Wei W , Lee J- A , Lin C-H , Damianov A , la Torre-Ubieta de L , et al . Rbfox1 Regulates Synaptic Transmission through the Inhibitory Neuron-Specific vSNARE Vamp1. Neuron. 2018;98 :127–141.e7. doi: 10.1016/j.neuron.2018.03.008 29621484
46 Trimble WS , Gray TS , Elferink LA , Wilson MC , Scheller RH . Distinct patterns of expression of two VAMP genes within the rat brain. J Neurosci. 1990;10 :1380–1387. doi: 10.1523/JNEUROSCI.10-04-01380.1990 2329380
47 Pang ZP , Melicoff E , Padgett D , Liu Y , Teich AF , Dickey BF , et al . Synaptotagmin-2 is essential for survival and contributes to Ca2+ triggering of neurotransmitter release in central and neuromuscular synapses. J Neurosci. 2006;26 :13493–13504. doi: 10.1523/JNEUROSCI.3519-06.2006 17192432
48 Kosaka T , Kosaka K . Heterogeneity of parvalbumin-containing neurons in the mouse main olfactory bulb, with special reference to short-axon cells and betaIV-spectrin positive dendritic segments. Neurosci Res. 2008;60 :56–72. doi: 10.1016/j.neures.2007.09.008 17976845
49 Compans B , Burrone J . Chandelier cells shine a light on the formation of GABAergic synapses. Curr Opin Neurobiol. 2023;80 :102697. doi: 10.1016/j.conb.2023.102697 36907075
50 Nigro MJ , Kirikae H , Kjelsberg K , Nair RR , Witter MP . Not All That Is Gold Glitters: PV-IRES-Cre Mouse Line Shows Low Efficiency of Labeling of Parvalbumin Interneurons in the Perirhinal Cortex. Front Neural Circuits. 2021;15 :781928. doi: 10.3389/fncir.2021.781928 34819840
51 Egger V , Kuner T . Olfactory bulb granule cells: specialized to link coactive glomerular columns for percept generation and discrimination of odors. Cell Tissue Res. 2021;383 :495–506. doi: 10.1007/s00441-020-03402-7 33404844
52 Pressler RT , Strowbridge BW . Activation of Granule Cell Interneurons by Two Divergent Local Circuit Pathways in the Rat Olfactory Bulb. J Neurosci. 2020;40 :9701–9714. doi: 10.1523/JNEUROSCI.0989-20.2020 33234611
53 Mueller M , Egger V . Dendritic integration in olfactory bulb granule cells upon simultaneous multispine activation: Low thresholds for nonlocal spiking activity. PLoS Biol. 2020;18 :e3000873. doi: 10.1371/journal.pbio.3000873 32966273
54 De Saint Jan D , Hirnet D , Westbrook GL , Charpak S . External tufted cells drive the output of olfactory bulb glomeruli. J Neurosci. 2009;29 :2043–2052. doi: 10.1523/JNEUROSCI.5317-08.2009 19228958
55 McNaughton BL , Morris RGM . Hippocampal synaptic enhancement and information storage within a distributed memory system. Trends Neurosci. 1987;10 :408–415. doi: 10.1002/hipo.450030204 8353599
56 Egger V , Urban NN . Dynamic connectivity in the mitral cell-granule cell microcircuit. Semin Cell Dev Biol. 2006;17 :424–432. doi: 10.1016/j.semcdb.2006.04.006 16889994
57 Aghvami SS , Kubota Y , Egger V . Anatomical and Functional Connectivity at the Dendrodendritic Reciprocal Mitral Cell–Granule Cell Synapse: Impact on Recurrent and Lateral Inhibition. Front Neuroanat. 2022;16 :1–18. doi: 10.3389/fncir.2022.933201 35937203
58 Kashiwadani H , Sasaki YF , Uchida N , Mori K . Synchronized oscillatory discharges of mitral/tufted cells with different molecular receptive ranges in the rabbit olfactory bulb. J Neurophysiol. 1999;82 :1786–1792. doi: 10.1152/jn.1999.82.4.1786 10515968
59 McTavish TS , Migliore M , Shepherd GM , Hines ML . Mitral cell spike synchrony modulated by dendrodendritic synapse location. Front Comput Neurosci. 2012;6 :3. doi: 10.3389/fncom.2012.00003 22319487
60 McIntyre ABR , Cleland TA . Biophysical constraints on lateral inhibition in the olfactory bulb. J Neurophysiol. 2016;115 :2937–2949. doi: 10.1152/jn.00671.2015 27009162
61 Isaacson JS , Strowbridge BW . Olfactory reciprocal synapses: dendritic signaling in the CNS. Neuron. 1998;20 :749–761. doi: 10.1016/s0896-6273(00)81013-2 9581766
62 Urban NN , Sakmann B . Reciprocal intraglomerular excitation and intra- and interglomerular lateral inhibition between mouse olfactory bulb mitral cells. J Physiol (Lond). 2002;542 :355–367. doi: 10.1113/jphysiol.2001.013491 12122137
63 Burton SD , Urban NN . Cell and circuit origins of fast network oscillations in the mammalian main olfactory bulb. Elife. 2021;10 . doi: 10.7554/eLife.74213 34658333
64 Lage-Rupprecht V , Zhou L , Bianchini G , Aghvami SS , Mueller M , Rózsa B , et al . Presynaptic NMDARs cooperate with local spikes toward GABA release from the reciprocal olfactory bulb granule cell spine. Elife. 2020;9 . doi: 10.7554/eLife.63737 33252329
65 Hamilton KA , Parrish-Aungst S , Margolis FL , Erdelyi F , Szabo G , Puche AC . Sensory deafferentation transsynaptically alters neuronal GluR1 expression in the external plexiform layer of the adult mouse main olfactory bulb. Chem Senses. 2008;33 :201–210. doi: 10.1093/chemse/bjm079 18184638
66 Petralia RS , Wenthold RJ . Light and electron immunocytochemical localization of AMPA-selective glutamate receptors in the rat brain. J Comp Neurol. John Wiley & Sons, Ltd; 1992;318 :329–354. doi: 10.1002/cne.903180309 1374769
67 Giustetto M , Bovolin P , Fasolo A , Bonino M , Cantino D , Sassoe-Pognetto M . Glutamate receptors in the olfactory bulb synaptic circuitry: heterogeneity and synaptic localization of N-methyl-D-aspartate receptor subunit 1 and AMPA receptor subunit 1. Neuroscience. 1997;76 :787–798. doi: 10.1016/s0306-4522(96)00285-0 9135051
68 Petralia RS , Wang YX , Mayat E , Wenthold RJ . Glutamate receptor subunit 2-selective antibody shows a differential distribution of calcium-impermeable AMPA receptors among populations of neurons. J Comp Neurol. 1997;385 :456–476. doi: 10.1002/(sici)1096-9861(19970901)385:3&lt;456::aid-cne9&gt;3.0.co;2-2 9300771
69 Montague AA , Greer CA . Differential distribution of ionotropic glutamate receptor subunits in the rat olfactory bulb. J Comp Neurol. 1999;405 :233–246. doi: 10.1002/(sici)1096-9861(19990308)405:2&lt;233::aid-cne7&gt;3.0.co;2-a 10023812
70 Darcy DP , Isaacson JS . Calcium-permeable AMPA receptors mediate glutamatergic signaling in neural precursor cells of the postnatal olfactory bulb. J Neurophysiol. 2010;103 :1431–1437. doi: 10.1152/jn.00821.2009 20089820
71 Burton SD , Larocca G , Liu A , Cheetham CEJ , Urban NN . Olfactory Bulb Deep Short-Axon Cells Mediate Widespread Inhibition of Tufted Cell Apical Dendrites. J Neurosci. 2017;37 :1117–1138. doi: 10.1523/JNEUROSCI.2880-16.2016 28003347
72 Whitesell JD , Sorensen KA , Jarvie BC , Hentges ST , Schoppa NE . Interglomerular lateral inhibition targeted on external tufted cells in the olfactory bulb. J Neurosci. 2013;33 :1552–1563. doi: 10.1523/JNEUROSCI.3410-12.2013 23345229
73 Banerjee A , Marbach F , Anselmi F , Koh MS , Davis MB , Garcia da Silva P , et al . An Interglomerular Circuit Gates Glomerular Output and Implements Gain Control in the Mouse Olfactory Bulb. Neuron. 2015;87 :193–207. doi: 10.1016/j.neuron.2015.06.019 26139373
74 Vaaga CE , Yorgason JT , Williams JT , Westbrook GL . Presynaptic gain control by endogenous cotransmission of dopamine and GABA in the olfactory bulb. J Neurophysiol. 2017;117 :1163–1170. doi: 10.1152/jn.00694.2016 28031402
75 Schoppa NE . Synchronization of olfactory bulb mitral cells by precisely timed inhibitory inputs. Neuron. 2006;49 :271–283. doi: 10.1016/j.neuron.2005.11.038 16423700
76 Najac M , Sanz Diez A , Kumar A , Benito N , Charpak S , De Saint Jan D . Intraglomerular lateral inhibition promotes spike timing variability in principal neurons of the olfactory bulb. J Neurosci. 2015;35 :4319–4331. doi: 10.1523/JNEUROSCI.2181-14.2015 25762678
77 Smear M , Shusterman R , O’Connor R , Bozza T , Rinberg D . Perception of sniff phase in mouse olfaction. Nature. 2011;479 :397–400. doi: 10.1038/nature10521 21993623
78 Wachowiak M. All in a sniff: olfaction as a model for active sensing. Neuron. 2011;71 :962–973. doi: 10.1016/j.neuron.2011.08.030 21943596
79 Burton SD , Brown A , Eiting TP , Youngstrom IA , Rust TC , Schmuker M , et al . Mapping odorant sensitivities reveals a sparse but structured representation of olfactory chemical space by sensory input to the mouse olfactory bulb. Elife. 2022;11 . doi: 10.7554/eLife.80470 35861321
80 Yokoi M , Mori K , Nakanishi S . Refinement of odor molecule tuning by dendrodendritic synaptic inhibition in the olfactory bulb. Proc Natl Acad Sci U S A. 1995;92 :3371–3375. doi: 10.1073/pnas.92.8.3371 7724568
81 Tan J , Savigner A , Ma M , Luo M . Odor information processing by the olfactory bulb analyzed in gene-targeted mice. Neuron. 2010;65 :912–926. doi: 10.1016/j.neuron.2010.02.011 20346765
82 Van Gehuchten A , Martin I . Le bulbe olfactif chez quelques mammiferes. Cellule. 1891. p. 205–237.
83 Schneider SP , Macrides F . Laminar distributions of internuerons in the main olfactory bulb of the adult hamster. Brain Res Bull. 1978;3 :73–82. doi: 10.1016/0361-9230(78)90063-1 630423
84 López-Mascaraque L , De Carlos JA , Valverde F . Structure of the olfactory bulb of the hedgehog (Erinaceus europaeus): a Golgi study of the intrinsic organization of the superficial layers. J Comp Neurol. 1990;301 :243–261. doi: 10.1002/cne.903010208 2262591
85 Brunjes PC , Feldman S , Osterberg SK . The Pig Olfactory Brain: A Primer. Chem Senses. 2016;41 :415–425. doi: 10.1093/chemse/bjw016 26936231
86 Markram H , Toledo-Rodriguez M , Wang Y , Gupta A , Silberberg G , Wu C . Interneurons of the neocortical inhibitory system. Nat Rev Neurosci. 2004;5 :793–807. doi: 10.1038/nrn1519 15378039
87 Maccaferri G , Lacaille J- C . Interneuron Diversity series: Hippocampal interneuron classifications--making things as simple as possible, not simpler. Trends Neurosci. 2003;26 :564–571. doi: 10.1016/j.tins.2003.08.002 14522150
88 Kosaka T , Komada M , Kosaka K . Sodium channel cluster, betaIV-spectrin and ankyrinG positive “hot spots” on dendritic segments of parvalbumin-containing neurons and some other neurons in the mouse and rat main olfactory bulbs. Neurosci Res. 2008;62 :176–186. doi: 10.1016/j.neures.2008.08.002 18786578
89 Kosaka T , Kosaka K . Heterogeneity of calbindin-containing neurons in the mouse main olfactory bulb: I. General description. Neurosci Res. 2010;67 :275–292. doi: 10.1016/j.neures.2010.04.009 20406658
90 Eyre MD , Renzi M , Farrant M , Nusser Z . Setting the time course of inhibitory synaptic currents by mixing multiple GABA(A) receptor α subunit isoforms. J Neurosci. 2012;32 :5853–5867. doi: 10.1523/JNEUROSCI.6495-11.2012 22539847
91 Fritschy JM , Mohler H . GABAA-receptor heterogeneity in the adult rat brain: differential regional and cellular distribution of seven major subunits. J Comp Neurol. John Wiley & Sons, Ltd; 1995;359 :154–194. doi: 10.1002/cne.903590111 8557845
92 Pirker S , Schwarzer C , Wieselthaler A , Sieghart W , Sperk G . GABA(A) receptors: immunocytochemical distribution of 13 subunits in the adult rat brain. Neuroscience. 2000;101 :815–850. doi: 10.1016/s0306-4522(00)00442-5 11113332
93 Panzanelli P , Perazzini A- Z , Fritschy J- M , Sassoè-Pognetto M . Heterogeneity of gamma-aminobutyric acid type A receptors in mitral and tufted cells of the rat main olfactory bulb. J Comp Neurol. 2005;484 :121–131. doi: 10.1002/cne.20440 15717305
94 Lagier S , Panzanelli P , Russo RE , Nissant A , Bathellier B , Sassoè-Pognetto M , et al . GABAergic inhibition at dendrodendritic synapses tunes gamma oscillations in the olfactory bulb. Proc Natl Acad Sci U S A. 2007;104 :7259–7264. doi: 10.1073/pnas.0701846104 17428916
95 Miyamichi K , Shlomai-Fuchs Y , Shu M , Weissbourd BC , Luo L , Mizrahi A . Dissecting local circuits: parvalbumin interneurons underlie broad feedback control of olfactory bulb output. Neuron. 2013;80 :1232–1245. doi: 10.1016/j.neuron.2013.08.027 24239125
96 Bartel DL , Rela L , Hsieh L , Greer CA . Dendrodendritic synapses in the mouse olfactory bulb external plexiform layer. J Comp Neurol. 2015;523 :1145–1161. doi: 10.1002/cne.23714 25420934
97 Arenkiel BR , Hasegawa H , Yi JJ , Larsen RS , Wallace ML , Philpot BD , et al . Activity-induced remodeling of olfactory bulb microcircuits revealed by monosynaptic tracing. PLoS ONE. 2011;6 :e29423. doi: 10.1371/journal.pone.0029423 22216277
98 Garcia I , Quast KB , Huang L , Herman AM , Selever J , Deussing JM , et al . Local CRH signaling promotes synaptogenesis and circuit integration of adult-born neurons. Dev Cell. 2014;30 :645–659. doi: 10.1016/j.devcel.2014.07.001 25199688
99 Crespo C , Blasco-Ibáñez JM , Marqués-Marí AI , Martínez-Guijarro FJ . Parvalbumin-containing interneurons do not innervate granule cells in the olfactory bulb. Neuroreport. 2001;12 :2553–2556. doi: 10.1097/00001756-200108080-00052 11496147
100 Urban NN , Castro JB . Functional polarity in neurons: what can we learn from studying an exception? Curr Opin Neurobiol. 2010;20 :538–542. doi: 10.1016/j.conb.2010.07.005 20724138
101 Buhl EH , Cobb SR , Halasy K , Somogyi P . Properties of unitary IPSPs evoked by anatomically identified basket cells in the rat hippocampus. Eur J Neurosci. John Wiley & Sons, Ltd; 1995;7 :1989–2004. doi: 10.1111/j.1460-9568.1995.tb00721.x 8528474
102 Thomson AM , West DC , Hahn J , Deuchars J . Single axon IPSPs elicited in pyramidal cells by three classes of interneurones in slices of rat neocortex. J Physiol (Lond). 1996;496 (Pt 1 ):81–102. doi: 10.1113/jphysiol.1996.sp021667 8910198
103 Kosaka T , Kosaka K . Neuronal organization of the main olfactory bulb revisited. Anat Sci Int Springer Japan. 2016;91 :115–127. doi: 10.1007/s12565-015-0309-7 26514846
104 Molnár G , Oláh S , Komlósi G , Füle M , Szabadics J , Varga C , et al . Complex events initiated by individual spikes in the human cerebral cortex. PLoS Biol. 2008;6 :e222. doi: 10.1371/journal.pbio.0060222 18767905
105 Campanac E , Gasselin C , Baude A , Rama S , Ankri N , Debanne D . Enhanced intrinsic excitability in basket cells maintains excitatory-inhibitory balance in hippocampal circuits. Neuron. 2013;77 :712–722. doi: 10.1016/j.neuron.2012.12.020 23439123
106 Borst JG , Helmchen F , Sakmann B . Pre- and postsynaptic whole-cell recordings in the medial nucleus of the trapezoid body of the rat. J Physiol. (Lond). John Wiley & Sons, Ltd; 1995;489 (Pt 3 ):825–840. doi: 10.1113/jphysiol.1995.sp021095 8788946
107 Vandael D , Jonas P . Structure, biophysics, and circuit function of a “giant” cortical presynaptic terminal. Science. 2024;383 :eadg6757. doi: 10.1126/science.adg6757 38452088
108 Eccles JC , Llinaś R , Sasaki K . The excitatory synaptic action of climbing fibres on the Purkinje cells of the cerebellum. J Physiol. (Lond). John Wiley & Sons, Ltd; 1966;182 :268–296. doi: 10.1113/jphysiol.1966.sp007824 5944665
109 Strowbridge BW . Role of cortical feedback in regulating inhibitory microcircuits. Ann N Y Acad Sci. 2009;1170 :270–274. doi: 10.1111/j.1749-6632.2009.04018.x 19686146
110 Lindeman S , Fu X , Reinert JK , Fukunaga I . Value-related learning in the olfactory bulb occurs through pathway-dependent perisomatic inhibition of mitral cells. PLoS Biol. 2024;22 :e3002536. doi: 10.1371/journal.pbio.3002536 38427708
111 Arnson HA , Strowbridge BW . Spatial Structure of Synchronized Inhibition in the Olfactory Bulb. J Neurosci. 2017;37 :10468–10480. doi: 10.1523/JNEUROSCI.1004-17.2017 28947574
112 Burton SD , Ermentrout GB , Urban NN . Intrinsic heterogeneity in oscillatory dynamics limits correlation-induced neural synchronization. J Neurophysiol. 2012;108 :2115–2133. doi: 10.1152/jn.00362.2012 22815400
113 Li G , Cleland TA . A coupled-oscillator model of olfactory bulb gamma oscillations. PLoS Comput Biol. 2017;13 :e1005760. doi: 10.1371/journal.pcbi.1005760 29140973
114 Burton SD , Urban NN . Rapid Feedforward Inhibition and Asynchronous Excitation Regulate Granule Cell Activity in the Mammalian Main Olfactory Bulb. J Neurosci. 2015;35 :14103–14122. doi: 10.1523/JNEUROSCI.0746-15.2015 26490853
115 Li J , Ishii T , Feinstein P , Mombaerts P . Odorant receptor gene choice is reset by nuclear transfer from mouse olfactory sensory neurons. Nature. 2004;428 :393–399. doi: 10.1038/nature02433 15042081
116 Smear M , Resulaj A , Zhang J , Bozza T , Rinberg D . Multiple perceptible signals from a single olfactory glomerulus. Nat Neurosci. 2013;16 :1687–1691. doi: 10.1038/nn.3519 24056698
117 Arneodo EM , Penikis KB , Rabinowitz N , Licata A , Cichy A , Zhang J , et al . Stimulus dependent diversity and stereotypy in the output of an olfactory functional unit. Nat Commun. 2018;9 :1347. doi: 10.1038/s41467-018-03837-1 29632302
118 Haddad R , Lanjuin A , Madisen L , Zeng H , Murthy VN , Uchida N . Olfactory cortical neurons read out a relative time code in the olfactory bulb. Nat Neurosci. 2013;16 :949–957. doi: 10.1038/nn.3407 23685720
119 Hippenmeyer S , Vrieseling E , Sigrist M , Portmann T , Laengle C , Ladle DR , et al . A developmental switch in the response of DRG neurons to ETS transcription factor signaling. PLoS Biol. Public Library of Science. 2005;3 :e159. doi: 10.1371/journal.pbio.0030159 15836427
120 Madisen L , Zwingman TA , Sunkin SM , Oh SW , Zariwala HA , Gu H , et al . A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat Neurosci. 2010;13 :133–140. doi: 10.1038/nn.2467 20023653
121 Hooks BM , Lin JY , Guo C , Svoboda K . Dual-channel circuit mapping reveals sensorimotor convergence in the primary motor cortex. J Neurosci. 2015;35 :4418–4426. doi: 10.1523/JNEUROSCI.3741-14.2015 25762684
122 Dietz SB , Markopoulos F , Murthy VN . Postnatal development of dendrodendritic inhibition in the Mammalian olfactory bulb. Front Cell Neurosci. 2011;5 :10. doi: 10.3389/fncel.2011.00010 21738497
123 Yu Y , Burton SD , Tripathy SJ , Urban NN . Postnatal development attunes olfactory bulb mitral cells to high-frequency signaling. J Neurophysiol. 2015;114 :2830–2842. doi: 10.1152/jn.00315.2015 26354312
124 Maher BJ , McGinley MJ , Westbrook GL . Experience-dependent maturation of the glomerular microcircuit. Proc Natl Acad Sci U S A. 2009;106 :16865–16870. doi: 10.1073/pnas.0808946106 19805387
125 Kosaka K , Heizmann CW , Kosaka T . Calcium-binding protein parvalbumin-immunoreactive neurons in the rat olfactory bulb. 2. Postnatal development. Exp Brain Res. 1994;99 :205–213. doi: 10.1007/BF00239587 7925803
126 Batista-Brito R , Close J , Machold R , Fishell G . The distinct temporal origins of olfactory bulb interneuron subtypes. J Neurosci. 2008;28 :3966–3975. doi: 10.1523/JNEUROSCI.5625-07.2008 18400896
127 Garcia I , Bhullar PK , Tepe B , Ortiz-Guzman J , Huang L , Herman AM , et al . Local corticotropin releasing hormone (CRH) signals to its receptor CRHR1 during postnatal development of the mouse olfactory bulb. Brain Struct Funct. 2016;221 :1–20. doi: 10.1007/s00429-014-0888-4 25224546
128 Burton SD , Urban NN . Greater excitability and firing irregularity of tufted cells underlies distinct afferent-evoked activity of olfactory bulb mitral and tufted cells. J Physiol (Lond). 2014;592 :2097–2118. doi: 10.1113/jphysiol.2013.269886 24614745
129 Carlson GC , Shipley MT , Keller A . Long-lasting depolarizations in mitral cells of the rat olfactory bulb. J Neurosci. 2000;20 :2011–2021. doi: 10.1523/JNEUROSCI.20-05-02011.2000 10684902
130 Schoppa NE , Westbrook GL . Glomerulus-specific synchronization of mitral cells in the olfactory bulb. Neuron. 2001;31 :639–651. doi: 10.1016/s0896-6273(01)00389-0 11545722
131 Smith TC , Jahr CE . Self-inhibition of olfactory bulb neurons. Nat Neurosci. 2002;5 :760–766. doi: 10.1038/nn882 12089528
132 Ma J , Lowe G . Calcium permeable AMPA receptors and autoreceptors in external tufted cells of rat olfactory bulb. Neuroscience. 2007;144 :1094–1108. doi: 10.1016/j.neuroscience.2006.10.041 17156930
133 Zak JD , Whitesell JD , Schoppa NE . Metabotropic glutamate receptors promote disinhibition of olfactory bulb glomeruli that scales with input strength. J Neurophysiol. 2015;113 :1907–1920. doi: 10.1152/jn.00222.2014 25552635
134 Arshadi C , Günther U , Eddison M , Harrington KIS , Ferreira TA . SNT: a unifying toolbox for quantification of neuronal anatomy. Nat Methods. 2021;18 :374–377. doi: 10.1038/s41592-021-01105-7 33795878
135 Clements JD , Bekkers JM . Detection of spontaneous synaptic events with an optimally scaled template. Biophys J. 1997;73 :220–229. doi: 10.1016/S0006-3495(97)78062-7 9199786
