Abstract
Animals navigate the auditory world by recognizing complex sounds, from the rustle of a predator to the call of a potential mate. This ability depends in part on the octopus cells of the auditory brainstem, which respond to multiple frequencies that change over time, as occurs in natural stimuli. Unlike the average neuron, which integrates inputs over time on the order of tens of milliseconds, octopus cells must detect momentary coincidence of excitatory inputs from the cochlea during an ongoing sound on both the millisecond and submillisecond time scale. Here, we show that octopus cells receive inhibitory inputs on their dendrites that enhance opportunities for coincidence detection in the cell body, thereby allowing for responses both to rapid onsets at the beginning of a sound and to frequency modulations during the sound. This mechanism is crucial for the fundamental process of integrating the synchronized frequencies of natural auditory signals over time.
eLife assessment
This valuable work analyzes how specialized cells in the auditory cells, known as the octopus cells, can detect coincidences in their inputs at the submillisecond time scale. While previous work indicated that these cells receive no inhibitory inputs, the present study unambiguously demonstrates that these cells receive inhibitory glycinergic inputs. The physiologic impact of these inputs needs to be studied further. It remains incomplete at present but could be improved by addressing caveats related to similar sizes of excitatory postsynaptic potentials and spikes in the octopus neurons.
Introduction
Perception depends on the ability of neurons to encode discrete features of complex external stimuli. These computations are determined by the type of information received from the periphery, the nature and position of the synapses, and the biophysical properties of the target neuron. For instance, in the auditory system, spiral ganglion neurons (SGNs) encode sound information from hair cells in the cochlea and distribute it to specialized target neurons in the cochlear nucleus complex (CNC) that extract individual elements of the original sound, such as frequency, phase, intensity, and timing. The outcome of each dedicated computation continues to be processed through parallel ascending pathways and ultimately to the cortex, where the auditory features are reassembled to generate a percept. Thus, understanding how these first computations are made is a key step towards deciphering the basis of perception.
To generate accurate percepts of complex auditory stimuli, neurons must compute both what frequencies are present and when those frequencies occur across multiple time scales. For instance, overlapping sound stimuli, such as two competing speakers in a noisy room, are perceptually distinguished by correctly binding together frequencies with coherent onsets that then continue to change together over time1,2. Such computations require coincidence detection that can accurately encode co-occurring frequencies with submillisecond precision. Frequency information is communicated by SGNs, whose auditory nerve fibers (ANFs) project through the eighth nerve, bifurcate, and spread tonotopically to fill each division of the CNC (Fig. 1A). In addition, SGNs fall into physiologically distinct subtypes that are recruited at different intensities, thereby allowing sounds to be detected across a wide dynamic range and in the presence of background noise. Many target neurons receive SGN inputs from a limited range of frequencies and fire at the onset of the sound, effectively breaking it up into its frequency components. This presents a challenge for perception as auditory circuits must ultimately bind together co-occurring frequencies while also retaining information about their sequence to locate and recognize sounds.
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_fig1.tif/full/max/0/default.jpg)
Excitatory and inhibitory synapses to octopus cells form two domains.
(A) Illustration of spiral ganglion neuron (SGN) central axons branching within a parasagittal section of the mouse cochlear nucleus complex (CNC). SGN somas in the cochlea are tono-topically organized according to frequency. Axons remain organized throughout the ventral (VCN) and dorsal (DCN) divisions of the CNC. Octopus cells (inset) are found in the octopus cell area (OCA) of the VCN. (B) Excitatory SGN puncta labeled with a VGLUT1 antibody (left) and glycinergic puncta labeled with Glyt2Cre-dependent syp/tdT (Glyt2-syp/tdT; right) in a par-asagittal section of the CNC. The teardrop-shaped OCA is not devoid of inhibitory inputs, although less prominent than in the surrounding CNC. (C) A Thy1 sparsely labeled octopus cell with excitatory SGN (VGLUT1) and inhibitory (Glyt2-syp/tdT) puncta. Micrographs of 3µm confocal z-stacks show puncta on the medial surface of a soma (top) and a dendrite (bottom). (D) Representative reconstruction of excitatory SGN puncta labeled with Foxg1Cre-dependent syp/tdT (Foxg1-syp/tdT; blue) onto a Thy1+ octopus cell. (E) Representative reconstruction of inhibitory puncta labeled with Glyt2-syp/tdT (green) onto a Thy1 sparsely labeled octopus cell (white). (F) Puncta density for excitatory SGN (Foxg1-syp/tdT; black: 10.7 ± 3.0, n = 8 cells, 4 mice) and glycinergic puncta (Glyt2-syp/tdT; green: 4.2 ± 0.8, n = 8 cells, 3 mice) on octopus cell dendrites. Data are presented as mean ± SD. (G) Puncta density on somas for excitatory SGN (black: 13.3 ± 2.2, n = 8 cells, 4 mice) and glycinergic puncta (green: 1.8 ± 0.1, n = 8 cells, 3 mice) and the density along the length of dendrites. Data are presented as mean ± SEM. (H) Top: Illustration of an octopus cell and the ratio between excitatory SGN puncta and glycinergic puncta. Inset: Illustration of an octopus cell and the relative innervation densities of excitatory SGNs (blue) and inhibitory puncta (green).
In the mammalian auditory system, precise encoding of broadband timing information begins with the octopus cells of the CNC. Octopus cells are excitatory neurons that bind together co-occurring frequency information on a submillisecond timescale and send this information along one of the parallel ascending pathways in the auditory brainstem. Octopus cells are named for their large-diameter tentacle-like dendrites3,4, which are oriented unidirectionally across a tonotopic array of SGNs such that each neuron integrates inputs from a wide range of frequencies5–8. SGNs provide the major excitatory inputs onto octopus cells. Biophysically, octopus cells have low input resistances near rest (∼4MΩ), fast time constants (∼200µs), and large low-voltage-activated potassium (∼40nS at rest) and hyperpolarization activated (∼62nS at rest) conductances. Together these properties give these cells impressively narrow windows of coincidence detection on the order of 1 millisecond9–16. This combination of receiving SGN innervation across broad frequencies and their biophysical specializations establish octopus cells as spectrotemporal coincidence detectors that can reliably encode the timing of complex stimuli, such as the broadband transients found in speech and other natural sounds13,17,18. Fittingly, in vivo recordings from octopus cells demonstrate their ability to phase lock to broadband transients at rates up to 1kHz19–21. Moreover, computational models of octopus cells demonstrate that onset responses are governed by the cell’s biophysical specializations and are, in large part, the result of temporal summation of excitation22–28. The simplicity of its connectivity combined with the precision of its temporal computations makes the octopus cell an attractive model for understanding how specialized anatomical and electrophysiological properties contribute to neuronal computations.
Although the octopus cell’s integration of SGN inputs within a very narrow time frame enables canonical coincidence detection, such a model does not explain how other temporal features of sound stimuli are encoded. Indeed, octopus cells encode spectrotemporal sequences within their broadly-tuned response areas, like frequency modulated sounds, that likely require further circuit specializations29. Although threshold somatic depolarization can be sufficient to activate an octopus cell23, the vast majority of synapses are found on dendrites. Further, SGN inputs are organized tonotopically along octopus cell dendrites, with inputs from high frequency regions located more distal than those from low frequency regions. Dendritic morphology, passive cable properties, active resting membrane properties, and the spatial and temporal relationship between synaptic inputs can all impact excitatory post synaptic potential (EPSP) summation as excitation sweeps across the dendritic arbor and towards the soma. This raises the possibility that computations made in the dendrites influence the effective window of coincidence detection by the octopus cell. Such mechanisms could enable flexible processing that is adaptive to the dynamics of the environment without compromising the fidelity of high-fidelity coincidence computations.
Here, we sought to define the circuit mechanisms that allow octopus cells to act as coincidence detectors across time scales. We generated a comprehensive anatomical and physiological map of excitatory and synaptic inputs onto octopus cell somas and dendrites and examined how this circuit organization influences octopus cell activation. Through a combination of in vitro experiments and computational modeling, we show that the somatic summation of excitation is shaped by dendritic inhibition. Thus, octopus cells depend on compartmentalized computations that enable preservation of timing information both at the moment of stimulus onset and within an extended window for evidence accumulation, which is fundamental for the spectrotemporal integration of natural auditory stimuli.
Results
The balance of excitatory and inhibitory synapses is different in somatic and dendritic domains
To determine the wiring pattern that drives octopus cell computations, we generated a detailed map of excitatory and inhibitory synaptic inputs (Fig. 1). Overall, octopus cells receive abundant excitatory VGLUT1+ innervation from SGNs30,31 and sparse inhibitory innervation from glycinergic neurons, as visualized using the glycinergic Cre driver Glyt2Cre and the Ai34 synaptophysin-tdTomato (syp/tdT) fusion protein reporter (Fig. 1B). In addition, sparse inhibitory inputs nestle between SGN inputs, especially on octopus cell dendrites (Fig. 1C).
Quantification of the number and distribution of presynaptic puncta onto octopus cells revealed marked differences in the ratio of excitation and inhibition in the somatic and dendritic compartments. Since innervation patterns have never been systematically analyzed, we made three-dimensional reconstructions of 16 octopus cells and their excitatory SGN (n = 8 cells, 4 mice) and inhibitory (n = 8 cells, 3 mice) inputs. Octopus cells were visualized using a Thy1 reporter and presynaptic puncta were labeled with the syp/tdT reporter driven either by Foxg1Cre (Fig. 1D) or Glyt2Cre (Fig. 1E). Consistent with qualitative assessment, the density of SGN inputs was higher (10.7 ± 3.0 SGN puncta/100µm2) than that of inhibitory inputs (4.2 ± 0.8 puncta/100µm2, Fig. 1F). Moreover, the relative proportions of excitatory and inhibitory inputs differed in the soma and dendrites (Fig. 1G). On somas, SGNs provided dense innervation that continued on the proximal dendrite, then gradually declined with distance from the soma. By contrast, somas received very few inhibitory inputs. On dendrites, inhibitory puncta were evenly distributed. As a result, octopus cells have a strikingly different average ratio of excitatory and inhibitory puncta on the soma (7:1) and on the dendrite (5:2), suggesting that each compartment contributes differentially to the final computation made by the octopus cell (Fig. 1H).
The majority of excitatory synapses on octopus cells are from type Ia SGNs
Although uniformly glutamatergic, SGNs exhibit stereotyped physiological differences in response thresholds that could affect the nature of their inputs onto octopus cells and influence octopus cell coincidence detection32–35,35,36. It was recently discovered that there are three molecularly distinct SGN subtypes, referred to as Ia, Ib, and Ic SGNs, which correlate with previously shown physiological groups37–43 (Fig. 2A). Therefore, we further categorized excitatory inputs based on SGN subtype identity. These can be identified with the presence of Ntng1-dependent reporter expression44 in Ib and Ic SGNs (Ib/c) and its absence in Ia SGNs40,41,43, coupled with very low to undetectable levels of calretinin (CR-) in Ic SGNs, and moderate to high levels of calretinin (CR+, CR++) in Ib and Ia SGNs (Fig. 2B). Ntng1Cre-labeled Ib/c SGNs accounted for 60.1 ± 2.6% of the entire population, with 28.5 ± 12.2% Ib SGNs, 31.6% Ic SGNs, and 39.9 ± 2.6% Ia SGNs (Fig. 2C: n = 1599 neurons, 4 mice; mean ± SD). Further, proportions of SGN subtypes matched scRNA-seq estimates (Fig. 2C), indicating that this approach provides full coverage. SGN subtype identity was further confirmed by examining the spatial organization of SGN peripheral processes en route to the IHCs in the cochlea (Fig 2A, Supp. Fig. 1A-C).
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_fig2.tif/full/max/0/default.jpg)
Type Ia SGNs are the primary excitatory contributors to octopus cells.
(A) Ia (yellow), Ib (orange), and Ic (magenta) SGN axons innervate the CNC. SGNs have a continuum of properties organized along the pillar-modiolar axis of inner hair cells (IHCs) and the habenula. Ntng1-expressing Ib/c fibers are positioned on the modiolar side (closest to the ganglion). Strongly calretinin immunopositive Ia fibers are on the other side (closest to the pillar cells). This organization correlates with spontaneous rates (SR) and thresholds (thresh.) measured in vivo. Somas of all SGN subtypes are found at all tonotopic locations. (B) Calretinin (CR) immunolabeling distinguishes SGN subtypes. Ia/b somas label with high (CR++) and medium (CR+) levels of CR, respectively. Ic somas label with low to undetectable levels of CR (CR-). Ntng1Cre-mediated expression of tdT (Ntng1-tdT) labels Ib/c SGNs. (C) Ias (tdT-, CR++) make up 39.9 ± 2.6% of the SGN population. Ib/cs (tdT+) make up 60.1 ± 2.6% of the SGN population. Ibs (tdT+CR+) make up 28.5 ± 12.2% of the SGN population (n = 1599 neurons, 4 mice). Data are mean ± SD; individual points represent percent coverage per animal, lines connect measurements from the same animal. Dotted lines indicate percentages from 1: Petitpré et al. 2018, 2: Shrestha et al. 2018, and 3: Sun et al. 2018. (D) Reconstructed Ib/c puncta labeled with Ntng1Cre-dependent syp/tdT (Ntng1-syp/tdT; magenta) onto a Thy1+ octopus cell. (E) Puncta density for all SGNs (Foxg1-syp/tdT; black: data from Fig. 1F), Ias (Foxg1 -Ntng1; yellow: 6.6 ± 1.0), and Ib/cs (Ntng1-syp/tdT; magenta: 4.1 ± 1.0, n = 9 cells, 5 mice) along the total dendritic length. Ia density was calculated by subtracting Ib/c density from total SGN density; lines connect measurements from the same reconstruction. Data are mean ± SD. (F) Puncta density on somas for all SGNs (Foxg1-syp/tdT; black: data from Fig. 1E), Ias (Foxg1 -Ntng1; yellow: 8.4 ± 2.3), and Ib/cs (Ntng1-syp/tdT; magenta: 4.9 ± 1.2, n = 9 cells, 5 mice) and the density along the length of dendrites. Data are mean ± SEM. (G) Relative innervation densities of Ias (yellow), Ib/cs (magenta), and inhibitory puncta (green).
Within the ventral cochlear nucleus (VCN), where octopus cells reside, Ntng1Cre labeling was restricted to SGNs (Supp. Fig. 1D). In the VCN, expression of Ntng1-tdT and CR in SGN central axons was consistent with the moderate to undetectable levels of CR in Ntng1-tdT SGN somas in the periphery (Supp. Fig. 2E). Thus, Ntng1Cre-driven expression of syp/tdT is an appropriate tool for mapping subtype-specific connectivity onto octopus cells.
Reconstruction of Ntng1Cre-labeled Ib/c puncta (Fig. 2D) demonstrated that octopus cells are dominated by inputs from Ia SGN fibers, which are the fibers with the lowest response thresholds and highest rates of spontaneous activity. Octopus cell dendrites received 4.1 ± 1.0 puncta/100µm2 from Ib/c SGNs (Fig. 2E, magenta: n = 9 cells, 5 mice; mean ± SD), accounting for 38% of the total SGN density. Given that Ntng1-tdT+ cells account for 60.1% of the SGN population (Fig. 2C, magenta), Ib/c inputs were underrepresented on octopus cells. Octopus cells receive similarly low innervation from Ic inputs (Supp. Fig. 2G-I: n = 6 cells, 2 mice), as estimated from the degree of sparse labeling of Ic axons achieved by Myo15iCre reporter expression (Supp. Fig. 2A-F) and the expected proportion of Ic SGNs in the ganglion (Supp. Fig. 2F). By contrast, Ia SGNs, which comprise only ∼40% of the total population (Fig. 2C, yellow), accounted for 62 ± 9.7% of SGN synapses on octopus cells (Fig. 2E, yellow: 6.6 ± 1.0 puncta/100µm2). All three subtypes showed the same overall distribution from the soma to the distal dendrite (Fig. 2F). Together, excitatory and inhibitory puncta densities in the innervation maps indicate the average octopus cell receives ∼1035 SGN synapses (642 Ia SGN, 393 Ib/c SGN) and ∼354 inhibitory synapses. Additionally, the majority of synapses on the average octopus cell (83%) are found on dendrites, highlighting their critical role in the octopus cell computation.
Octopus cell reconstructions showed the same basic wiring patterns regardless of where each cell was positioned in the octopus cell area. The tonotopic position of all reconstructed octopus cell somas was estimated in 3D reconstructions aligned to a normalized CNC model of tonotopy45. Octopus cells had similar morphologies (Supp. Fig. 3E-G) and patterns of synaptic innervation (Supp. Fig. 3H-M) regardless of where they were positioned along the tonotopic axis. Thus, we have established a wiring diagram that finds low threshold, Ia SGN synapses to be the primary input to both the soma and dendrites of octopus cells. Additionally, the whole-neuron wiring diagram identifies a dendritic domain where inhibitory synapses are approximately equal in number to the Ib/c excitatory synapses from the periphery.
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_fig3.tif/full/max/0/default.jpg)
SGN subtype inputs to octopus cells do not differ in short term plasticity.
(A) Illustration of the experimental paradigm and representative EPSPs recorded during in vitro whole-cell current clamp recordings of octopus cells. SGN stimulation method included electrical stimulation or full-field, light-evoked activation of Foxg-ChR2 or Ntng1-ChR2 SGNs. TTL trigger pulses are shown in gray. (B) Paired pulse ratios for electrically stimulated SGNs (open circles: n = 5 cells, 3 mice), ChR2 stimulated SGNs (Foxg1-ChR2; black: n = 8 cells, 5 mice), and ChR2 stimulated Ib/c SGNs (Ntng1-ChR2; magenta: n = 7 cells, 6 mice) at three interstimulus intervals. With electrical stimulation, SGN inputs to octopus cells were stable and exhibited slight facilitation at 50 Hz (20ms interstimulus interval). ChR2 stimulation caused paired pulse depression not seen with electrical stimulation. Data are presented as mean ± SD. Each data point represents the average paired pulse ratio for a cell. p values from ANOVA and subsequent Tukey HSD test are reported for comparisons between methods of SGN activation (electrical and ChR2) and SGN subpopulation composition within method of activation (SGN-ChR2 and Ib/c-ChR2). Welch’s ANOVA was used for comparisons at 20ms interstimulus interval (50Hz) as data in this condition did not meet the homogeneity of variance assumption. (C) Pulse ratios at 50Hz for electrically stimulated SGNs with physiological 1.4mM Ca2+ ACSF (open circles: n = 5 cells, 3 mice), electrically stimulated SGNs with 2.4mM Ca2+ ACSF (grey: n = 3 cells, 2 mice) and ChR2 stimulated SGNs with physiological ACSF (Foxg1-ChR2; black: n = 8 cells, 5 mice). Data are presented as mean ± SD. p < 0.001 from ANOVA and subsequent Tukey HSD test for all comparisons between methods of SGN activation (electrical and ChR2). There were no statistically significant differences for all comparisons under 1.4mM and 2.4mM Ca2+ (p > 0.100, ANOVA).
SGN inputs to octopus cells facilitate at high stimulation frequencies
Whether or not an octopus cell responds to its inputs depends on when and how EPSPs travel to and then summate in the soma. To determine if SGN subtypes transmit information differently to their central targets, we performed in vitro whole-cell current clamp recordings of octopus cells (Fig. 3A) while using Channelrhodopsin-2 (ChR2) to stimulate either all SGNs (Foxg1-ChR2) or only Ib/c SGNs (Ntng1-ChR2). Trains of ChR2-evoked SGN stimulation, ranging from 5 to 50Hz, in both the total SGN population (Fig. 3B, black: n = 8 cells, 5 mice) and the Ib/c SGN population (Fig. 3B, magenta: n = 7 cells, 6 mice) exhibited no differences in paired-pulse plasticity at any frequency of stimulation (p > 0.35 at all interstimulus intervals, Tukey’s HSD), although the Ib/c population exhibited higher variability than the total SGN population (at 20ms: SD = 0.11, SD = 0.24, respectively).
ChR2-evoked synaptic responses are known to artificially undergo synaptic depression46,47. To determine if the paired pulse depression measured in ChR2-stimulated experiments was physiological (Fig. 3B), we used electrical stimulation to evoke EPSPs from SGNs. Electrically-evoked SGN EPSPs had higher paired-pulse ratios than ChR2-evoked EPSPs and were mildly facilitating at short (20ms) intervals (Fig. 3B-C, open circles: n = 5 cells, 3 mice), consistent with an octopus cell’s ability to respond reliably to click trains in vivo5,7,20,48. In contrast, previous results using electrical stimulation demonstrated short-term depression of SGN inputs to octopus cells49,50. However, these experiments were carried out in the presence of higher, non-physiological levels of extracellular calcium. We repeated paired pulse plasticity experiments with non-physiological calcium concentrations (2.4mM) and similarly found that electrically-evoked EP-SPs from SGNs resulted in short-term depression at 50Hz of electrical stimulation (Fig. 3C, grey: n = 3 cells, 2 mice), though not to the degree observed when using full-field, ChR2-evoked inputs.
Glycine evokes inhibitory post synaptic potentials that are occluded by a low input resistance
Given the increased density of inhibitory synapses on octopus cell dendrites, we considered the possibility that somatic and dendritic compartments contribute differently to the temporal computation made by octopus cells. A role for inhibition has never been incorporated into octopus cell models as previous efforts failed to reveal physiological evidence of functional inhibitory synapses onto octopus cells either in vitro12,51,52 or in vivo29. Similarly, we did not observe light-evoked (Glyt2-ChR2) inhibitory post synaptic potentials (IPSPs) in octopus cell somas during whole-cell current clamp recordings from P30-45 mice. Since inhibitory synapses are located primarily on octopus cell dendrites, we posited that their voltage spread to the soma is limited given the extremely low input resistance of octopus cell somas.
To decrease electrotonic isolation of the dendrites and increase input resistance, we pharmacologically blocked voltagegated potassium (Kv) and hyperpolarization-activated cyclic nucleotide-gated (HCN) channels using 100µM 4-Aminopyridine (4-AP) and 50µM ZD 7288 (ZD). This cocktail increased octopus cell membrane resistance (Fig. 4A). To isolate inhibition, 15µM 2,3-dioxo-6-nitro-7-sulfamoyl-benzo[f]quinoxaline (NBQX) was added to block AMPA receptor activation. Consistent with our hypothesis, the increase in input resistance unveiled light-evoked IPSPs in recordings from octopus cell somas (Fig. 4B). Bath application of 500nM strychnine (STN) fully abolished IPSPs (Fig. 4C), confirming functional glycinergic inhibitory synaptic transmission onto octopus cells.
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_fig4.tif/full/max/0/default.jpg)
Octopus cells receive glycinergic inhibitory post synaptic potentials.
(A) Voltage responses to a -200pA current injection. This representative neuron hyperpolarized 0.7mV (black) in control conditions. After bath application of 100µM 4-Aminopyridine (4-AP) and 50µM ZD 7288 (ZD), hyperpolarizing responses to the same -200pA current injection increased to 8.8mV at steady state (green). (B) Postsynaptic responses to ChR2 stimulation of glycinergic terminals (Glyt2-ChR2) with a 5Hz train (gray) of 1ms full-field blue light pulses before (black) and after bath application of 100µM 4-AP, 50µM ZD, and 15µM NBQX (green: n = 9 cells, 8 mice). Increased input resistance reveals inhibitory potentials that are difficult to detect. (C) Postsynaptic responses to Glyt2-ChR2 stimulation after bath application of 100µM 4-AP, 50µM ZD, and 15µM NBQX (green), with further sequential addition of 20µM picrotoxin (PTX, pink), 100µM cyclothiazide (CTZ, blue), and 500nM strychnine (STN, orange; n = 6 cells, 5 mice). (D) Change in membrane voltage in response to hyperpolarizing somatic current steps in a morphologically and biophysically realistic model of octopus cells before (black) and after removal of voltage-gated potassium (Kv) and hyperpolarization-activated cyclic nucleotide-gated (HCN) channels (blue). As in in vitro current-clamp recordings, removing Kv and HCN channels increased the magnitude of voltage responses (ΔVm) to hyperpolarizing current. (E) IPSPs measured at the soma of a modeled octopus cell before (black) and after removal of Kv and HCN channels (blue). As in in vitro current-clamp recordings, this allows for somatic IPSP detection. (F) IPSP magnitude in experimental data (green) and the model (blues) as a function of input resistance. In somatic measurements, IPSP size increases with input resistance. Modeled IPSPs are shown for two conductance levels (1nS, dark blue; 100nS light blue). (G) Fold change in magnitude of soma-measured IPSPs (blue) or dendrite-measured IPSCs at proximal (dark orange) and distal (light orange) dendritic locations after removal of Kv and HCN channels. Kv and HCN block increases the magnitude of soma-measured IPSPs. The size of dendritic IPSCs are not changed with Kv and HCN or synapse location.
To determine the types of glycinergic receptors contributing to IPSPs, we pharmacologically blocked subsets of glycine receptors (Fig. 4C). IPSPs were reduced upon addition of 20µM picrotoxin (PTX), which blocks homomeric glycine receptors53–55. Sequential addition of 100µM cyclothiazide (CTZ), which blocks α2-containing homomeric and heteromeric glycine receptors56,57, nearly abolished the remaining IPSPs, and responses were fully abolished with further application of 500nM STN. Collectively, these data demonstrate that glycinergic synaptic contacts onto octopus cell dendrites are functional. Additionally, glycine receptor subunit composition (Fig. 4C) implicates a role for both large conductance extrasynaptic β-subunit lacking homomeric receptors and synaptically localized α2β receptors with slower kinetics58–60.
To confirm whether the confinement of IPSPs to the dendrites is consistent with our understanding of octopus cell biophysics, we developed an improved biophysically and anatomically accurate model of octopus cells based on our findings (Supp. Fig. 4)18,61. This model performed as predicted based on our experimental results. As in our current-clamp recordings (Fig. 4A), removal of Kv and HCN conductances in the model changed the input resistance and current-voltage relationship of the neuron, resulting in reduced electronic isolation (Fig. 4D, blue). In control conditions, stimulation of dendritic glycinergic conductances induced negligible hyperpolarizing voltage changes (Fig. 4E, black). With increased input resistance, dendritic IPSPs measured at the soma were similar to in vitro recordings (Fig. 4E-F). Thus, this model confirms that dendritic IPSPs can elicit somatic hyperpolarization in octopus cells when electrotonic dendritic isolation is reduced.
While blocking Kv and HCN allowed us to reveal IPSPs at the soma, 4-AP increases the duration of the already unphysiological ChR2-evoked presynaptic action potential47, resulting in altered release probabilities and synaptic properties, amongst other caveats62. To directly confirm that the increase in somatically-measured IPSP amplitude can be explained by changes in input resistance alone, we used the octopus cell model to simulate dendritic glycinergic conductances and measure changes in dendritic current and somatic potential amplitude in the presence of blocked Kv and HCN channels. Kv and HCN block and the resulting change in input resistance increased the magnitude of soma-measured IPSPs for all glycine conductances (Fig. 4G, blue). Dendrite-measured currents did not change as a result of the hyperpolarization induced by Kv and HCN block regardless of synaptic location on the dendrites or the magnitude of the glycine conductance (Fig 4G, dark orange, light orange). Together, results from the model replicate those collected in vitro and provide evidence of functional glycinergic synaptic transmission that is difficult to detect with in vitro somatic recordings.
Inhibition decreases the magnitude and advances the timing of dendritic SGN inputs
SGN synapses onto octopus cell dendrites are arranged tonotopically, with higher frequency SGNs from the base of the cochlea terminating on the distal dendrites and lower frequency SGNs from more apical positions terminating more proximally (Fig. 1A). This organization has been proposed to re-synchronize coincidentally firing SGNs that are activated at slightly different times due to the time it takes for the sound stimulus to travel from the base to the apex of the cochlea18. To test how inhibition in the dendrites shapes coincidence detection, we first used our model to explore the influence of simultaneous activation of inhibitory and excitatory synapses at varying locations along the dendritic tree. By placing inhibitory synapses on proximal or distal dendrites and moving the relative location of excitation, we modelled the effect of on-path and off-path inhibition63 on somatically recorded EPSPs (Fig. 5A, Supp. Fig. 5). For both inhibition proximal to excitation (Fig. 5B-C: on-path) and inhibition distal to excitation (Fig. 5D-E: off-path), the model predicted that inhibition reduces EPSP amplitude and accelerates EPSP peak timing at the soma. Thus, the presence of inhibition appears to enable modulation of EPSP timing in dendritic compartments during continuous auditory stimuli when inhibition can be recruited beyond the onset of a sound and thus allow for adaptable temporal processing during the duration of a stimulus.
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_fig5.tif/full/max/0/default.jpg)
Coincident excitation and inhibition on octopus cell dendrites advances EPSP peak times.
(A) The impact of distance between excitatory and inhibitory synapses was measured in a computational model of octopus cells. Inhibitory synapses were placed either proximally (on-path) or distally (off-path) to excitation. Excitatory synapses were placed at varying locations along the dendritic arbor to change the anatomical distance (Δd) where d=0 is the location of inhibition (GGly) and d=1 is the condition where excitation (GAMPA) and inhibition are maximally separated. EPSPs were measured at the soma in all conditions (green). (B-E) Quantification of the percent change in soma-measured EPSP magnitude and the shift in EPSP peak timing in models of (B-C) on-path and (D-E) off-path inhibition. Example traces show EPSPs with (green) and without (black) inhibition at d=0 and d=1. Distal dendrites (d=1) have higher local input resistance and lower IPSP attenuation due to the sealed end. Inset scale bars are 1mV, 200ms. (F-H) Coincident stimulation of excitation and inhibition changes EPSP shape. (F) Representative responses to independent stimulation of excitatory SGNs (black), independent stimulation of inhibitory inputs (light blue), coincident stimulation of both excitation and inhibition (green), and independent stimulation of excitatory SGNs with the addition of 1µM strychnine (STN). Quantification of (G) the percent change in EPSP height and (H) the shift in EPSP peak timing during coincident Glyt2-ChR2 activation of inhibitory inputs (green: n = 8 cells, 6 mice), bath application of 25µM glycine (dark blue: n = 4 cells, 3 mice), and bath application of 1µM STN (orange: n = 5 cells, 4 mice). Activation of glycinergic receptors during excitation decreases EPSP heights and advances EPSP peaks. Blocking of tonically active glycine receptors slows and delays EPSPs. Data are presented as mean ± SD. Markers represent the average quantification for a cell.
To directly test if the model’s prediction that temporally coincident excitation and inhibition affects the timing and amplitude of excitatory SGN inputs as they travel towards the octopus cell soma, we coincidently activated SGNs and glycinergic inputs in vitro. In these experiments, the octopus cell properties were not altered pharmacologically and inhibition was undetectable or only visible with averaging over many sweeps (Fig. 5F, blue). When synaptic inhibition was evoked together with excitation (Fig. 5F, green), the soma-recorded EPSPs were smaller than when excitation was evoked alone (Fig. 5F, black: n = 8 cells, 6 mice). ChR2-evoked inhibition decreased EPSP heights by 25.2 ± 9.0% (Fig. 5G, green) and shifted the peak of EPSPs forward 57.5 ± 26µs (Fig. 5H, green). This effect was mimicked by bath application of 25µM glycine (Fig. 5G-H, blue: n = 4 cells, 3 mice). Further, bath application of 1µM STN had the opposite effect, resulting in larger EPSPs, delayed peak times, and increased half-widths (Fig. 5F-H, orange: n = 5 cells, 4 mice). Thus, the timing of EPSP arrival may be shaped both by the release of synaptic glycine and by tonically active glycine channels. Of note, many SGNs also terminate on the octopus cell soma, where inhibition is minimal. This suggests that the octopus cell’s ability to act as a coincidence detector depends on two stages of compartmentalized computations, one in the dendrite that combines excitation and inhibition to provide important information about which frequencies co-occur in a complex sound stimulus and one in the soma that is restricted by the rigid temporal summation window for coincidence detection. Together with the electrotonic properties of the octopus cell and the dominance of low threshold, low jitter Ia SGN inputs, these combined computations can enable reliable coincidence detection and proper cross-frequency binding needed for perception of sound.
Discussion
Coincidence detection plays a critical role in many cognitive and perceptual processes, from the ability to localize sound, to the binding of auditory and visual features of a common stimulus. Depending on the computation, the temporal window for integration can range widely, thereby requiring circuitry with distinct anatomical and physiological properties. Here, we describe a two-domain mechanism for coincidence detection that can detect co-occurring frequencies with different degrees of precision. By mapping and selectively activating synaptic inputs onto octopus cells both in vitro and in a computational model, we revealed that compartmentalized dendritic nonlinearities impact the temporal integration window under which somatic coincidence detection computations are made. The arrival of many small, reliable excitatory inputs (Fig. 3) from low-threshold SGNs (Fig. 2) is continuous throughout an ongoing stimulus. We demonstrate that glycinergic inhibition to octopus cell dendrites (Fig. 1) can shift the magnitude and timing of SGN EPSPs as they summate in the soma (Fig. 4,5). The narrow window for coincidence detection computations allows the octopus cell to respond with temporal precision using momentary evidence provided by SGNs at the onset of the stimulus. We propose that, as a stimulus persists, inhibition onto octopus cell dendrites can adjust the timing of excitation before arriving at the soma for the final input-output computation. This allows the cell to make an additional computation with a slightly longer window for evidence accumulation without compromising the accuracy of the somatic onset computation (Fig. 6).
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_fig6.tif/full/max/0/default.jpg)
Proposed model of flexible dendritic timing for precise somatic coincidence detection.
(A-B) Co-occurring frequencies in broadband onsets and frequency modulations occur on different time scales. (Top) Spectrograms illustrate frequency strength over time. (Bottom) Waveform illustrate total intensity over time. Both of these stimuli drive firing in octopus cells despite the limitations imposed by a ∼1ms window for temporal integration. (C) Excitation must summate at the soma of octopus cells within a narrow time window (∼1ms) to achieve a depolarization rate rapid enough to trigger action potentials. Summation of excitation alone accounts for responses to sound onsets (inset, top). Stimuli that require summation over a longer time period, such as frequency modulated sweeps, require synaptic inhibition to modulate the timing of excitation on octopus cell dendrites before they reach the soma for coincidence detection computations (inset, bottom). We propose a mechanism for preferential processing of a subset of excitatory inputs where selective temporal advancement of a subset of EPSPs could expand the effective window for coincidence detection at the soma.
As coincidence detectors in the auditory system, octopus cells are faced with the challenge of recognizing complex sounds that include many frequencies that co-occur from the beginning to the end of the stimulus. As shown by in vivo recordings6,29, octopus cells respond well to cues that include complex spectrotemporal patterns, including frequency modulations beyond the onset of the stimulus29. Given that the auditory environment is filled with overlapping sound stimuli, such responses presumably allow the octopus cell to encode which frequencies belong to which sound. Our data thus support the role of octopus cells beyond simple onset coincidence detectors that rely solely on the temporal summation of excitation. The results suggest that, despite high Kv and HCN conductances at rest, the addition of dendritic inhibition transforms the magnitude and timing of SGN signals as they arrive in the cell body, which may expand their response selectivity and allow them to become a slightly leakier integrator and thus accumulate evidence beyond onsets. Although, this inhibition is difficult to detect because of shunting, our data demonstrate that it is both present and impactful.
As well as needing to work beyond onsets, an effective coincidence detector in the auditory system must also function reliably across a range of sound intensities. Intensity information is encoded by the number and types of SGNs that are activated in the cochlea. The Ia, Ib, and Ic molecular subtypes defined in mouse40,41,43 broadly correspond to the anatomically and physiologically defined subtypes described across species42,64. We find that the majority of inputs onto octopus cells come from Ia SGNs, which most closely correspond to the low-threshold, high-spontaneous rate population. Consistent with this result, single-unit SGN recordings in cats demonstrated a bias towards low-threshold, high-spontaneous rate axon collaterals in the octopus cell area33. Low-threshold SGNs are also characterized by short first spike latencies and low temporal jitter65–68. A hallmark of the octopus cell is the fact that it only fires action potentials when many SGN inputs are activated within a narrow period of time69. The presence of many low-threshold and temporally precise inputs on the octopus cell may help ensure that coincidence detection still works reliably for quiet sounds. Further, Ia inputs onto octopus cells do not exhibit paired-pulse depression, not unlike low levels of depression seen in Ia inputs to bushy cells70. The presence of SGN inputs without paired-pulse depression could be beneficial for encoding sustained auditory signals. Finally, although Ia SGNs are over-represented, Ib and Ic inputs are also present. Since precise, low-threshold SGN responses can be saturated by background noise such that responses to relevant stimuli are masked38,65,68,71, recruitment of higher threshold SGNs at higher sound intensities may compensate for this tradeoff.
The presence of inhibitory inputs onto dendrites is a fundamental feature of the nervous system and, in other systems, contributes to a neuron’s final computation. For example, direction selectivity computations in dendrites of retinal cells require excitation-inhibition interactions in dendritic compartments72,73. In pyramidal cells of the cortex and hippocampus, the spatial distribution of inhibition impacts dendritic non-linearities in a branch selective manner74–79. However, octopus cells do not share all mechanisms for dendritic computation as neurons in the cortex and hippocampus. The lack of backpropagating action potentials and dendritic calcium spikes in octopus cells causes submillisecond timing differences in dendritic summation of coincident excitation and local inhibition to be consequential in subthreshold computation before somatic action potential generation.
Although this work uncovers a role for inhibition, understanding of octopus cell computations is limited by the fact that it remains unclear what kind of information is carried by inhibitory inputs. Although the presence of presynaptic glycinergic puncta in the octopus cell area80–83 and glycinergic receptor expression in octopus cells84–88 is well established, it is unknown where this glycinergic innervation originates. Of the local neurons within the CNC that provide inhibition to the VCN, there is no evidence of connections to octopus cells from D-Stellate52, L-Stellate89, or tuberculoventral cells90. Outside of the CNC, terminal degeneration experiments in cats suggested the superior periolivary nucleus (SPON) and the ventral division of the lateral lemniscus (VNLL) as potential sources of descending inhibition to the octopus cell area91. Octopus cells provide excitatory input to both the SPON92–96 and the VNLL20,97–101, raising the possibility of feedback inhibition from the auditory brainstem as a circuit mechanism for elongated temporal summation windows during ongoing stimuli. Although feedback inhibition is not rapid enough to prevent or alter the onset response that octopus cells are well-known for, it could limit the duration of a response or change the effective coincidence detection window as the stimulus continues. Future studies will be required to identify the source of inhibition and its organization along the dendrites. If inhibitory inputs tonotopically match the local, narrowly-tuned dendritic SGN inputs, it is possible that frequency matched inhibition could influence spectral selectivity or feature extraction. On the other hand, broadly tuned inhibition could reduce depolarization block or serve as a temporal milestone that signals gaps or offsets. Further characterization of in vivo octopus cell responses in complex sound environments may clarify the effect of noise on signal detection and could reveal additional features of this cell’s contributions to perception of the auditory world.
Acknowledgements
We thank Dr. Nace Golding (University of Texas at Austin), Dr. Matthew McGinley (Baylor College of Medicine), Dr. Phillip Joris (KU Leuven), and Dr. Bernardo Sabatini for helpful discussions and feedback. Sadie Quinn, Lucy Lee, and Ryan Merrow provided valuable technical assistance. Dr. Bruce Bean (Harvard Medical School) generously provided access to electrophysiological equipment. Slc6a5tm1.1(Cre)Ksak mice were kindly provided by Dr. Wade Regehr (Harvard Medical School). Ntng1em1(Cre)Kfra mice were made and kindly provided by Dr. Fan Wang (Massachusetts Institute of Technology). Myo15atm1.1(Cre)Ugds mice were kindly provided by Dr. Stefan Heller (Stanford). We thank Rigoberto Ramirez, Tenzin Paljorwa, and Edgar Ramirez for animal care support. We are grateful to the Neurobiology Imaging Facility (NIF) for software availability and to the HMS Research Instrumentation Core for the design and fabrication of temperature regulation equipment. This work was supported by grants from the BRAIN Initiative 1R01NS118402 to L.V.G., the National Institute on Deafness and Other Communication Disorders 5R01DC009223 to L.V.G. and 1F32DC020070 to L.J.K, the William Randolph Hearst Fund to L.J.K, and the Broad Institute’s Stanley Center for Psychiatric Research to S.H. and G.F.
Declaration of Interests
The authors declare no competing interests.
Materials and Methods
Animal Use and Transgenic Mouse Lines
All procedures were approved by and conducted in accordance with Harvard Medical School Institutional Animal Care and Use Committee. Male and female mice (Mus musculus) were bred on a C57BL/6 background at the Harvard Center for Comparative Medicine or obtained from Jackson Laboratories (Bar Harbor, ME). Mice were housed in groups of up to five animals and maintained on a 12hr light/dark cycle. Transgenic alleles were heterozygous for each transgene for all experimental animals. Descriptions of allele combinations for all experiments can be found in Supplemental Table 1.
Spiral ganglion neurons (SGNs) and their central auditory nerve fibers (ANFs) were targeted using either Foxg1tm1.1(Cre)Ddmo (Foxg1Cre)102 or Foxg1Flp, both of which drive robust reporter expression neurons in the auditory and vestibular ganglion103,104 and the neocortex105,106, but not in brainstem or midbrain neurons. Foxg1Flp mice were generated by crossing the Foxg1tm1.1Fsh mouse line107 with the Tg(EIIa-Cre)C5379Lmgd mouse line108, then backcrossing to isolate the flp transgene and remove the Cre transgene.
Inhibitory inputs to octopus cells were targeted with Slc6a5tm1.1(Cre)Ksak mice (Glyt2Cre)109.
Octopus cells were sparsely labeled with the Tg(Thy1-YFP)HJrs (Thy1) mouse line110. This line labels ∼0-15 octopus cells amongst other neurons throughout the brain.
Ib/c SGNs were targeted using the Ntng1em1(Cre)Kfra (Ntng1Cre) mouse line, which drives expression in neurons throughout the nervous system (Supp. Fig. 1F) and disrupts expression of the endogenous allele44. Auditory brainstem responses in adult Ntng1Cre/+ mice are normal. Ic SGNs were sparsely targeted with the Myo15atm1.1(Cre)Ugds (Myo15iCre) mouse line111.
Fluorescent reporters included Gt(ROSA)26Sortm14(CAG-tdTomato) (Ai14, tdT)112, Gt(Rosa)26Sortm34.1(CAG-Syp/tdTomato) (Ai34, syp/tdT), and Gt(Rosa)26Sortm1.2(CAG-EGFP)Fsh (RCE:FRT, EYFP)113. We also used Gt(Rosa)26Sortm32(CAG-COP4*H134R.EYFP) (Ai32, ChR2)114 to drive synaptic activity in in vitro slice experiments.
Histology and Reconstructions
For immunohistochemical labeling, mice were deeply anesthetized with isoflurane and transcardially perfused with 15mL of 4% paraformaldehyde (PFA) in 0.1M phosphate-buffered saline (PBS) using a peristaltic pump (Gilson). Whole skulls containing brain and cochlea were immediately transferred to 20mL of 4% PFA and post-fixed overnight at 4°C. Fixed brains and cochlea were removed from the skulls and washed with 0.1M PBS.
Brains were collected from mice of both sexes, aged 28-38 days, and embedded in gelatin-albumin hardened with 5% glutaraldehyde and 37% PFA115. Sections were cut at 35, 65, or 100μm with a vibrating microtome (Leica VT1000S) and free-floating tissue was collected in 0.1M PBS. For sections less than 65μm, tissue was permeabilized and nonspecific staining was blocked in a solution of 0.2% Triton X-100 and 5% normal donkey serum (NDS, RRID: AB_2337258) in 0.1M PBS for 1 hour. After blocking, tissue was treated with primary antibody in a solution containing 0.2% Triton X-100 and 5% NDS in PBS for 1-2 nights at room temperature. Primary antibodies used were: chicken anti-GFP (1:1000, RRID:AB_10000240), rabbit anti-RFP (1:1000, RRID:AB_2209751), goat anti-calretinin (1:1000, RRID:AB_1000034), and guinea pig anti-VGLUT1 (1:500, RRID:AB_887878). Sections were washed in 0.1M PBS then incubated in a secondary antibody solution (1:1000) containing 0.2% Triton X-100 and 5% NDS for 2-3hrs at room temperature. Tissue sections were mounted on charged slides and coverslipped (Vectashield Hardset Antifade Mounting Medium with DAPI), and imaged using a Zeiss Observer.Z1 confocal microscope.
For 100μm sections, tissue was washed in CUBIC-1A solution for 1hr for strong permeabilization and delipidization116,117. Tissue was then further permeabilized and nonspecific staining was blocked in a solution of 0.2% Triton X-100 and 5% NDS in 0.1M PBS for 1hr. After blocking, tissue was treated with primary antibody in a solution containing 0.2% Triton X-100 and 5% NDS in PBS for 4 nights at 37°C. Primary antibodies used were: chicken anti-GFP (1:1000, RRID:AB_10000240), and rabbit anti-RFP (1:1000, RRID:AB_2209751). Sections were then incubated in a secondary antibody solution (1:400) containing 0.2% Triton X-100 and 5% NDS for 4 nights at 37°C. Tissue sections were pre-incubated in CUBIC2 solution, then temporarily mounted on uncharged slides with CUBIC2 solution for immediate imaging using a Zeiss Observer.Z1 confocal microscope.
Octopus cells and synaptic puncta were reconstructed in Imaris (Oxford Instruments). YFP signal from the target octopus cell was used to generate a surface reconstruction and mask syp/tdT signal. Dendrites were reconstructed using the masked YFP signal and separated into 10μm increments. Masked syp/tdT puncta were marked and localized to a 10μm increment of the dendritic tree. Synapse counts, dendrite metrics, and masked channels were exported to Excel (Microsoft) for further analysis.
Cochlea were collected from mice of both sexes, aged 28-42 days. The bony labyrinth of the inner ear was decalcified in 0.5M ethylenediamine tetraacetic acid (EDTA) for 3 nights at 4°C and embedded in gelatin-albumin hardened with 5% glutaraldehyde and 37% PFA. Sections were cut at 65μm with a vibrating microtome (Leica VT1000S) and free-floating tissue was collected in 0.1M PBS. Sections were washed in CUBIC-1A solution for 1hr for strong permeabilization and delipidization. Tissue was further permeabilized and nonspecific staining was blocked in a solution of 0.2% Triton X-100 and 5% NDS in 0.1M PBS for 1hr. After blocking, tissue was treated with primary antibody in a solution containing 0.2% Triton X-100 and 5% NDS in PBS for 2 nights at room temperature. Primary antibodies used were: chicken anti-GFP (1:1000, RRID:AB_10000240), rabbit anti-RFP (1:1000, RRID:AB_2209751), goat anti-calretinin (1:1000, RRID:AB_1000034).
Sections were then incubated in a secondary antibody solution (1:500) containing 0.2% Triton X-100 and 5% normal goat serum for 2-3hrs at room temperature. Tissue sections were mounted on charged slides, coverslipped (Vectashield Hardset Antifade Mounting Medium with DAPI), and imaged using a Zeiss Observer.Z1 confocal microscope.
Acute Slice Electrophysiology
Data were obtained from mice of both sexes, aged 24-47 days. Mice were deeply anesthetized with isoflurane and perfused transcardially with 3mL of 35°C artificial cerebral spinal fluid (ACSF; 125mM NaCl, 25mM glucose, 25mM NaHCO3, 2.5mM KCl, 1.25mM NaH2PO4, 1.4mM CaCl2, and 1.6mM MgSO4, pH adjusted to 7.45 with NaOH). For high calcium concentration experiments presented in Fig. 3C, ACSF contained 125mM NaCl, 25mM glucose, 25mM NaHCO3, 2.5mM KCl, 1.25mM NaH2PO4, 2.4mM CaCl2, and 1.3mM MgSO4. Mice were rapidly decapitated and the brain was removed and immediately submerged in ACSF. Brains were bisected and 250μm slices were prepared in the sagittal plane with a vibrating microtome (Leica VT1200S; Leica Systems). Prepared slices were incubated for 30min at 35°C, then allowed to recover at room temperature for at least 30min. ACSF was continuously bubbled with 95% O2/5% CO2.
Whole-cell recordings were conducted at 35°C using a Multiclamp 700B (Molecular Devices) in current-clamp mode with experimenter adjusted and maintained bridge balance and capacitance compensation. Data were filtered at 12kHz, digitized at 83–100kHz, and acquired using pClamp9 (Molecular Devices). Neurons were visualized using infrared Dodt gradient contrast (Zeiss Examiner.D1; Zeiss Axiocam 305 mono). Glass recording electrodes (3–7MΩ) were wrapped in parafilm to reduce capacitance and filled with an intracellular solution containing 115mM K-gluconate, 4.42mM KCl, 0.5mM EGTA, 10mM HEPES, 10mM Na2Phosphocreatine, 4mM MgATP, 0.3mM NaGTP, and 0.1% biocytin, osmolality adjusted to 300mmol/kg with sucrose, pH adjusted to 7.30 with KOH. All membrane potentials are corrected for a 11mV junction potential.
For optogenetic activation, full-field 470nm blue light was presented through a 20x immersion objective (Zeiss Examiner.D1). Onset, duration, and intensity of light was controlled by a Colibri5 LED Light Source (Zeiss). For electrical stimulation, glass stimulating electrodes were placed in the auditory nerve root and 20µs current pulses were generated with a DS3 current stimulator (Digitimer).
Analysis and Statistical Tests
Cell counts and habenula measurements were performed in ImageJ/FIJI software (National Institutes of Health). Electrophysiology data were analyzed using custom scripts and NeuroMatic analysis routines118 in Igor Pro (Wavemetrics).
For data with equal variance (Levene’s test), one-way ANOVAs with Tukey’s HSD post hoc test were used where appropriate to determine statistical significance. For data with non-homogenous variances, one-way ANOVAs with a Welch F test were used with a Tukey’s HSD post hoc test. Errors and error bars report standard deviation (SD) or standard error of the mean (SEM) as noted in figure legends and throughout the text.
Computational Modelling
Computer simulations were performed using the NEURON 8.2 simulation environment119, with an integration time constant of 25µs. The morphology of the octopus neuron was obtained from McGinley et al., 2012. The active and passive properties of the model were optimized to match the experimental recordings. We set the passive parameters as follows: internal or axial resistance (Ri or Ra) to 150Ω.cm, membrane resistance (Rm) to 5KΩ.cm2, capacitance (Cm) to 0.9µF/Cm2 and resting membrane potential (Vm) to -65mV. We included the following ion-channel conductances in our morphologically realistic octopus neuron model: Fast Na+ (ḡNA), low-voltage activated K+ (ḡKLT), high threshold K+ (ḡKHT), fast transient K+ (ḡKA), hyperpolarization-activated cyclic nucleotide-gated HCN (ḡh), leak K+ (ḡleak). The kinetics of all the active ion-channel conductances were obtained from Manis and Campagnola, 2018 and the maximal conductance was optimized to match experimental data. We introduced a scaling factor (scl) to scale the maximal conductance to match the sag and input resistance of the experimental recordings (Supp. Fig. 4). Reversal potentials for HCN, Na+ and K+ respectively were (in mV), Eh= -38, ENa= 50 and EK= -70. Excitatory AMPA synaptic conductance and Inhibitory glycine synaptic conductance were introduced in the proximal and distal dendrites to test the impact of dendritic inhibition on the EPSP height and peak time. The rise and decay time of AMPA and glycine conductance were set to 0.3ms and 3ms respectively, to mimic the fast synaptic transients observed in octopus cells. The reversal potential of AMPA and glycine conductance was set to 0 and -80mV respectively.
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_tblS1.tif/full/max/0/default.jpg)
Summary and description of experimental genotypes presented in figures.
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_figS1.tif/full/max/0/default.jpg)
Ntng1Cre has high specificity for Ib/c SGNs.
(A) Calretinin immunopositive (CR+) Ia/b fibers (CR, yellow) preferentially innervate the pillar side of inner hair cells (IHCs). Ib/c fibers with Ntng1Cre-mediated expression of tdTomato (Ntng1-tdT, magenta) preferentially innervate the modiolar side of IHCs. IHCs also immunolabel for CR. (B) CR+ Ia/b fibers (yellow) pass through the pillar side of the habenula while Ntng1-tdT+ Ib/c fibers (magenta) pass through the modiolar side. Arrowhead highlights a Ntng1-tdT fiber passing through the pillar side of the habenula but ultimately terminating on the modiolar side of the IHC. (C) Normalized position of CR+ Ia/b (yellow) and Ntng1-tdT+ Ib/c (magenta) fibers along the pillar to modiolar axis of the habenula (n = 124 fibers; 5 mice). (D) In the central nervous system, Ntng1-tdT is present throughout the brain, but is restricted to SGN axons in the ventral cochlear nucleus where the octopus cell area (OCA) is found. (E) In the OCA, CR immunolabel is present in Ia/b SGN axons and puncta. As in the ganglion, CR co-labels with some Ib/c puncta (Ntng1-syp/tdT).
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_figS2.tif/full/max/0/default.jpg)
Myo15iCre sparsely labels Ic SGNs.
(A) Cochlear sections with calretinin (CR) immunolabeling of hair cells and type Ia/b SGNs and Myo15iCre-mediated expression of tdTomato (Myo15-tdT) in hair cells and some type Ic SGNs. (B) CR+ Ia/b fibers (yellow) preferentially innervate the pillar side of IHCs. Sparse Ic fibers with Myo15-tdT (magenta) preferentially innervate the modiolar side of IHCs. IHCs label with both tdT and CR. (C) CR+ Ia/b fibers (yellow) pass through the pillar side of the habenula while sparsely labeled Myo15-tdT+ Ic fibers (magenta) pass through the modiolar side. (D) Normalized position of CR+ Ia/b (yellow) and Myo15-tdT+ Ic (magenta) fibers along the pillar to modiolar axis of the habenula (n = 90 fibers; 4 mice). (E) 65µm cochlear section containing SGN somas. SGNs have variable levels of calretinin (CR) immunolabeling corresponding to the three molecular subtypes. Ia/b SGN somas label with high and medium levels of CR, respectively. Ic somas label with very low levels of CR. Myo15-tdT is sparsely found in Ic SGNs. All SGN somas are labeled with Foxg1Flp-mediated expression of EYFP (Foxg1-EYFP). (F) tdT+CR-SGNs make up 4.7 ± 2.3% of the SGN population (n = 2150 neurons, 5 mice), indicating sparse reporter expression. Data are presented as mean ± SD; individual data points signify percent coverage per animal. Dotted lines are estimated percentages for type Ic SGNs from 1: Petitpré et al., 2018, 2: Shrestha et al., 2018, and 3: Sun et al., 2018. (G) In the OCA, VGlut1 immunolabel neatly tiles around octopus cells with sparse Myo15-syp/tdT puncta. (H) Density of all SGNs (black: data from Fig. 1D), Ib/c SGNs (magenta: data from Fig. 2D), and sparse Ic inputs (open magenta circles: 1.1 ± 0.6, n = 6 cells, 3 mice). Data are presented as mean ± SD. Markers represent the total puncta density computed per reconstructed octopus cell. (I) Puncta density per 100µm2 of soma surface area (all SGN, black: data from Fig. 1E; sparse I/c inputs, magenta open circles: 0.7 ± 0.4, n = 6 cells, 3 mice) and density along the length of the dendritic tree, relative to the soma. Data are presented as mean ± SEM.
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_figS3.tif/full/max/0/default.jpg)
Dendritic and synaptic reconstructions of octopus cells.
(A) Total length of reconstructed dendritic arbors for 31 octopus cells. (B) Total surface area of reconstructed dendritic arbors for 31 octopus cells. (C) Octopus cell reconstructions were normalized to the longest reconstructed dendrite. Total length of reconstructed dendrites correlated with the longest branch per neuron. (D) Total length of reconstructed dendrites correlated with total dendritic surface area. (E-G) Longest branch length, total dendrite length, and total surface area compared to estimated position of the octopus cell soma in the tonotopic organization of the OCA. (H-J) Total number of reconstructed SGN puncta (Foxg1-syp/tdT, black), inhibitory puncta (Glyt2-syp/tdT, green), Ib/c SGN puncta (Ntng1-syp/tdT, magenta), and sparse Ic SGN puncta (Myo15-syp/tdT, open magenta circles) compared to the longest branch length, total dendrite length, and total dendrite surface area. (K-M) Density of reconstructed SGN puncta (Foxg1-syp/tdT, black), inhibitory puncta (Glyt2-syp/tdT, green), Ib/c SGN puncta (Ntng1-syp/tdT, magenta), and sparse Ic SGN puncta (Myo15-syp/tdT, open magenta circles) compared to longest branch length, total dendrite length, and total dendrite surface area.
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_figS4.tif/full/max/0/default.jpg)
Optimizing active and passive properties of an octopus cell model.
(A) Subthreshold somatic voltage response to a hyperpolarizing current injection in an in vitro whole-cell current clamp recording of an octopus cell. (B) Somatic voltage responses from a morphologically realistic octopus cell model for various scaling factors of maximal conductances of active ion-channels. (C) Comparison of somatic hyperpolarizations in a model reproducing experimental data during control (black) and Kv and HCN block conditions (blue). (D) Illustration of injection and recording locations for panels E-G in a morphologically realistic octopus cell model. (E) IV curves (change in somatic membrane potential as function of current injection magnitude) from a representative experimental octopus cell. Dotted lines plot linear fits of the experimental data. Rin dotted line is the slope of the fit. (F-G) Impact of leak conductance (ḡleak) and scaling factor (scl) on passive properties of the model. Input resistance of the octopus neuron model as a function of membrane resistivity (Rm) with ḡleak=0 (F) and ḡleak=1.67 pS (g) for various scaling factor values indicated in different colors. (H) Illustration of injection and recording locations for panels I-K in a morphologically realistic octopus cell model. (I) Inhibitory post synaptic currents (IPSCs) recorded from glycinergic synapses in proximal (blue) and distal (orange) stimulation during control (solid) and Kv and HCN block conditions (dotted). (J) Peak IPSC magnitude as function of glycine conductance in proximal (blue) and distal (orange) stimulation during control (solid) and Kv and HCN block conditions (dotted). (K) Transfer impedance as function of frequency from proximal dendrites to soma (blue) and distal dendrites to soma (orange) during control (solid) and Kv and HCN block conditions (dotted).
![](https://prod--epp.elifesciences.org/iiif/2/100492%2Fv1%2Fcontent%2F582808v2_figS5.tif/full/max/0/default.jpg)
Impact of inhibitory synaptic location and distance between excitatory and inhibitory synapse on somatic EPSP amplitude and timing.
(A-B) Illustration of injection and recording locations for off-path (A) and on-path (B) inhibition paradigms and the normalized relative distance (d) between excitatory synapses. The impact of on-path and off-path inhibition in the dendrites is primarily determined by the local potential change by EPSP and the attenuation or the length constant (λ) of the IPSP towards the excitatory synaptic location. The exponential decay of membrane voltage is asymmetric, with lower λ for the open end and higher λ for sealed end propagation. Distal parts of the dendrites have higher local input resistance and lower attenuation of IPSP due to the sealed end. (C-D) Percentage change in somatic EPSP height with dendritic glycinergic inhibition as function of normalized distance between excitatory and inhibitory synapses in off-path (C) and on-path (D) inhibition for various E/I ratio with excitatory AMPA conductance (GAMPA) set at 5nS. Average shown in black with SEM in shaded region. (E-F) Somatic EPSP peak time shift with dendritic glycinergic inhibition as function of normalized distance between excitatory and inhibitory synapses in off-path (E) and on-path (F) inhibition for various E/I ratio with excitatory AMPA conductance (GAMPA) set at 2nS. Average shown in black with SEM in shaded region.
References
- 1.Temporal coherence and attention in auditory scene analysisTrends Neurosci 34:114–123https://doi.org/10.1016/j.tins.2010.11.002
- 2.Auditory scene analysis: The sweet music of ambiguityFront. Hum. Neurosci 5
- 3.The intrinsic organization of the cochlear nuclei in the catActa Otolaryngol. (Stockh 67:352–359https://doi.org/10.3109/00016486909125462
- 4.Cytoarchitecture of the cochlear nuclei in the catJ. Comp. Neurol 136:453–483https://doi.org/10.1002/cne.901360407
- 5.Single unit activity in the posteroventral cochlear nucleus of the catJ. Comp. Neurol 162:247–268https://doi.org/10.1002/cne.901620206
- 6.Information Processing by Onset Neurons in the Cat Auditory BrainstemJARO - J. Assoc. Res. Otolaryngol 21:201–224https://doi.org/10.1007/s10162-020-00757-0
- 7.Physiological response properties of cells labeled intracellularly with horseradish peroxidase in cat dorsal cochlear nucleusJ. Comp. Neurol 213:426–447https://doi.org/10.1002/cne.902130407
- 8.Single unit analysis of the posteroventral cochlear nucleus of the decerebrate catNeuroscience 7:1995–2010https://doi.org/10.1016/0306-4522(82)90013-6
- 9.Hyperpolarization-activated, mixedcation current (I(h)) in octopus cells of the mammalian cochlear nucleusJ. Neurophysiol 84:806–817https://doi.org/10.1152/jn.2000.84.2.806
- 10.Potassium currents in octopus cells of the mammalian cochlear nucleusJ. Neurophysiol 86:2299–2311https://doi.org/10.1152/jn.2001.86.5.2299
- 11.Genetic perturbations suggest a role of the resting potential in regulating the expression of the ion channels of the KCNA and HCN families in octopus cells of the ventral cochlear nucleusHear. Res 345:57–68https://doi.org/10.1016/j.heares.2017.01.001
- 12.Recordings from slices indicate that octopus cells of the cochlear nucleus detect coincident firing of auditory nerve fibers with temporal precisionJ. Neurosci 15:3138–3153https://doi.org/10.1523/jneurosci.15-04-03138.1995
- 13.Role of intrinsic conductances underlying responses to transients in octopus cells of the cochlear nucleusJ. Neurosci 19:2897–2905https://doi.org/10.1523/jneurosci.19-08-02897.1999
- 14.Synaptic integration in dendrites: Exceptional need for speedJ. Physiol 590:5563–5569https://doi.org/10.1113/jphysiol.2012.229328
- 15.Outward currents in isolated ventral cochlear nucleus neuronsJ. Neurosci 11:2865–2880https://doi.org/10.1523/jneurosci.11-09-02865.1991
- 16.Synaptic responses and electrical properties of cells in brain slices of the mouse anteroventral cochlear nucleusJ. Neurosci 3:2043–2053https://doi.org/10.1523/jneurosci.03-10-02043.1983
- 17.Rapid Integration Across Tonotopy by Individual Auditory Brainstem Octopus CellsThe Computing Dendrite, From Structure to Function :223–243https://doi.org/10.1007/978-1-4614-8094-5_14
- 18.Generating Synchrony from the Asynchronous: Compensation for Cochlear Traveling Wave Delays by the Dendrites of Individual Brainstem NeuronsJ. Neurosci 32:9301–9311https://doi.org/10.1523/jneurosci.0272-12.2012
- 19.Submillisecond monaural coincidence detection by octopus cellsActa Acust. United Acust 104:852–855https://doi.org/10.3813/AAA.919238
- 20.Acoustic stria: Anatomy of physiologically characterized cells and their axonal projection patternsJ. Comp. Neurol 482:349–371https://doi.org/10.1002/cne.20407
- 21.Responses of Cochlear Nucleus Cells and Projections of their AxonsIn The Mammalian Cochlear Nuclei: Organization and Function NATO ASI series :349–360https://doi.org/10.1007/978-1-4615-2932-3_27
- 22.Contributions of Ion Conductances to the Onset Responses of Octopus Cells in the Ventral Cochlear Nucleus: Simulation ResultsJ. Neurophysiol 83:301–314https://doi.org/10.1152/jn.2000.83.1.301
- 23.Mechanisms of Onset Responses in Octopus Cells of the Cochlear Nucleus: Implications of a ModelJ. Neurophysiol 78:872–883https://doi.org/10.1152/jn.1997.78.2.872
- 24.Sensitivity of the cochlear nucleus octopus cell to synaptic and membrane properties: A modeling studyJ. Acoust. Soc. Am 102:403–412https://doi.org/10.1121/1.419762
- 25.Mechanisms of the cochlear nucleus octopus cell’s onset response: Synaptic effectiveness and thresholdJ. Acoust. Soc. Am 103:1940–1950https://doi.org/10.1121/1.421346
- 26.A computational model of the cochlear nucleus octopus cellJ. Acoust. Soc. Am 102:391–402https://doi.org/10.1121/1.419761
- 27.A phenomenological spiking model for octopus cells in the posterior–ventral cochlear nucleusBiol. Cybern 115:331–341https://doi.org/10.1007/s00422-021-00881-x
- 28.An investigation of dendritic delay in octopus cells of the mammalian cochlear nucleusFront. Comput. Neurosci 6https://doi.org/10.3389/fncom.2012.00083
- 29.Mammalian octopus cells are direction selective to frequency sweeps by excitatory synaptic sequence detectionProc. Natl. Acad. Sci 119https://doi.org/10.1073/pnas.2203748119
- 30.A bushy cell network in the rat ventral cochlear nucleusJ. Comp. Neurol 516:241–263https://doi.org/10.1002/cne.22139
- 31.Vessicular glutamate transporters 1 and 2 are differentially associated with auditory nerve and spinal trigeminal inputs to the cochlear nucleusJ. Comp. Neurol 500:777–787https://doi.org/10.1002/cne.21208
- 32.Central projections of auditory-nerve fibers of differing spontaneous rateI. Anteroventral cochlear nucleus. J. Comp. Neurol 313:240–258https://doi.org/10.1002/cne.903130205
- 33.Central projections of auditory nerve fibers of differing spontaneous rate, II: Posteroventral and dorsal cochlear nucleiJ. Comp. Neurol 327:17–36https://doi.org/10.1002/cne.903270103
- 34.The central projections of intracellularly labeled auditory nerve fibers in cats: an analysis of terminal morphologyJ. Comp. Neurol 249:261–278https://doi.org/10.1002/cne.902490210
- 35.Intracellular marking of physiologically characterized cells in the ventral cochlear nucleus of the catJ. Comp. Neurol 225:167–186https://doi.org/10.1002/cne.902250203
- 36.Intracellular labeling of auditory nerve fibers in guinea pig: central and peripheral projectionsJ. Comp. Neurol 381:188–202
- 37.Opposing Gradients of Ribbon Size and AMPA Receptor Expression Underlie Sensitivity Differences among Cochlear-Nerve/Hair-Cell SynapsesJ. Neurosci 31:801–808https://doi.org/10.1523/JNEUROSCI.3389-10.2011
- 38.Auditory-nerve response from cats raised in a low-noise chamberJ. Acoust. Soc. Am 63:442–455https://doi.org/10.1121/1.381736
- 39.Single-neuron labeling in the cat auditory nerveScience 216:1239–1241https://doi.org/10.1126/science.7079757
- 40.Neuronal heterogeneity and stereotyped connectivity in the auditory afferent systemNat. Commun 9https://doi.org/10.1038/s41467-018-06033-3
- 41.Sensory Neuron Diversity in the Inner Ear Is Shaped by ActivityCell 174:1229–1246https://doi.org/10.1016/j.cell.2018.07.007
- 42.Molecular signatures define subtypes of auditory afferents with distinct peripheral projection patterns and physiological propertiesProc. Natl. Acad. Sci. U. S. A 120https://doi.org/10.1073/pnas.2217033120
- 43.Hair Cell Mechanotransduction Regulates Spontaneous Activity and Spiral Ganglion Subtype Specification in the Auditory SystemCell 174:1247–1263https://doi.org/10.1016/j.cell.2018.07.008
- 44.Recurrent circuitry is required to stabilize piriform cortex odor representations across brain stateseLife 9https://doi.org/10.7554/eLife.53125
- 45.3D model of frequency representation in the cochlear nucleus of the CBA/J mouseJ. Comp. Neurol 521:1510–1532https://doi.org/10.1002/cne.23238
- 46.Optical induction of synaptic plasticity using a light-sensitive channelNat. Methods 4:139–141https://doi.org/10.1038/nmeth988
- 47.Achieving High-Frequency Optical Control of Synaptic TransmissionJ. Neurosci 34:7704–7714https://doi.org/10.1523/jneurosci.4694-13.2014
- 48.Detection of synchrony in the activity of auditory nerve fibers by octopus cells of the mammalian cochlear nucleusProc. Of 97:11773–11779https://doi.org/10.1073/pnas.97.22.11773
- 49.Auditory nerve fibers excite targets through synapses that vary in convergence, strength, and short-term plasticityJ. Neurophysiol 104:2308–2320https://doi.org/10.1152/jn.00451.2010
- 50.Connections and synaptic function in the posteroventral cochlear nucleus of deaf jerker miceJ. Comp. Neurol 510:297–308https://doi.org/10.1002/cne.21788
- 51.Electrophysiological properties of ventral cochlear nucleus neurons of the dogHear. Res 256:93–103https://doi.org/10.1016/j.heares.2009.07.004
- 52.Morphology and physiology of cells in slice preparations of the posteroventral cochlear nucleus of miceJ. Comp. Neurol 295:136–154https://doi.org/10.1002/cne.902950112
- 53.Mutations Affecting the Glycine Receptor Agonist Transduction Mechanism Convert the Competitive Antagonist, Picrotoxin, into an Allosteric PotentiatorJ. Biol. Chem 270:13799–13806https://doi.org/10.1074/jbc.270.23.13799
- 54.The atypical M2 segment of the beta subunit confers picrotoxinin resistance to inhibitory glycine receptor channelsEMBO J 11:4305–4311https://doi.org/10.1002/j.1460-2075.1992.tb05529.x
- 55.Mechanisms for Picrotoxin Block of α2 Homomeric Glycine ReceptorsJ. Biol. Chem 281:3841–3855https://doi.org/10.1074/jbc.M511022200
- 56.Differential Distribution of Glycine Receptor Subtypes at the Rat Calyx of Held SynapseJ. Neurosci 32:17012–17024https://doi.org/10.1523/JNEUROSCI.1547-12.2012
- 57.Alpha2 subunit specificity of cyclothiazide inhibition on glycine receptorsMol. Pharmacol 73:1195–1202https://doi.org/10.1124/mol.107.042655
- 58.Inhibitory Glycine Receptors: An Update *J. Biol. Chem 287:40216–40223https://doi.org/10.1074/jbc.R112.408229
- 59.Native glycine receptor subtypes and their physiological rolesNeuropharmacology 56:303–309https://doi.org/10.1016/j.neuropharm.2008.07.034
- 60.Spontaneous IPSCs and glycine receptors with slow kinetics in wide-field amacrine cells in the mature rat retinaJ. Physiol 581:203–219https://doi.org/10.1113/jphysiol.2006.127316
- 61.A biophysical modelling platform of the cochlear nucleus and other auditory circuits: From channels to networksHear. Res 360:76–91https://doi.org/10.1016/j.heares.2017.12.017
- 62.Voltage-activated potassium channels in mammalian neurons and their block by novel pharmacological agentsGen. Pharmacol. Vasc. Syst 30:13–24https://doi.org/10.1016/S0306-3623(97)00034-7
- 63.Nonlinear interactions in a dendritic tree: localization, timing, and role in information processingProc. Natl. Acad. Sci. U. S. A 80:2799–2802
- 64.Diversity matters — extending sound intensity coding by inner hair cells via heterogeneous synapsesEMBO J 42https://doi.org/10.15252/embj.2023114587
- 65.Effects of continuous noise backgrounds on rate response of auditory nerve fibers in catJ. Neurophysiol 51:1326–1344https://doi.org/10.1152/jn.1984.51.6.1326
- 66.Onset Coding Is Degraded in Auditory Nerve Fibers from Mutant Mice Lacking Synaptic RibbonsJ. Neurosci 30:7587–7597https://doi.org/10.1523/JNEUROSCI.0389-10.2010
- 67.The Role of BKCa Channels in Electrical Signal Encoding in the Mammalian Auditory PeripheryJ. Neurosci 26:6181–6189https://doi.org/10.1523/JNEUROSCI.1047-06.2006
- 68.Contribution of auditory nerve fibers to compound action potential of the auditory nerveJ. Neurophysiol 112:1025–1039https://doi.org/10.1152/jn.00738.2013
- 69.Rate thresholds determine the precision of temporal integration in principal cells of the ventral cochlear nucleusHear. Res 216:52–63https://doi.org/10.1016/j.heares.2006.02.006
- 70.Calretinin-Expressing Synapses Show Improved Synaptic Efficacy with Reduced Asynchronous Release during High-Rate ActivityJ. Neurosci 42:2729–2742https://doi.org/10.1523/JNEUROSCI.1773-21.2022
- 71.Sound coding in the auditory nerve of gerbilsHear. Res 338:32–39https://doi.org/10.1016/j.heares.2016.05.006
- 72.Functional Inhibition in Direction-Selective Retinal Ganglion Cells: Spatiotemporal Extent and Intralaminar InteractionsJ. Neurophysiol 88:1026–1039https://doi.org/10.1152/jn.2002.88.2.1026
- 73.Species-specific wiring for direction selectivity in the mammalian retinaNature 535:105–110https://doi.org/10.1038/nature18609
- 74.Structured Dendritic Inhibition Supports Branch-Selective Integration in CA1 Pyramidal CellsNeuron 89:1016–1030https://doi.org/10.1016/j.neuron.2016.01.029
- 75.Principles governing the operation of synaptic inhibition in dendritesNeuron 75:330–341https://doi.org/10.1016/j.neuron.2012.05.015
- 76.An arithmetic rule for spatial summation of excitatory and inhibitory inputs in pyramidal neuronsProc. Natl. Acad. Sci. U. S. A 106:21906–21911https://doi.org/10.1073/pnas.0912022106
- 77.Whole-Neuron Synaptic Mapping Reveals Spatially Precise Excitatory/Inhibitory Balance Limiting Dendritic and Somatic SpikingNeuron 106:566–578https://doi.org/10.1016/j.neuron.2020.02.015
- 78.Location-Dependent Effects of Inhibition on Local Spiking in Pyramidal Neuron DendritesPLOS Comput. Biol 8https://doi.org/10.1371/journal.pcbi.1002550
- 79.Regulation of neuronal input transformations by tunable dendritic inhibitionNat. Neurosci 15:423–430https://doi.org/10.1038/nn.3024
- 80.Three classes of inhibitory amino acid terminals in the cochlear nucleus of the guinea pigJ. Comp. Neurol 373:11–26
- 81.The distribution of GABA and glycine immunostaining in the cochlear nucleus of the mustached bat (Pteronotus parnellii)Cell Tissue Res 287:487–506https://doi.org/10.1007/s004410050773
- 82.An atlas of glycine- and GABA-like immunoreactivity and colocalization in the cochlear nuclear complex of the guinea pigAnat. Embryol. (Berl 186:443–465
- 83.gamma-Aminobutyric acid and glycine in the baboon cochlear nuclei: an immunocytochemical colocalization study with reference to interspecies differences in inhibitory systemsJ. Comp. Neurol 369:497–519
- 84.Patterns of glutamate decarboxylase immunostaining in the feline cochlear nuclear complex studied with silver enhancement and electron microscopyJ. Comp. Neurol 262:375–401https://doi.org/10.1002/cne.902620305
- 85.Development of adult-type inhibitory glycine receptors in the central auditory system of ratsJ. Comp. Neurol 385:117–134https://doi.org/10.1002/(sici)1096-9861(19970818)385:1<117::aid-cne7>3.0.co;2-5
- 86.Expression of glycine receptor subunit mRNAs in the rat cochlear nucleusHear. Res 144:47–52https://doi.org/10.1016/s0378-5955(00)00044-7
- 87.Origins and targets of commissural connections between the cochlear nuclei in guinea pigsJ. Comp. Neurol 375:128–146
- 88.Localization of GABA immunoreactivity in the auditory brainstem of guinea pigsBrain Res 339:119–122https://doi.org/10.1016/0006-8993(85)90628-6
- 89.Identification of an inhibitory neuron subtype, the L-stellate cell of the cochlear nucleuseLife 9https://doi.org/10.7554/eLife.54350
- 90.Tuberculoventral Neurons Project to the Multipolar Cell Area But Not to the Octopus Cell Area of the Posteroventral Cochlear NucleusJournal of Comparative Neurology 313:457–468https://doi.org/10.1002/cne.903130306
- 91.Descending inputs to octopus cell area of the cat cochlear nucleus: An electron microscopic studyJ. Comp. Neurol 173:337–354https://doi.org/10.1002/cne.901730209
- 92.Octopus Cells in the Posteroventral Cochlear Nucleus Provide the Main Excitatory Input to the Superior Paraolivary NucleusFront. Neural Circuits 11
- 93.Divergent projections of physiologically characterized rat ventral cochlear nucleus neurons as shown by intra-axonal injection of horseradish peroxidaseExp. Brain Res 73:263–284https://doi.org/10.1007/BF00248219
- 94.Projections from the cochlear nucleus to the superior paraolivary nucleus in guinea pigsJ. Comp. Neurol 360:135–149https://doi.org/10.1002/cne.903600110
- 95.Projections from the posteroventral cochlear nucleus to the superior olivary complex in guinea pig: Light and em observations with the PHA-L methodJ. Comp. Neurol 311:495–508https://doi.org/10.1002/cne.903110405
- 96.Projections from the cochlear nuclei in the mustache bat, Pteronotus parnelliiJ. Comp. Neurol 237:307–324https://doi.org/10.1002/cne.902370303
- 97.Projections from octopus cells of the posteroventral cochlear nucleus to the ventral nucleus of the lateral lemniscus in cat and humanAudit. Neurosci 3:335–350
- 98.Large Somatic Synapses on Neurons in the Ventral Lateral Lemniscus Work in PairsJ. Neurosci 34:3237–3246https://doi.org/10.1523/JNEUROSCI.3664-13.2014
- 99.Powerful, Onset Inhibition in the Ventral Nucleus of the Lateral LemniscusJ. Neurophysiol 94:1651–1654https://doi.org/10.1152/jn.00167.2005
- 100.Ventral nucleus of the lateral lemniscus in guinea pigs: Cytoarchitecture and inputs from the cochlear nucleusJ. Comp. Neurol 379:363–385
- 101.Functional organization of ascending and descending connections of the cochlear nucleus of horseshoe batsJ. Comp. Neurol 292:373–395https://doi.org/10.1002/cne.902920305
- 102.Generation and analysis of an improved Foxg1-IRES-Cre driver mouse lineDev. Biol 412:139–147https://doi.org/10.1016/j.ydbio.2016.02.011
- 103.Dynamics of placodal lineage development revealed by targeted transgene expressionDev. Dyn 215:332–343
- 104.Foxg1 Is Required for Morphogenesis and Histogenesis of the Mammalian Inner EarDev. Dyn. Off. Publ. Am. Assoc. Anat 235:2470–2482https://doi.org/10.1002/dvdy.20839
- 105.Brain Factor-1 Controls the Proliferation and Differentiation of Neocortical Progenitor Cells through Independent MechanismsJ. Neurosci 22:6526–6536https://doi.org/10.1523/JNEUROSCI.22-15-06526.2002
- 106.Telencephalon-restricted expression of BF-1, a new member of the HNF-3/fork head gene family, in the developing rat brainNeuron 8:957–966https://doi.org/10.1016/0896-6273(92)90210-5
- 107.Dynamic FoxG1 expression coordinates the integration of multipolar pyramidal neuron precursors into the cortical plateNeuron 74:1045–1058https://doi.org/10.1016/j.neuron.2012.04.025
- 108.Efficient in vivo manipulation of mouse genomic sequences at the zygote stageProc. Natl. Acad. Sci. U. S. A 93:5860–5865https://doi.org/10.1073/pnas.93.12.5860
- 109.A glycine transporter 2-Cre knock-in mouse line for glycinergic neuron-specific gene manipulationIBRO Rep 3:9–16https://doi.org/10.1016/j.ibror.2017.07.002
- 110.Imaging Neuronal Subsets in Transgenic Mice Expressing Multiple Spectral Variants of GFPNeuron 28:41–51https://doi.org/10.1016/S0896-6273(00)00084-2
- 111.Usher type 1G protein sans is a critical component of the tip-link complex, a structure controlling actin polymerization in stereociliaProc. Natl. Acad. Sci. U. S. A 108:5825–5830https://doi.org/10.1073/pnas.1017114108
- 112.A robust and high-throughput Cre reporting and characterization system for the whole mouse brainNat. Neurosci 13:133–140https://doi.org/10.1038/nn.2467
- 113.Characterization of Nkx6-2-derived neocortical interneuron lineagesCereb. Cortex N. Y. N 1991 19:i1–i10https://doi.org/10.1093/cercor/bhp038
- 114.A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencingNat. Neurosci 15:793–802https://doi.org/10.1038/nn.3078
- 115.The effect of progressive hearing loss on the morphology of endbulbs of Held and bushy cellsHear. Res 343:14–33https://doi.org/10.1016/j.heares.2016.07.004
- 116.Advanced CUBIC tissue clearing for whole-organ cell profilingNat. Protoc 14:3506–3537https://doi.org/10.1038/s41596-019-0240-9
- 117.Advanced CUBIC protocols for whole-brain and whole-body clearing and imagingNat. Protoc 10:1709–1727https://doi.org/10.1038/nprot.2015.085
- 118.NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological DataFront. Neuroinformatics 12https://doi.org/10.3389/fninf.2018.00014
- 119.The NEURON simulation environmentNeural Comput 9:1179–1209https://doi.org/10.1162/neco.1997.9.6.1179
Article and author information
Version history
- Sent for peer review:
- Preprint posted:
- Reviewed Preprint version 1:
Copyright
© 2024, Kreeger et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.