The role of inhibition in a computational model of an auditory cortical neuron during the encoding of temporal information

A scalable spiking amygdala model that explains fear conditioning, extinction, renewal and generalization

The amygdala (AMY) is widely implicated in fear learning and fear behaviour, but it remains unclear how the many biological components present within AMY interact to achieve these abilities. Building on previous work, we hypothesize that individual AMY nuclei represent different quantities and that fear conditioning arises from error-driven learning on the synapses between AMY nuclei. We present a computational model of AMY that (a) recreates the divisions and connections between AMY nuclei and their constituent pyramidal and inhibitory neurons; (b) accommodates scalable high-dimensional representations of external stimuli; (c) learns to associate complex stimuli with the presence (or absence) of an aversive stimulus; (d) preserves feature information when mapping inputs to salience estimates, such that these estimates generalize to similar stimuli; and (e) induces a diverse profile of neural responses within each nucleus. Our model predicts (1) defensive responses and neural activities in several experimental conditions, (2) the consequence of artificially ablating particular nuclei and (3) the tendency to generalize defensive responses to novel stimuli. We test these predictions by comparing model outputs to neural and behavioural data from animals and humans. Despite the relative simplicity of our model, we find significant overlap between simulated and empirical data, which supports our claim that the model captures many of the neural mechanisms that support fear conditioning. We conclude by comparing our model to other computational models and by characterizing the theoretical relationship between pattern separation and fear generalization in healthy versus anxious individuals.

Quantitative models of auditory cortical processing

To generate insight from experimental data, it is critical to understand the inter-relationships between individual data points and place them in context within a structured framework. Quantitative modeling can provide the scaffolding for such an endeavor. Our main objective in this review is to provide a primer on the range of quantitative tools available to experimental auditory neuroscientists. Quantitative modeling is advantageous because it can provide a compact summary of observed data, make underlying assumptions explicit, and generate predictions for future experiments. Quantitative models may be developed to characterize or fit observed data, to test theories of how a task may be solved by neural circuits, to determine how observed biophysical details might contribute to measured activity patterns, or to predict how an experimental manipulation would affect neural activity. In complexity, quantitative models can range from those that are highly biophysically realistic and that include detailed simulations at the level of individual synapses, to those that use abstract and simplified neuron models to simulate entire networks. Here, we survey the landscape of recently developed models of auditory cortical processing, highlighting a small selection of models to demonstrate how they help generate insight into the mechanisms of auditory processing. We discuss examples ranging from models that use details of synaptic properties to explain the temporal pattern of cortical responses to those that use modern deep neural networks to gain insight into human fMRI data. We conclude by discussing a biologically realistic and interpretable model that our laboratory has developed to explore aspects of vocalization categorization in the auditory pathway.

Loss of oligodendrocyte ErbB receptor signaling leads to hypomyelination, reduced density of parvalbumin-expressing interneurons, and inhibitory function in the auditory cortex

For a long time, myelin was thought to be restricted to excitatory neurons, and studies on dysmyelination focused primarily on excitatory cells. Recent evidence showed that axons of inhibitory neurons in the neocortex are also myelinated, but the role of myelin on inhibitory circuits remains unknown. Here we studied the impact of mild hypomyelination on both excitatory and inhibitory connectivity in the primary auditory cortex (A1) with well-characterized mouse models of hypomyelination due to loss of oligodendrocyte ErbB receptor signaling. Using laser-scanning photostimulation, we found that mice with mild hypomyelination have reduced functional inhibitory connections to A1 L2/3 neurons without changes in excitatory connections, resulting in altered excitatory/inhibitory balance. These effects are not associated with altered expression of GABAergic and glutamatergic synaptic components, but with reduced density of parvalbumin-positive (PV⁺ ) neurons, axons, and synaptic terminals, which reflect reduced PV expression by interneurons rather than PV⁺ neuronal loss. While immunostaining shows that hypomyelination occurs in both PV⁺ and PV^- axons, there is a strong correlation between MBP and PV expression, suggesting that myelination influences PV expression. Together, the results indicate that mild hypomyelination impacts A1 neuronal networks, reducing inhibitory activity, and shifting networks towards excitation.

Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex

Studies of the encoding of sensory stimuli by the brain often consider recorded neurons as a pool of identical units. Here, we report divergence in stimulus-encoding properties between subpopulations of cortical neurons that are classified based on spike timing and waveform features. Neurons in auditory cortex of the awake marmoset (Callithrix jacchus) encode temporal information with either stimulus-synchronized or nonsynchronized responses. When we classified single-unit recordings using either a criteria-based or an unsupervised classification method into regular-spiking, fast-spiking, and bursting units, a subset of intrinsically bursting neurons formed the most highly synchronized group, with strong phase-locking to sinusoidal amplitude modulation (SAM) that extended well above 20 Hz. In contrast with other unit types, these bursting neurons fired primarily on the rising phase of SAM or the onset of unmodulated stimuli, and preferred rapid stimulus onset rates. Such differentiating behavior has been previously reported in bursting neuron models and may reflect specializations for detection of acoustic edges. These units responded to natural stimuli (vocalizations) with brief and precise spiking at particular time points that could be decoded with high temporal stringency. Regular-spiking units better reflected the shape of slow modulations and responded more selectively to vocalizations with overall firing rate increases. Population decoding using time-binned neural activity found that decoding behavior differed substantially between regular-spiking and bursting units. A relatively small pool of bursting units was sufficient to identify the stimulus with high accuracy in a manner that relied on the temporal pattern of responses. These unit type differences may contribute to parallel and complementary neural codes.

Neurons in auditory cortex show highly diverse responses to sounds. This study suggests that neuronal type inferred from baseline firing properties accounts for much of this diversity, with a subpopulation of bursting units being specialized for precise temporal encoding.

Impaired Subcortical Processing of Amplitude-Modulated Tones in Mice Deficient for Cacna2d3, a Risk Gene for Autism Spectrum Disorders in Humans

Temporal processing of complex sounds is a fundamental and complex task in hearing and a prerequisite for processing and understanding vocalization, speech, and prosody. Here, we studied response properties of neurons in the inferior colliculus (IC) in mice lacking Cacna2d3, a risk gene for autism spectrum disorders (ASDs). The α₂δ3 auxiliary Ca²⁺ channel subunit encoded by Cacna2d3 is essential for proper function of glutamatergic synapses in the auditory brainstem. Recent evidence has shown that much of auditory feature extraction is performed in the auditory brainstem and IC, including processing of amplitude modulation (AM). We determined both spectral and temporal properties of single- and multi-unit responses in the IC of anesthetized mice. IC units of α₂δ3^−/− mice showed normal tuning properties yet increased spontaneous rates compared with α₂δ3^+/+. When stimulated with AM tones, α₂δ3^−/− units exhibited less precise temporal coding and reduced evoked rates to higher modulation frequencies (f_m). Whereas first spike latencies (FSLs) were increased for only few modulation frequencies, population peak latencies were increased for f_m ranging from 20 to 100 Hz in α₂δ3^−/− IC units. The loss of precision of temporal coding with increasing f_m from 70 to 160 Hz was characterized using a normalized offset-corrected (Pearson-like) correlation coefficient, which appeared more appropriate than the metrics of vector strength. The processing deficits of AM sounds analyzed at the level of the IC indicate that α₂δ3^−/− mice exhibit a subcortical auditory processing disorder (APD). Similar deficits may be present in other mouse models for ASDs.

The role of adaptation in generating monotonic rate codes in auditory cortex

In primary auditory cortex, slowly repeated acoustic events are represented temporally by the stimulus-locked activity of single neurons. Single-unit studies in awake marmosets (Callithrix jacchus) have shown that a sub-population of these neurons also monotonically increase or decrease their average discharge rate during stimulus presentation for higher repetition rates. Building on a computational single-neuron model that generates stimulus-locked responses with stimulus evoked excitation followed by strong inhibition, we find that stimulus-evoked short-term depression is sufficient to produce synchronized monotonic positive and negative responses to slowly repeated stimuli. By exploring model robustness and comparing it to other models for adaptation to such stimuli, we conclude that short-term depression best explains our observations in single-unit recordings in awake marmosets. Together, our results show how a simple biophysical mechanism in single neurons can generate complementary neural codes for acoustic stimuli.

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

The interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. Here we present analytical methods to efficiently fit spiking circuit models to single-trial spike trains. Using derived likelihood functions, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Comprehensive evaluations on synthetic data, validations using ground truth in-vitro and in-vivo recordings, and comparisons with existing techniques demonstrate that parameter estimation is very accurate and efficient, even for highly subsampled networks. Our methods bridge statistical, data-driven and theoretical, model-based neurosciences at the level of spiking circuits, for the purpose of a quantitative, mechanistic interpretation of recorded neuronal population activity.

It is difficult to fit mechanistic, biophysically constrained circuit models to spike train data from in vivo extracellular recordings. Here the authors present analytical methods that enable efficient parameter estimation for integrate-and-fire circuit models and inference of the underlying connectivity structure in subsampled networks.

Spectral tuning of adaptation supports coding of sensory context in auditory cortex

Perception of vocalizations and other behaviorally relevant sounds requires integrating acoustic information over hundreds of milliseconds. Sound-evoked activity in auditory cortex typically has much shorter latency, but the acoustic context, i.e., sound history, can modulate sound evoked activity over longer periods. Contextual effects are attributed to modulatory phenomena, such as stimulus-specific adaption and contrast gain control. However, an encoding model that links context to natural sound processing has yet to be established. We tested whether a model in which spectrally tuned inputs undergo adaptation mimicking short-term synaptic plasticity (STP) can account for contextual effects during natural sound processing. Single-unit activity was recorded from primary auditory cortex of awake ferrets during presentation of noise with natural temporal dynamics and fully natural sounds. Encoding properties were characterized by a standard linear-nonlinear spectro-temporal receptive field (LN) model and variants that incorporated STP-like adaptation. In the adapting models, STP was applied either globally across all input spectral channels or locally to subsets of channels. For most neurons, models incorporating local STP predicted neural activity as well or better than LN and global STP models. The strength of nonlinear adaptation varied across neurons. Within neurons, adaptation was generally stronger for spectral channels with excitatory than inhibitory gain. Neurons showing improved STP model performance also tended to undergo stimulus-specific adaptation, suggesting a common mechanism for these phenomena. When STP models were compared between passive and active behavior conditions, response gain often changed, but average STP parameters were stable. Thus, spectrally and temporally heterogeneous adaptation, subserved by a mechanism with STP-like dynamics, may support representation of the complex spectro-temporal patterns that comprise natural sounds across wide-ranging sensory contexts.

The rough sound of salience enhances aversion through neural synchronisation

Being able to produce sounds that capture attention and elicit rapid reactions is the prime goal of communication. One strategy, exploited by alarm signals, consists in emitting fast but perceptible amplitude modulations in the roughness range (30-150 Hz). Here, we investigate the perceptual and neural mechanisms underlying aversion to such temporally salient sounds. By measuring subjective aversion to repetitive acoustic transients, we identify a nonlinear pattern of aversion restricted to the roughness range. Using human intracranial recordings, we show that rough sounds do not merely affect local auditory processes but instead synchronise large-scale, supramodal, salience-related networks in a steady-state, sustained manner. Rough sounds synchronise activity throughout superior temporal regions, subcortical and cortical limbic areas, and the frontal cortex, a network classically involved in aversion processing. This pattern correlates with subjective aversion in all these regions, consistent with the hypothesis that roughness enhances auditory aversion through spreading of neural synchronisation.

Computational Neural Modeling of Auditory Cortical Receptive Fields

Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cortical neurons is observed during behavioral tasks that require discrimination of particular sounds. This rapid task-related change is believed to be one of the processing strategies utilized by the auditory cortex to selectively attend to one stream of sound in the presence of mixed sounds. However, the mechanism by which the brain evokes this rapid plasticity in the auditory cortex remains unclear. This paper uses a neural network model to investigate how synaptic transmission within the cortical neuron network can change the receptive fields of individual neurons. A sound signal was used as input to a model of the cochlea and auditory periphery, which activated or inhibited integrate-and-fire neuron models to represent networks in the primary auditory cortex. Each neuron in the network was tuned to a different frequency. All neurons were interconnected with excitatory or inhibitory synapses of varying strengths. Action potentials in one of the model neurons were used to calculate the receptive field using reverse correlation. The results were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. The neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic weights in the model. The model predicts that altering synaptic drive between cortical neurons and/or bottom-up synaptic drive from the cochlear model to the cortical neurons can account for rapid task-related changes observed experimentally in A1 neurons. By identifying changes in the synaptic drive during behavioral tasks, the model provides insights into the neural mechanisms utilized by the auditory cortex to enhance the perception of behaviorally salient sounds.

Distinct Subthreshold Mechanisms Underlying Rate-Coding Principles in Primate Auditory Cortex

A key computational principle for encoding time-varying signals in auditory and somatosensory cortices of monkeys is the opponent model of rate coding by two distinct populations of neurons. However, the subthreshold mechanisms that give rise to this computation have not been revealed. Because the rate-coding neurons are only observed in awake conditions, it is especially challenging to probe their underlying cellular mechanisms. Using a novel intracellular recording technique that we developed in awake marmosets, we found that the two types of rate-coding neurons in auditory cortex exhibited distinct subthreshold responses. While the positive-monotonic neurons (monotonically increasing firing rate with increasing stimulus repetition frequency) displayed sustained depolarization at high repetition frequency, the negative-monotonic neurons (opposite trend) instead exhibited hyperpolarization at high repetition frequency but sustained depolarization at low repetition frequency. The combination of excitatory and inhibitory subthreshold events allows the cortex to represent time-varying signals through these two opponent neuronal populations.

Neuronal Mechanisms and Transformations Encoding Time-Varying Signals

Sensation in natural environments requires the analysis of time-varying signals. While previous work has uncovered how a signal's temporal rate is represented by neurons in sensory cortex, in this issue of Neuron, new evidence from Gao et al. (2016) provides insights on the underlying mechanisms.