Receptive field for dorsal cochlear nucleus neurons at multiple sound levels

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.

Linear coding of complex sound spectra by discharge rate in neurons of the medial nucleus of the trapezoid body (MNTB) and its inputs

The interaural level difference (ILD) cue to sound location is first encoded in the lateral superior olive (LSO). ILD sensitivity results because the LSO receives excitatory input from the ipsilateral cochlear nucleus and inhibitory input indirectly from the contralateral cochlear nucleus via glycinergic neurons of the ipsilateral medial nucleus of the trapezoid body (MNTB). It is hypothesized that in order for LSO neurons to encode ILDs, the sound spectra at both ears must be accurately encoded via spike rate by their afferents. This spectral-coding hypothesis has not been directly tested in MNTB, likely because MNTB neurons have been mostly described and studied recently in regards to their abilities to encode temporal aspects of sounds, not spectral. Here, we test the hypothesis that MNTB neurons and their inputs from the cochlear nucleus and auditory nerve code sound spectra via discharge rate. The Random Spectral Shape (RSS) method was used to estimate how the levels of 100-ms duration spectrally stationary stimuli were weighted, both linearly and non-linearly, across a wide band of frequencies. In general, MNTB neurons, and their globular bushy cell inputs, were found to be well-modeled by a linear weighting of spectra demonstrating that the pathways through the MNTB can accurately encode sound spectra including those resulting from the acoustical cues to sound location provided by head-related directional transfer functions (DTFs). Together with the anatomical and biophysical specializations for timing in the MNTB-LSO complex, these mechanisms may allow ILDs to be computed for complex stimuli with rapid spectrotemporally-modulated envelopes such as speech and animal vocalizations and moving sound sources.

Nonlinear temporal receptive fields of neurons in the dorsal cochlear nucleus

Studies of the dorsal cochlear nucleus (DCN) have focused on spectral processing because of the complex spectral receptive fields of the DCN. However, temporal fluctuations in natural signals convey important information, including information about moving sound sources or movements of the external ear in animals like cats. Here, we investigate the temporal filtering properties of DCN principal neurons through the use of temporal weighting functions that allow flexible analysis of nonlinearities and time variation in temporal response properties. First-order temporal receptive fields derived from the neurons are sufficient to characterize their response properties to low-contrast (3-dB standard deviation) stimuli. Larger contrasts require the second-order terms. Allowing temporal variation of the parameters of the first-order model or adding a component representing refractoriness improves predictions by the model by relatively small amounts. The importance of second-order components of the model is shown through simulations of nonlinear envelope synchronization behavior across sound level. The temporal model can be combined with a spectral model to predict tuning to the speed and direction of moving sounds.

Adaptive stimulus optimization for sensory systems neuroscience

In this paper, we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical optimal stimulus paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the iso-response paradigm which finds sets of stimuli giving rise to constant responses, and the system identification paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of non-linear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify non-linear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and toward a new paradigm of real-time model estimation and comparison.

Frequency response areas in the inferior colliculus: nonlinearity and binaural interaction

The tuning, binaural properties, and encoding characteristics of neurons in the central nucleus of the inferior colliculus (CNIC) were investigated to shed light on nonlinearities in the responses of these neurons. Results were analyzed for three types of neurons (I, O, and V) in the CNIC of decerebrate cats. Rate responses to binaural stimuli were characterized using a 1st- plus 2nd-order spectral integration model. Parameters of the model were derived using broadband stimuli with random spectral shapes (RSS). This method revealed four characteristics of CNIC neurons: (1) Tuning curves derived from broadband stimuli have fixed (i. e., level tolerant) bandwidths across a 50-60 dB range of sound levels; (2) 1st-order contralateral weights (particularly for type I and O neurons) were usually larger in magnitude than corresponding ipsilateral weights; (3) contralateral weights were more important than ipsilateral weights when using the model to predict responses to untrained noise stimuli; and (4) 2nd-order weight functions demonstrate frequency selectivity different from that of 1st-order weight functions. Furthermore, while the inclusion of 2nd-order terms in the model usually improved response predictions related to untrained RSS stimuli, they had limited impact on predictions related to other forms of filtered broadband noise [e. g., virtual-space stimuli (VS)]. The accuracy of the predictions varied considerably by response type. Predictions were most accurate for I neurons, and less accurate for O and V neurons, except at the lowest stimulus levels. These differences in prediction performance support the idea that type I, O, and V neurons encode different aspects of the stimulus: while type I neurons are most capable of producing linear representations of spectral shape, type O and V neurons may encode spectral features or temporal stimulus properties in a manner not easily explained with the low-order model. Supported by NIH grant DC00115.

Two-dimensional adaptation in the auditory forebrain

Sensory neurons exhibit two universal properties: sensitivity to multiple stimulus dimensions, and adaptation to stimulus statistics. How adaptation affects encoding along primary dimensions is well characterized for most sensory pathways, but if and how it affects secondary dimensions is less clear. We studied these effects for neurons in the avian equivalent of primary auditory cortex, responding to temporally modulated sounds. We showed that the firing rate of single neurons in field L was affected by at least two components of the time-varying sound log-amplitude. When overall sound amplitude was low, neural responses were based on nonlinear combinations of the mean log-amplitude and its rate of change (first time differential). At high mean sound amplitude, the two relevant stimulus features became the first and second time derivatives of the sound log-amplitude. Thus a strikingly systematic relationship between dimensions was conserved across changes in stimulus intensity, whereby one of the relevant dimensions approximated the time differential of the other dimension. In contrast to stimulus mean, increases in stimulus variance did not change relevant dimensions, but selectively increased the contribution of the second dimension to neural firing, illustrating a new adaptive behavior enabled by multidimensional encoding. Finally, we demonstrated theoretically that inclusion of time differentials as additional stimulus features, as seen so prominently in the single-neuron responses studied here, is a useful strategy for encoding naturalistic stimuli, because it can lower the necessary sampling rate while maintaining the robustness of stimulus reconstruction to correlated noise.

Active data collection for efficient estimation and comparison of nonlinear neural models

The stimulus-response relationship of many sensory neurons is nonlinear, but fully quantifying this relationship by a complex nonlinear model may require too much data to be experimentally tractable. Here we present a theoretical study of a general two-stage computational method that may help to significantly reduce the number of stimuli needed to obtain an accurate mathematical description of nonlinear neural responses. Our method of active data collection first adaptively generates stimuli that are optimal for estimating the parameters of competing nonlinear models and then uses these estimates to generate stimuli online that are optimal for discriminating these models. We applied our method to simple hierarchical circuit models, including nonlinear networks built on the spatiotemporal or spectral-temporal receptive fields, and confirmed that collecting data using our two-stage adaptive algorithm was far more effective for estimating and comparing competing nonlinear sensory processing models than standard nonadaptive methods using random stimuli.

The effect of supplemental dietary taurine on tinnitus and auditory discrimination in an animal model

Loss of central inhibition has been hypothesized to underpin tinnitus and impact auditory acuity. Taurine, a partial agonist at inhibitory glycine and γ-amino butyric acid receptors, was added to the daily diet of rats to examine its effects on chronic tinnitus and normal auditory discrimination. Eight rats were unilaterally exposed once to a loud sound to induce tinnitus. The rats were trained and tested in an operant task shown to be sensitive to tinnitus. An equivalent unexposed control group was run in parallel. Months after exposure, 6 of the exposed rats showed significant evidence of chronic tinnitus. Two concentrations of taurine in drinking water were given over several weeks (attaining average daily doses of 67 mg/kg and 294 mg/kg). Water consumption was unaffected. Three main effects were obtained: (1) The high taurine dose significantly attenuated tinnitus, which returned to near pre-treatment levels following washout. (2) Auditory discrimination was significantly improved in unexposed control rats at both doses. (3) As indicated by lever pressing, taurine at both doses had a significant group-equivalent stimulant effect. These results are consistent with the hypothesis that taurine attenuates tinnitus and improves auditory discrimination by increasing inhibitory tone and decreasing noise in the auditory pathway.

Context dependence of spectro-temporal receptive fields with implications for neural coding

The spectro-temporal receptive field (STRF) is frequently used to characterize the linear frequency-time filter properties of the auditory system up to the neuron recorded from. STRFs are extremely stimulus dependent, reflecting the strong non-linearities in the auditory system. Changes in the STRF with stimulus type (tonal, noise-like, vocalizations), sound level and spectro-temporal sound density are reviewed here. Effects on STRF shape of task and attention are also briefly reviewed. Models to account for these changes, potential improvements to STRF analysis, and implications for neural coding are discussed.

Multilinear models of single cell responses in the medial nucleus of the trapezoid body

The representation of acoustic stimuli in the brainstem forms the basis for higher auditory processing. While some characteristics of this representation (e.g. tuning curve) are widely accepted, it remains a challenge to predict the firing rate at high temporal resolution in response to complex stimuli. In this study we explore models for in vivo, single cell responses in the medial nucleus of the trapezoid body (MNTB) under complex sound stimulation. We estimate a family of models, the multilinear models, encompassing the classical spectrotemporal receptive field and allowing arbitrary input-nonlinearities and certain multiplicative interactions between sound energy and its short-term auditory context. We compare these to models of more traditional type, and also evaluate their performance under various stimulus representations. Using the context model, 75% of the explainable variance could be predicted based on a cochlear-like, gamma-tone stimulus representation. The presence of multiplicative contextual interactions strongly reduces certain inhibitory/suppressive regions of the linear kernels, suggesting an underlying nonlinear mechanism, e.g. cochlear or synaptic suppression, as the source of the suppression in MNTB neuronal responses. In conclusion, the context model provides a rich and still interpretable extension over many previous phenomenological models for modeling responses in the auditory brainstem at submillisecond resolution.

Dynamic spectrotemporal feature selectivity in the auditory midbrain

The transformation of auditory information from the cochlea to the cortex is a highly nonlinear process. Studies using tone stimuli have revealed that changes in even the most basic parameters of the auditory stimulus can alter neural response properties; for example, a change in stimulus intensity can cause a shift in a neuron's preferred frequency. However, it is not yet clear how such nonlinearities contribute to the processing of spectrotemporal features in complex sounds. Here, we use spectrotemporal receptive fields (STRFs) to characterize the effects of stimulus intensity on feature selectivity in the mammalian inferior colliculus (IC). At low intensities, we find that STRFs are relatively simple, typically consisting of a single excitatory region, indicating that the neural response is simply a reflection of the stimulus amplitude at the preferred frequency. In contrast, we find that STRFs at high intensities typically consist of a combination of an excitatory region and one or more inhibitory regions, often in a spectrotemporally inseparable arrangement, indicating selectivity for complex auditory features. We show that a linear-nonlinear model with the appropriate STRF can predict neural responses to stimuli with a fixed intensity, and we demonstrate that a simple extension of the model with an intensity-dependent STRF can predict responses to stimuli with varying intensity. These results illustrate the complexity of auditory feature selectivity in the IC, but also provide encouraging evidence that the prediction of nonlinear responses to complex stimuli is a tractable problem.