Biology subjects

Willmore, B. D. B.

Publications and source records attributed to Willmore, B. D. B..

6 recordsLinked to original sources

bioRxiv2025

Auditory midbrain encodes training-induced plasticity in soundlocalization behavior

The ability of the brain to learn from experience and to compensate for changes in sensory inputs is usually associated with plasticity in the cerebral cortex. Descending corticofugal pathways have been implicated in learning but there is limited evidence that subcortical processing is shaped by training. We show that sound-source location can be accurately decoded from neuronal populations recorded in the inferior colliculus of one or both hemispheres while ferrets perform a localization task. Furthermore, changes in neural decoding performance matched improvements in localization accuracy of individual animals when ferrets were trained to adapt to abnormal spatial cues resulting from reversible occlusion of one ear. These findings demonstrate that the activity patterns of populations of neurons in the inferior colliculus can account for sound localization behavior in different hearing contexts, and that training-dependent plasticity in the auditory midbrain may support spatial learning following monaural hearing loss.

neuroscience↗

bioRxiv2023

Cochlear tuning characteristics arise from temporal prediction of natural sounds

The cochlea decomposes incoming sound waveforms into different frequency components along the length of its basilar membrane. The receptor hair cells at the apical end of this resonant membrane are tuned to the lowest sound frequencies, with the preferred sound frequency of hair cell tuning increasing near-exponentially along the length of the membrane towards its basal end. This frequency composition of the sound is then transmitted to the brain by the auditory nerve fibers that innervate the inner hair cells. Hair cells respond to a sound impulse with a temporally asymmetric envelope and the sharpness of their tuning changes as the frequency to which they are most sensitive varies with their position along the basilar membrane. We ask if there is a normative explanation for why the cochlea decomposes sounds in this manner. Inspired by findings in the retina, we propose that cochlear tuning properties may be optimized for temporal prediction. This principle states that the sensory features represented by neurons are optimized to predict immediate future input from recent past input. We show that an artificial neural network optimized for temporal prediction of the immediate future of raw waveforms of natural sounds from their recent past produces tuning properties that resemble those observed in the auditory nerve. Specifically, the model captures the temporally asymmetric impulse responses, the tonotopic distribution and variation in tuning sharpness along the cochlea, and the frequency glide polarity of the impulse responses. These characteristics are not captured by a similar model optimized for compression of the sound waveform, rather than prediction. Given its success in accounting for the tuning properties at various processing levels in the auditory and visual systems, this finding for the cochlea provides further evidence that temporal prediction may be a general principle of sensory processing.

neuroscience↗

bioRxiv2019

Simple spectral transformations capture the contribution of peripheral processing to cortical responses to natural sounds

Processing in the sensory periphery involves various mechanisms that enable the detection and discrimination of sensory information. Despite their biological complexity, could these processing steps sub-serve a relatively simple transformation of sensory inputs, which are then transmitted to the CNS? Here we explored both biologically-detailed and very simple models of the auditory periphery to find the appropriate input to a phenomenological model of auditory cortical responses to natural sounds. We examined a range of cochlear models, from those involving detailed biophysical characteristics of the cochlea and auditory nerve to very pared-down spectrogram-like approximations of the information processing in these structures. We tested the capacity of these models to predict the time-course of single-unit neural responses recorded in the ferret primary auditory cortex, when combined with a linear non-linear encoding model. We show that a simple model based on a log-spaced, log-scaled power spectrogram with Hill-function compression performs as well as biophysically-detailed models of the cochlea and the auditory nerve. These findings emphasize the value of using appropriate simple models of the periphery when building encoding models of sensory processing in the brain, and imply that the complex properties of the auditory periphery may together result in a simpler than expected functional transformation of the inputs.

neuroscience↗

bioRxiv2019

Auditory contrast gain control predicts perceptual performance and is not dependent on cortical activity

Neural adaptation enables sensory information to be represented optimally in the brain despite large fluctuations over time in the statistics of the environment. Auditory contrast gain control represents an important example, which is thought to arise primarily from cortical processing. We find, however, that neurons in both the auditory thalamus and midbrain of mice show robust contrast gain control, and that this is implemented independently of cortical activity. Although neurons at each level exhibit contrast gain control to similar degrees, adaptation time constants become longer at later stages of the processing hierarchy, resulting in progressively more stable representations. We also show that auditory discrimination thresholds in human listeners compensate for changes in contrast, and that the strength of this perceptual adaptation can be predicted from physiological measurements. Contrast adaptation is therefore a robust property of both the subcortical and cortical auditory system and accounts for the short-term adaptability of perceptual judgments.

neuroscience↗

bioRxiv2018

The DeepTune framework for modeling and characterizing neurons in visual cortex area V4

Deep neural network models have recently been shown to be effective in predicting single neuron responses in primate visual cortex areas V4. Despite their high predictive accuracy, these models are generally difficult to interpret. This limits their applicability in characterizing V4 neuron function. Here, we propose the DeepTune framework as a way to elicit interpretations of deep neural network-based models of single neurons in area V4. V4 is a midtier visual cortical area in the ventral visual pathway. Its functional role is not yet well understood. Using a dataset of recordings of 71 V4 neurons stimulated with thousands of static natural images, we build an ensemble of 18 neural network-based models per neuron that accurately predict its response given a stimulus image. To interpret and visualize these models, we use a stability criterion to form optimal stimuli (DeepTune images) by pooling the 18 models together. These DeepTune images not only confirm previous findings on the presence of diverse shape and texture tuning in area V4, but also provide rich, concrete and naturalistic characterization of receptive fields of individual V4 neurons. The population analysis of DeepTune images for 71 neurons reveals how different types of curvature tuning are distributed in V4. In addition, it also suggests strong suppressive tuning for nearly half of the V4 neurons. Though we focus exclusively on the area V4, the DeepTune framework could be applied more generally to enhance the understanding of other visual cortex areas.

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bioRxiv2018

Exponentially-decaying temporal integration in a network model can explain much of the neural sensitivity to stimulus history in primary auditory cortex.

Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by dynamical aspects of neurons. We constructed a neural-network model whose output is the weighted sum of units whose responses are determined by a dynamic firing-rate equation. The dynamic aspect performs low-pass filtering on each units response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this dynamic network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.\n\nAUTHOR SUMMARYThe responses of neurons in the primary auditory cortex depend on the recent history of sounds over seconds or less. Typically, this dependence on the past has been modelled by applying a wide span of time delays to the input, although this is likely to be biologically unrealistic. Real neurons integrate the history of their activity due to the dynamical properties of their cell membranes and other components. We show that a network with a realistically narrow span of delays and with units having dynamic characteristics like those found in neurons, succinctly models neural responses recorded from ferret primary auditory cortex. Because these integrative properties are widespread, our dynamic network provides a basis for modelling responses in other neural systems.

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