Biology subjects

Willmore, B.

Publications and source records attributed to Willmore, B..

3 recordsLinked to original sources

bioRxiv2025

Corticofugal gated recurrency captures auditory cortical responses

Memory is essential for the neural processing of natural sounds. It has been proposed that cortical memory is subserved by gated recurrency, a powerful machine learning method that enables memory of sequential dependencies. However, standard forms lack biological realism. We built a computational model using a subtractive form of gated recurrency, consistent with cortical circuitry and feedback that resembles corticofugal projections. This architecture outperformed state-of-the-art models with delay lines or non-biological gated recurrency at predicting auditory cortical responses to natural sounds. Its performance was further improved by incorporating a network layer that mirrors the high neuronal density in superficial cortical layers. Analyzing the model, we found that memory retention is longer in secondary than in primary auditory cortex, and that gated recurrency particularly helps capture responses during abrupt changes and quiet periods in the sounds. This work lays out the fundamental functional circuitry of the auditory pathway for processing the sequential dependencies in natural sounds.

neuroscience↗

bioRxiv2021

Cortical adaptation to sound reverberation

In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation. Our brains usually cope well with reverberation, allowing us to recognize sound sources regardless of their environments. In contrast, reverberation can cause severe difficulties for speech recognition algorithms and hearing-impaired people. The present study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant counterparts. The model neurons achieved this by extending the inhibitory component of their receptive filters for more reverberant spaces, and did so in a frequency-dependent manner. These predicted effects were observed in the responses of auditory cortical neurons of ferrets in the same simulated reverberant environments. Together, these results suggest that auditory cortical neurons adapt to reverberation by adjusting their filtering properties in a manner consistent with dereverberation.

neuroscience↗

bioRxiv2017

Sensory cortex is optimised for prediction of future input

Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimised to represent features in the recent past of sensory input that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few video or audio frames in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, in their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields tended to resemble those in the brain. This suggests that sensory processing is optimised to extract those features with the most capacity to predict future input.\n\nImpact statementPrediction of future input explains diverse neural tuning properties in sensory cortex.

neuroscience↗