Year: 2025
Type: article
Abstract: Computational models of auditory processing can be valuable tools for research and technology development. Models of the cochlea are highly accurate and widely used, but models of the auditory brain lag far behind in both performance and penetration. Here we present ICNet, a convolutional encoder-decoder model of neural coding in the inferior colliculus. We developed ICNet using large-scale intracranial recordings from anaesthetized gerbils, addressing three key modelling challenges that are common across all sensory systems: capturing the full statistical structure of neuronal response patterns; accounting for physiological and experimental non-stationarity; and extracting features of sensory processing that are shared across different brains. ICNet provides highly accurate simulation of multi-unit neural responses to a wide range of complex sounds, including near-perfect responses to speech. It also reproduces key neurophysiological phenomena such as forward masking and dynamic range adaptation. ICNet can be used to simulate activity from thousands of neural units or to provide a compact representation of early central auditory processing through its latent dynamics, facilitating a wide range of hearing and audio applications. It can also serve as a foundation core, providing a baseline neural representation for models of active listening or higher-level auditory processing.
Source: Nature Machine Intelligence
Authors: Fotios Drakopoulos, Lloyd Pellatt, Shievanie Sabesan, Yiqing Xia, Andreas Fragner, Nicholas A. Lesica
Institution: University College London
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
Awards: G1587164963, G2640609433, Characterizing the effects of hearing loss and hearing aids on the neural code for music, G423163525, G4580141385, Transforming hearing aids through large-scale electrophysiology and deep learning, G755048420, 22-BBSRC/NSF-BIO - Interpretable & Noise-robust Machine Learning for Neurophysiology
OpenAlex ID: W4414335759