A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses DOI Creative Commons
Ulysse Rançon, Timothée Masquelier, Benoit R. Cottereau

et al.

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(8), P. e1012288 - e1012288

Published: Aug. 2, 2024

Sounds are temporal stimuli decomposed into numerous elementary components by the auditory nervous system. For instance, a to spectro-temporal transformation modelling frequency decomposition performed cochlea is widely adopted first processing step in today’s computational models of neural responses. Similarly, increments and decrements sound intensity (i.e., raw waveform itself or its spectral bands) constitute critical features code, with high behavioural significance. However, despite growing attention scientific community on OFF responses, their relationship transient ON, sustained responses adaptation remains unclear. In this context, we propose new general model, based pair linear filters, named AdapTrans , that captures both ON unifying easy expand framework. We demonstrate filtering audio cochleagrams permits accurately render known properties measured different mammal species such as dependence stimulus fall time preceding duration. Furthermore, integrating our framework gold standard state-of-the-art machine learning predict from stimuli, following supervised training large compilation electrophysiology datasets (ready-to-deploy PyTorch pre-processed shared publicly), show systematically improves prediction accuracy estimated within cortical areas rat ferret brain. Together, these results motivate use for systems neuroscientists willing increase plausibility performances audition.

Language: Английский

A general theoretical framework unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses DOI Creative Commons
Ulysse Rançon, Timothée Masquelier, Benoit R. Cottereau

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 20, 2024

Sounds are temporal stimuli decomposed into numerous elementary components by the auditory nervous system. For instance, a to spectro-temporal transformation modelling frequency decomposition performed cochlea is widely adopted first processing step in today's computational models of neural responses. Similarly, increments and decrements sound intensity (i.e., raw waveform itself or its spectral bands) constitute critical features code, with high behavioural significance. However, despite growing attention scientific community on OFF responses, their relationship transient ON, sustained responses adaptation remains unclear. In this context, we propose new general model, based pair linear filters, named "AdapTrans" that captures both ON unifying easy expand framework. We demonstrate filtering audio cochleagrams AdapTrans permits accurately render known properties measured different mammal species such as dependence stimulus fall time preceding duration. Furthermore, integrating our framework gold standard state-of-the-art machine learning predict from stimuli, following supervised training large compilation electrophysiology datasets (ready-to-deploy PyTorch pre-processed shared publicly), show systematically improves prediction accuracy estimated within cortical areas rat ferret brain. Together, these results motivate use for systems neuroscientists willing increase plausibility performances audition.

Language: Английский

Citations

0
A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses DOI Creative Commons
Ulysse Rançon, Timothée Masquelier, Benoit R. Cottereau

et al.

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(8), P. e1012288 - e1012288

Published: Aug. 2, 2024

Sounds are temporal stimuli decomposed into numerous elementary components by the auditory nervous system. For instance, a to spectro-temporal transformation modelling frequency decomposition performed cochlea is widely adopted first processing step in today’s computational models of neural responses. Similarly, increments and decrements sound intensity (i.e., raw waveform itself or its spectral bands) constitute critical features code, with high behavioural significance. However, despite growing attention scientific community on OFF responses, their relationship transient ON, sustained responses adaptation remains unclear. In this context, we propose new general model, based pair linear filters, named AdapTrans , that captures both ON unifying easy expand framework. We demonstrate filtering audio cochleagrams permits accurately render known properties measured different mammal species such as dependence stimulus fall time preceding duration. Furthermore, integrating our framework gold standard state-of-the-art machine learning predict from stimuli, following supervised training large compilation electrophysiology datasets (ready-to-deploy PyTorch pre-processed shared publicly), show systematically improves prediction accuracy estimated within cortical areas rat ferret brain. Together, these results motivate use for systems neuroscientists willing increase plausibility performances audition.

Language: Английский

Citations

0