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Adaptive Efficient Coding: A Variational Auto-encoder Approach

View ORCID ProfileGuy Aridor, Francesco Grechi, View ORCID ProfileMichael Woodford
doi: https://doi.org/10.1101/2020.05.29.124453
Guy Aridor
1Columbia University,
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  • For correspondence: garidor@gmail.com ga2449@columbia.edu
Francesco Grechi
2Columbia University,
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  • For correspondence: fg2403@columbia.edu
Michael Woodford
3Columbia University,
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  • For correspondence: mw2230@columbia.edu
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Abstract

We study a model of neural coding with the structure of a variational auto-encoder. The model posits that the encoding of individual stimulus values is optimally adjusted for a finite training sample of stimuli retained in memory. We demonstrate that this model can rationalize existing experimental evidence on both perceptual discrimination thresholds and neural tuning curve widths in multiple sensory domains. Finally, since our model implies that encoding is optimized for a sample from the environment, it also provides predictions about the adaptation of neural coding as the environmental frequency distribution changes.

Competing Interest Statement

The authors have declared no competing interest.

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Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted May 31, 2020.
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Adaptive Efficient Coding: A Variational Auto-encoder Approach
Guy Aridor, Francesco Grechi, Michael Woodford
bioRxiv 2020.05.29.124453; doi: https://doi.org/10.1101/2020.05.29.124453
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Adaptive Efficient Coding: A Variational Auto-encoder Approach
Guy Aridor, Francesco Grechi, Michael Woodford
bioRxiv 2020.05.29.124453; doi: https://doi.org/10.1101/2020.05.29.124453

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