RT Journal Article
SR Electronic
T1 Adaptive Efficient Coding: A Variational Auto-encoder Approach
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.29.124453
DO 10.1101/2020.05.29.124453
A1 Aridor, Guy
A1 Grechi, Francesco
A1 Woodford, Michael
YR 2020
UL http://biorxiv.org/content/early/2020/05/31/2020.05.29.124453.abstract
AB 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 StatementThe authors have declared no competing interest.