PT  - JOURNAL ARTICLE
AU  - Guy Aridor
AU  - Francesco Grechi
AU  - Michael Woodford
TI  - Adaptive Efficient Coding: A Variational Auto-encoder Approach
AID  - 10.1101/2020.05.29.124453
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.05.29.124453
4099  - http://biorxiv.org/content/early/2020/05/31/2020.05.29.124453.short
4100  - http://biorxiv.org/content/early/2020/05/31/2020.05.29.124453.full
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.