Neural Implementation of Bayesian Inference in Population Codes
Abstract
This study investigates a population decoding paradigm, in which the estimation of stimulus in the previous step is used as prior knowledge for consecutive decoding. We analyze the decoding accu(cid:173) racy of such a Bayesian decoder (Maximum a Posteriori Estimate), and show that it can be implemented by a biologically plausible recurrent network, where the prior knowledge of stimulus is con(cid:173) veyed by the change in recurrent interactions as a result of Hebbian learning.
Cite
Text
Wu and Amari. "Neural Implementation of Bayesian Inference in Population Codes." Neural Information Processing Systems, 2001.Markdown
[Wu and Amari. "Neural Implementation of Bayesian Inference in Population Codes." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/wu2001neurips-neural/)BibTeX
@inproceedings{wu2001neurips-neural,
title = {{Neural Implementation of Bayesian Inference in Population Codes}},
author = {Wu, Si and Amari, Shun-ichi},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {317-323},
url = {https://mlanthology.org/neurips/2001/wu2001neurips-neural/}
}