Computational Differences Between Asymmetrical and Symmetrical Networks
Abstract
Symmetrically connected recurrent networks have recently been used as models of a host of neural computations. However, be(cid:173) cause of the separation between excitation and inhibition, biolog(cid:173) ical neural networks are asymmetrical. We study characteristic differences between asymmetrical networks and their symmetri(cid:173) cal counterparts, showing that they have dramatically different dynamical behavior and also how the differences can be exploited for computational ends. We illustrate our results in the case of a network that is a selective amplifier.
Cite
Text
Li and Dayan. "Computational Differences Between Asymmetrical and Symmetrical Networks." Neural Information Processing Systems, 1998.Markdown
[Li and Dayan. "Computational Differences Between Asymmetrical and Symmetrical Networks." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/li1998neurips-computational/)BibTeX
@inproceedings{li1998neurips-computational,
title = {{Computational Differences Between Asymmetrical and Symmetrical Networks}},
author = {Li, Zhaoping and Dayan, Peter},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {274-280},
url = {https://mlanthology.org/neurips/1998/li1998neurips-computational/}
}