Learning Winner-Take-All Competition Between Groups of Neurons in Lateral Inhibitory Networks
Abstract
It has long been known that lateral inhibition in neural networks can lead to a winner-take-all competition, so that only a single neuron is active at a steady state. Here we show how to organize lateral inhibition so that groups of neurons compete to be active. Given a collection of poten(cid:173) tially overlapping groups, the inhibitory connectivity is set by a formula that can be interpreted as arising from a simple learning rule. Our analy(cid:173) sis demonstrates that such inhibition generally results in winner-take-all competition between the given groups, with the exception of some de(cid:173) generate cases. In a broader context, the network serves as a particular illustration of the general distinction between permitted and forbidden sets, which was introduced recently. From this viewpoint, the computa(cid:173) tional function of our network is to store and retrieve memories as per(cid:173) mitted sets of coactive neurons.
Cite
Text
Xie et al. "Learning Winner-Take-All Competition Between Groups of Neurons in Lateral Inhibitory Networks." Neural Information Processing Systems, 2000.Markdown
[Xie et al. "Learning Winner-Take-All Competition Between Groups of Neurons in Lateral Inhibitory Networks." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/xie2000neurips-learning/)BibTeX
@inproceedings{xie2000neurips-learning,
title = {{Learning Winner-Take-All Competition Between Groups of Neurons in Lateral Inhibitory Networks}},
author = {Xie, Xiaohui and Hahnloser, Richard H. R. and Seung, H. Sebastian},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {350-356},
url = {https://mlanthology.org/neurips/2000/xie2000neurips-learning/}
}