STATE GENERATORS AND COMPLEX NEURAL MEMORIES

被引：5
作者：
KAK, SC
机构：
[1] Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, 70803-5901, LA
来源：
PRAMANA-JOURNAL OF PHYSICS | 1992年 / 38卷 / 03期
关键词：
NEURAL NETWORKS; NONLINEAR SYSTEMS; ITERATED MAPS;
D O I：
10.1007/BF02875373
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
The mechanism of self-indexing for feedback neural networks that generates memories from short subsequences is generalized so that a single bit together with an appropriate update order suffices for each memory. This mechanism explains how stimulating an appropriate neuron can recall a memory. Although information is distributed in this model, yet our self-indexing mechanism makes it appear localized. Also a new complex valued neuron model is presented to generalize McCulloch-Pitts neurons.
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页码：271 / 278
页数：8
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