PATTERN-RECOGNITION, CHAOS, AND MULTIPLICITY IN NEURAL NETWORKS OF EXCITABLE SYSTEMS

被引：43
|
作者：
HJELMFELT, A
ROSS, J
机构：
[1] Department of Chemistry, Stanford University, Stanford
来源：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 1994年 / 91卷 / 01期
关键词：
FITZHUGH NEURONS; TRANSIENT SOLUTIONS; ASSOCIATIVE MEMORY; PERIODIC PERTURBATIONS;
D O I：
10.1073/pnas.91.1.63
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
We study a neural network composed of excitable FitzHugh neurons that interact by diffusive type connections. Patterns of neural activity may be stored by a Hebbian rule. The stored patterns are recalled and given by the transient activity of the neurons after the network has been perturbed by related patterns and relaxes back to its steady state. Periodic perturbations of the network are repeated requests for computations and result in simple periodic, complex periodic, and chaotic responses and corresponding computational performances.
引用
收藏
页码：63 / 67
页数：5
相关论文
共 50 条
12345