Continuous neural network with windowed Hebbian learning

被引：0
|
作者：
M. Fotouhi
M. Heidari
M. Sharifitabar
机构：
[1] Sharif University of Technology,Department of Mathematical Sciences
[2] Sharif University of Technology,Department of Mechanical Engineering
[3] Institute for Research in Fundamental Sciences (IPM),School of Mathematics
来源：
Biological Cybernetics | 2015年 / 109卷
关键词：
Neural field; Continuous network; Bump; Traveling front; Delay equation; Existence; Stability; 35B35; 35C07; 45K05; 92B20; 92C20;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We introduce an extension of the classical neural field equation where the dynamics of the synaptic kernel satisfies the standard Hebbian type of learning (synaptic plasticity). Here, a continuous network in which changes in the weight kernel occurs in a specified time window is considered. A novelty of this model is that it admits synaptic weight decrease as well as the usual weight increase resulting from correlated activity. The resulting equation leads to a delay-type rate model for which the existence and stability of solutions such as the rest state, bumps, and traveling fronts are investigated. Some relations between the length of the time window and the bump width is derived. In addition, the effect of the delay parameter on the stability of solutions is shown. Also numerical simulations for solutions and their stability are presented.
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页码：321 / 332
页数：11
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