Strategies to associate memories by unsupervised learning in neural networks

被引：0

作者：

Agnes, E. J. ^{[1
]}

Mizusaki, B. E. P. ^{[1
]}

Erichsen, R., Jr. ^{[1
]}

Brunnet, L. G. ^{[1
]}

机构：

[1] Univ Fed Rio Grande do Sul, Inst Fis, BR-90046900 Porto Alegre, RS, Brazil

来源：

PHYSICS, COMPUTATION, AND THE MIND - ADVANCES AND CHALLENGES AT INTERFACES | 2013年 / 1510卷

关键词：

unsupervised learning; spiking neurons; homeostasis; STDP; MODEL;

D O I：

10.1063/1.4776533

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this work we study the effects of three different strategies to associate memories in a neural network composed by both excitatory and inhibitory spiking neurons, which are randomly connected through recurrent excitatory and inhibitory synapses. The system is intended to store a number of memories, associated to spatial external inputs. The strategies consist in the presentation of the input patterns through trials in: i) ordered sequence; ii) random sequence; iii) clustered sequences. In addition, an order parameter indicating the correlation between the trials' activities is introduced to compute associative memory capacities and the quality of memory retrieval.

引用

页码：255 / 257

页数：3

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