Cooperation in self-organizing map networks enhances information transmission in the presence of input background activity

被引:0
|
作者
Maxim Raginsky
Thomas J. Anastasio
机构
[1] University of Illinois,Beckman Institute for Advanced Science and Technology
[2] Duke University,Department of Electrical and Computer Engineering
[3] University of Illinois,Beckman Institute for Advanced Science and Technology, Department of Molecular and Integrative Physiology
来源
Biological Cybernetics | 2008年 / 98卷
关键词
Mutual Information; Superior Colliculus; Channel Capacity; Neighborhood Size; Output Unit;
D O I
暂无
中图分类号
学科分类号
摘要
The self-organizing map (SOM) algorithm produces artificial neural maps by simulating competition and cooperation among neurons. We study the consequences of input background activity on simulated self-organization, using the SOM, of the retinotopic map in the superior colliculus. The colliculus not only represents its inputs but also uses them to localize saccadic targets. Using the colliculus as a test-bed enables us to quantify the results of self- organization both descriptively, in terms of input-output mutual information, and functionally, in terms of the probability of error (expected distortion) in localizing targets. We find that mutual information is low, and distortion is high, when the SOM operates in the presence of input background activity but without the cooperative component (no neighbor training). Cooperation (training neighbors) greatly increases mutual information and greatly decreases expected distortion. Our simulation results extend theoretical work suggesting that cooperative mechanisms are needed to increase the information content of neural representations. They also identify input background activity as a factor affecting the self-organization of information-transmitting channels in the nervous system.
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页码:195 / 211
页数:16
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