Criticality of avalanche dynamics in adaptive recurrent networks

被引:19
作者
Levina, Anna
Ernst, Udo
Herrmann, J. Michael
机构
[1] Univ Gottingen, Grad Sch Identificat Math Models, D-37073 Gottingen, Germany
[2] MPI Dynam & Self Org, D-37073 Gottingen, Germany
[3] Ecole Normale Super, Grp Neural Theory, F-75014 Paris, France
[4] Univ Gottingen, Bernstein Ctr Computat Neurosci Gottingen, D-37073 Gottingen, Germany
[5] Univ Gottingen, Inst Nonlinear Dynam, D-37073 Gottingen, Germany
关键词
neuronal avalanches; neural adaptation; self-organized criticality; critical branching;
D O I
10.1016/j.neucom.2006.10.056
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In many studies of self-organized criticality (SOC), branching processes were used to model the dynamics of the activity of the system during avalanches. This mathematical simplification was also adopted when investigating systems with a complicated connection topology including recurrent and subthreshold interactions. However, none of these studies really analyzed whether this convenient approximation was indeed applicable. In present paper we study the correspondences between avalanches generated by branching processes and by a fully connected neural network. The benefit from the analysis is not only the justification of such correspondence but also a simple learning rule, which allows self-organization of the network towards a critical state as recently observed in slice experiments. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:1877 / 1881
页数:5
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