Evolution of network structure by temporal learning

We study the effect of learning dynamics on network topology. A network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity. This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution.

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Year of publication:	2009
Authors:	Jost, Jürgen ; Kolwankar, Kiran M.
Published in:	Physica A: Statistical Mechanics and its Applications. - Elsevier, ISSN 0378-4371. - Vol. 388.2009, 9, p. 1959-1966
Publisher:	Elsevier
Subject:	Coupled maps \| Scale-free network \| Hebbian learning \| Logistic map \| Synchronization

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011058491