TY  - JOUR
A1  - Eser, Jürgen
A1  - Zheng, Pengsheng
A1  - Triesch, Jochen
T1  - Nonlinear dynamics analysis of a self-organizing recurrent neural network : chaos waning
T2  - PLoS One
N2  - Self-organization is thought to play an important role in structuring nervous systems. It frequently arises as a consequence of plasticity mechanisms in neural networks: connectivity determines network dynamics which in turn feed back on network structure through various forms of plasticity. Recently, self-organizing recurrent neural network models (SORNs) have been shown to learn non-trivial structure in their inputs and to reproduce the experimentally observed statistics and fluctuations of synaptic connection strengths in cortex and hippocampus. However, the dynamics in these networks and how they change with network evolution are still poorly understood. Here we investigate the degree of chaos in SORNs by studying how the networks' self-organization changes their response to small perturbations. We study the effect of perturbations to the excitatory-to-excitatory weight matrix on connection strengths and on unit activities. We find that the network dynamics, characterized by an estimate of the maximum Lyapunov exponent, becomes less chaotic during its self-organization, developing into a regime where only few perturbations become amplified. We also find that due to the mixing of discrete and (quasi-)continuous variables in SORNs, small perturbations to the synaptic weights may become amplified only after a substantial delay, a phenomenon we propose to call deferred chaos.
Y1  - 2014
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32845
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-328453
SN  - 1932-6203
N1  - Copyright: © 2014 Eser et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
VL  - 9
IS  - (1):e86962
PB  - PLoS
CY  - Lawrence, Kan.
ER  -