Neural Comput - Intrinsic adaptation in autonomous recurrent neural networks.

Tópicos

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Resumo

A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depend crucially on the quality of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting, or chaotic activity patterns. We study the influence of nonsynaptic plasticity on the default dynamical state of recurrent neural networks. The nonsynaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes: a regular synchronized, an overall chaotic, and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interceded by chaotic bursts that respond sensitively to input signals. We discuss these findings in the context of self-organized information processing and critical brain dynamics.

Resumo Limpo

massiv recurr neural network respond one side input stimuli autonom activ side absenc sensori input stimuli inform process depend crucial qualiti autonomousst dynam ongo neural activ default neural activ may dynam structur time space show regular synchron burst chaotic activ pattern studi influenc nonsynapt plastic default dynam state recurr neural network nonsynapt adapt consid act intrins neural paramet threshold gain driven optim inform entropi observ presenc intrins adapt process three distinct global attract dynam regim regular synchron overal chaotic intermitt burst regim intermitt burst regim character interv regular flow quit insensit extern stimuli interced chaotic burst respond sensit input signal discuss find context selforgan inform process critic brain dynam

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