Abstract
Spontaneous activity found in neural networks usually results in a reduction of computational performance. As a consequence, artificial neural networks are often operated at the edge of chaos, where the network is stable yet highly susceptible to input information. Surprisingly, regular spontaneous dynamics in Neural Networks beyond their resting state possess a high degree of spatio-temporal synchronization, a situation that can also be found in biological neural networks. Characterizing information preservation via complexity indices, we show how spatial synchronization allows rRNNs to reduce the negative impact of regular spontaneous dynamics on their computational performance.
Highlights
Random recurrent neural networks are popular models to investigate basic principles of information processing inside the human brain
Dynamics of the here discussed recurrent neural networks (rRNNs) can be tuned via a single parameter, which typically results in a bifurcation phenomena as a route to chaos[25,26,27]
The windows of regular dynamics appear at distinct values which are highly comparable for all nodes
Summary
Random recurrent neural networks (rRNNs) are popular models to investigate basic principles of information processing inside the human brain. The underlying mechanisms are analysed based on the mutual information between each node of rRNN and the input time signal, as well as the rRNN’s maximal Lyapunov exponent. As an information processing system, the rRNN realizes computation on the bases of rich dynamical responses to external, i.e. sensory input.
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