Stochastic Spiking Neural Networks at the EDGE of CHAOS

In this work we show a study about which processes are related to chaotic and synchronized neural states based on the study of in-silico implementation of Stochastic Spiking Neural Networks (SSNN). Chaotic neural ensembles are excellent transmission and convolution systems. At the same time, synchronized cells (that can be understood as ordered states of the brain) are associated to more complex non-linear computations. We experimentally show that complex and quick pattern recognition processes arise when both synchronized and chaotic states are mixed. These measurements are in accordance with in-vivo observations related to the role of neural synchrony in pattern recognition and to the speed of the real biological process. The measurements obtained from the hardware implementation of different types of neural systems suggest that the brain processing can be governed by the superposition of these two complementary states with complementary functionalities (non-linear processing for synchronized states and information convolution and parallelization for chaotic).

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