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We demonstrate deterministic extensive chaos in the dynamics of large sparsenetworks of theta neurons in the balanced state. The analysis is based onnumerically exact calculations of the full spectrum of Lyapunov exponents, theentropy production rate and the attractor dimension. Extensive chaos is foundin inhibitory networks and becomes more intense when an excitatory populationis included. We find a strikingly high rate of entropy production that wouldlimit information representation in cortical spike patterns to the immediatestimulus response.

Dynamical Entropy Production in Spiking Neuron Networks in the Balanced State