Freezing transition in asymmetric random neural networks with deterministic dynamics

A network of binary elements (spins, neurons) which are completely connected by random couplings is investigated for a deterministic dynamics. For general values of the symmetry parameter η extensive numerical simulations have been performed. With increasing symetry η a sharp transition from a chaotic motion to a frozen state is found.

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