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The state space of a conventional Hopfield network typically exhibits manydifferent attractors of which only a small subset satisfy constraints betweenneurons in a globally optimal fashion. It has recently been demonstrated thatcombining Hebbian learning with occasional alterations of normal neural statesavoids this problem by means of self-organized enlargement of the best basinsof attraction. However, so far it is not clear to what extent this process ofself-optimization is also operative in real brains. Here we demonstrate that itcan be transferred to more biologically plausible neural networks byimplementing a self-optimizing spiking neural network model. In addition, byusing this spiking neural network to emulate a Hopfield network with Hebbianlearning, we attempt to make a connection between rate-based and temporalcoding based neural systems. Although further work is required to make thismodel more realistic, it already suggests that the efficacy of theself-optimizing process is independent from the simplifying assumptions of aconventional Hopfield network. We also discuss natural and cultural processesthat could be responsible for occasional alteration of neural firing patternsin actual brains

Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model