Optimal Region of Latching Activity in an Adaptive Potts Model for Networks of Neurons

Details Optimal Region of Latching Activity in an Adaptive Potts Model for Networks of Neurons Optimal Region of Latching Activity in an Adaptive Potts Model for Networks of Neurons

2011-11-01

arxiv.org/abs/1111.0309v2

Physics

Condensed Matter

Disordered Systems and Neural Networks

Scientific paper

In statistical mechanics, the Potts model is a model for interacting spins with more than two discrete states. Neural networks which exhibit features of learning and associative memory can also be modeled by a system of Potts spins. A spontaneous behavior of hopping from one discrete attractor state to another (referred to as latching) has been proposed to be associated with higher cognitive functions. Here we propose a model in which both the stochastic dynamics of Potts models and an adaptive potential function are present. A latching dynamics is observed in a limited region of the noise(temperature)-adaptation parameter space. We hence suggest noise as a fundamental factor in such alternations alongside adaptation. From a dynamical systems point of view, the noise-adaptation alternations may be the underlying mechanism for multi-stability in attractor based models. An optimality criterion for realistic models is finally inferred.

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