Abstract
Features of a voltage- and time-dependent potassium conductance, gK+(A), were incorporated into the neuronal response function of auto-associative, attractor neural networks. Simulations showed that an increase in the magnitude of gK+(A) decreased the rate of convergence to equilibria and could markedly change the probability that the final equilibrium state of the network would most highly correlate with a stationary stimulus embedded in noise. These results may provide a framework for evaluating the functional roles of the many agents which modulate gK+(A).
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