Optimal firing in sparsely-connected low-activity attractor networks |
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Authors: | Isaac Meilijson Eytan Ruppin |
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Institution: | (1) School of Mathematical Sciences, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, 69978 Tel-Aviv, Israel, IL |
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Abstract: | We examine the performance of Hebbian-like attractor neural networks, recalling stored memory patterns from their distorted
versions. Searching for an activation (firing-rate) function that maximizes the performance in sparsely connected low-activity
networks, we show that the optimal activation function is a threshold-sigmoid of the neuron's input field. This function is shown to be in close correspondence with the dependence of the firing rate
of cortical neurons on their integrated input current, as described by neurophysiological recordings and conduction-based
models. It also accounts for the decreasing-density shape of firing rates that has been reported in the literature.
Received:9 December 1994 / Accepted in revised form: 9 January 1996 |
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