A Discrete-Event Neural Network Simulator for General Neuron Models |
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Authors: | T Makino |
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Affiliation: | (1) Department of Complexity Science and Engineering, Graduate School of Frontier Science, Tokyo University, Hongo 7–3–1, 113–0033 Bunkyo-ku, Tokyo, Japan |
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Abstract: | Efficient simulation techniques for a discrete-event pulsed neural network simulator are developed. In a discrete-event simulation framework, simulation of complex neural behaviours, such as phase precession and phase arbitration, demands the prediction of delayed firing times. The new technique, the incremental partitioning method, uses linear envelopes of the state variable of a neuron to partition the simulated time so that the delayed-firing time is reliably calculated by applying the bisection-combined Newton-Raphson method to every partition. The quick filtering technique is also proposed for reducing calculation cost of linear envelopes. The simulator developed, Punnets, has achieved efficiency and precision, but is still capable of simulating a complex behaviour of large-scale neural network models. |
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Keywords: | Discrete-event simulation Event-driven simulation Incremental partitioning method Neural network simulator Punnets Pulsed neural network |
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