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A pulse-based reinforcement algorithm for learning continuous functions
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DOI：10.1016/S0925-2312(96)00034-3.png)
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Abstract
En 中文
An algorithm is presented which allows continuous functions to be learned by a neural network using spike-based reinforcement learning, Both the mean and the variance of the weights are changed during training; the latter is accomplished by manipulating the lengths of the spike trains used to represent real-valued quantifies, The method is here applied to the probabilistic RAM (pRAM) model, but it may be adapted for use with any pulse-based stochastic model in which individual weights behave as random variables.
Keywords:
pRAM
continuous-output reinforcement
pulse-coding
stochastic computing
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6.5
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2.5W
Citations:
6.5W
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Cognition
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