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A SPIKING NEURAL NETWORK FOR PROBABILISTIC COMPUTATION
A SPIKING NEURAL NETWORK FOR PROBABILISTIC COMPUTATION
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机译:概率神经计算的尖峰神经网络
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摘要
Described is a system for computing conditional probabilities of random variables for Bayesian inference. The system implements a spiking neural network of neurons to compute the conditional probability of two random variables X and Y. The spiking neural network includes an increment path for a synaptic weight that is proportional to a product of the synaptic weight and a probability of X, a decrement path for the synaptic weight that is proportional to a probability of X, Y, and delay and spike timing dependent plasticity (STDP) parameters such that the synaptic weight increases and decreases with the same magnitude for a single firing event.
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机译:描述了一种用于计算贝叶斯推断的随机变量的条件概率的系统。该系统实现了神经元的尖峰神经网络,以计算两个随机变量 X I>和 Y I>的条件概率。尖峰神经网络包括一个与突触权重和概率 X I>乘积成比例的突触权重的增量路径,一个与< I> X,Y I>,以及延迟和尖峰时间相关的可塑性(STDP)参数,使得单个触发事件的突触权重以相同的幅度增加和减少。
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