STDP的一种随机方法
A Stochastic Approach to STDP
摘要：
我们提出了一个数字实现的尖峰定时依赖可塑性（STDP）的学习规则。建议的数字实现包括一个指数衰减发生器阵列和STDP适配器阵列。在突触前和突触后尖峰到达时，STDP适配器将向衰减发生器发送数字尖峰。然后，衰减发生器将生成指数衰减，STDP适配器将使用该指数衰减来执行权重适配。指数衰减，这是计算昂贵的，有效地实现了使用一种新的随机方法，我们在这里分析和计算。我们使用时间复用的方法来实现8192（8 k）虚拟STDP适配器和衰变发生器，每个只有一个物理实现。我们已经验证了我们的随机STDP方法与平衡的激发/抑制实验的测量结果。我们的随机方法是理想的实现STDP学习规则在大规模脉冲神经网络运行在实时的。
英文摘要：
We present a digital implementation of the Spike Timing Dependent Plasticity (STDP) learning rule. The proposed digital implementation consists of an exponential decay generator array and a STDP adaptor array. On the arrival of a pre- and post-synaptic spike, the STDP adaptor will send a digital spike to the decay generator. The decay generator will then generate an exponential decay, which will be used by the STDP adaptor to perform the weight adaption. The exponential decay, which is computational expensive, is efficiently implemented by using a novel stochastic approach, which we analyse and characterise here. We use a time multiplexing approach to achieve 8192 (8k) virtual STDP adaptors and decay generators with only one physical implementation of each. We have validated our stochastic STDP approach with measurement results of a balanced excitation/inhibition experiment. Our stochastic approach is ideal for implementing the STDP learning rule in large-scale spiking neural networks running in real time.
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