Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model
Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model
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发表时间：
2004-12
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通讯作者：
Taro Toyoizumi;J. Pfister;K. Aihara;W. Gerstner
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作者：
Taro Toyoizumi;J. Pfister;K. Aihara;W. Gerstner
我们从尖峰神经元模型的互信息最大化的意义上推导了最佳学习规则。在输入波动较小的假设下，我们发现了一个尖峰时间依赖性可塑性（STDP）函数，该函数取决于兴奋性突触后电位（EPSP）的时间过程和突触后神经元的自相关函数。我们证明 STDP 函数具有正相位和负相位。正相与 EPSP 的形状有关，而负相则由神经元不应性控制。
We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model. Under the assumption of small fluctuations of the input, we find a spike-timing dependent plasticity (STDP) function which depends on the time course of excitatory postsynaptic potentials (EPSPs) and the autocorrelation function of the postsynaptic neuron. We show that the STDP function has both positive and negative phases. The positive phase is related to the shape of the EPSP while the negative phase is controlled by neuronal refractoriness.