关键词: 脉冲神经元, 脉冲序列, 精确学习, 梯度下降, 虚拟脉冲

Abstract: The supervised learning method based on gradient descent is to make the neuron can emit desired spike train by minimizing the error between the actual output spike train and the desired spike train using computation of gradient descent. However the error function is constructed dynamically by the actual output spike train and the corresponding desired spike train. That would lead to the number of actual output spike train and the desired spike train being unequal when the algorithm converges. This paper proposes an improved learning algorithm based on gradient descent and it detects the number of spikes in the desired output spike train and the actual output spike train. The algorithm uses a novel error function which is constructed with extra virtual actual spike and virtual desired spike to solve the problem of less or redundant output. Experimental results show that it can effectively prevent the early ending of learning with unequal number of output spike and make the neuron emit the desired spike train precisely.

Key words: spiking neuron, spike train, precise learning, gradient descent, virtual spike