Improve Response to Successive Input Patterns in SpikeProp - Discussion on Training Method and Response Function -

Abstract

In this article, we propose a new training method to train successive patterns for SpikeProp, which is a kind of spiking neural network. SpikeProp represents information by the timing of spikes and can train a transformation from an input spike sequence to the desired output spike sequence. In the previous study, the network tends to fail to output desired sequences in the case of successive input patterns with a narrow interval. We proposed a new training method that trains combined desired patterns and changes spike response function, which decides behavior of each unit. By simple experiments, we confirmed that the proposed method improves the network output for successive input patterns with a narrow interval.