EDL: An Extended Delay Learning Based Remote Supervised Method for Spiking Neurons

摘要

This paper presents an Extended Delay Learning based Remote Supervised Method, called EDL, which extends the existing DL-ReSuMe learning method previously proposed by the authors for mapping spatio-temporal input spiking patterns into desired spike trains. EDL merges the weight adjustment property of STDP and anti-STDP with a delay shift method similar to DL-ReSuMe but also introduces the following distinct features to improve learning performance. Firstly, EDL adjusts synaptic delays more than once to find more precise value for each delay. Secondly, EDL can increase or decrease the current value of delays during a learning epoch by initialising the delays at a value higher than zero at the start of learning. Thirdly, EDL adjusts the delays related to a group of inputs instead of a single input. The ability of multiple changes of each delay in addition to the adjustment of a group of delays helps the EDL method to find more appropriate values of delays to produce a desired spike train. Finally, EDL is not restricted to adjusting only one type of inputs (inhibitory or excitatory inputs) at each learning time. Instead, it trains the delays of both inhibitory and excitatory inputs cooperatively to enhance the learning performance.