Learning Rule with Precise Spike Timing Based on Direct Feedback Alignment

Due to the complex spatiotemporal dynamics of spike neurons and synapses,training spike neural networks(SNNs) is relatively challenging,and there are currently no widely accepted core training algorithms and techniques.In this paper,we propose a learning rule with precise spike timing based on direct feedback alignment(PREST-DFA).Inspired by the learning algorithm called spike layer error reassignment(SLAYER),PREST-DFA uses error signals based on spike convolution differences.The output layer iteratively calculates the error values,and utilizes direct feedback alignment(DFA) to broadcast the error to hidden layer neurons,finally achieving synaptic weights update.We implement time-driven PREST-DFA,and simulation experiments demonstrate that PREST-DFA has precise spike timing learning capabilities and good biological plausibility.Based on literature search results,this is the first time to verify that learning algorithm based on DFA can control the precise fire time of spikes in deep networks,indicating that the DFA mechanism can be applied to algorithm design based on spike timing.We also compare learning performance and training speed.Experimental results show that PREST-DFA can achieve good learning performance with lower inference latency and can accelerate training speed compared to learning algorithms trained using backpropagation with the same learning rule.