Unsupervised Learning Based on Temporal Coding Using STDP in Spiking Neural Networks

Spiking Neural Networks (SNNs) have been recognized as one of the next generation of Neural Networks (NNs), showing a great potential in a variety of applications. Spiking-Timing Dependent Plasticity (STDP) underlies the brain’s learning mechanisms, and trains SNNs with great energy efficiency. In this paper, we propose a low-cost spike-time based unsupervised learning method. It constructs a SNN with one fully-connected excitatory layer structure without inhibitory layer, and trains the SNN with STDP using a first-spike-based temporal coding scheme where input information is directly encoded into spike times. It only updates the synaptic weights connected to the neuron that first generates a spike in a forward propagation step, which reduces the frequency of the synaptic weight updates significantly. The forward propagation process can be stopped once a neuron fires whether in the training mode or the inference mode, by which many unnecessary computations are just avoided and the latency in the inference mode is reduced. The method was used to train on the classification task on MNIST dataset and achieved an accuracy of 90.4% with 800 excitatory neurons.