Spike-Train Level Unsupervised Learning Algorithm for Deep Spiking Belief Networks.

Deep spiking belief network (DSBN) uses unsupervised layer-wise pre-training method to train the network weights, it is stacked with the spike neural machine (SNM) modules. However, the synaptic weights of SNMs are difficult to pre-training through simple and effective approach for spike-train driven networks. This paper proposes a new algorithm that uses unsupervised multi-spike learning rule to train SNMs, which can implement the complex spatio-temporal pattern learning of spike trains. The spike signals first propagate in the forward direction, and then are reconstructed in the reverse direction, and the synaptic weights are adjusted according to the reconstruction error. The algorithm is successfully applied to spike train patterns, the module parameters are analyzed, such as the neuron number and learning rate in the SNMs. In addition, the low reconstruction errors of DSBNs are shown by the experimental results.