An end-to-end functional spiking model for sequential feature learning

Abstract

Spiking Neural Network (SNN) has recently gained significant momentum in the neuromorphic low-power systems. However, the existing SNN models have limited use in time-sequential feature learning, and the exhausting spike encoding and decoding make the SNNs not straightforward to use. Inspired by the functional organization in the primate visual system, we propose an end-to-end functional spiking model in this paper to address these issues. Specifically, we propose the functional spike response to make each neuron special, and the dynamic synaptic efficiency to make the transmission of each input signal controllable. We represent inputs by a simple two-tuple set instead of conventional complex encoding, which achieves end-to-end learning. Experiments on synthetic datasets demonstrate that employing the two-tuple encoding strategy, our method improves the accuracy of the traditional SNN model significantly. In addition, we apply our method to seven real-world datasets and one human motion prediction dataset to investigate its performance. Experimental results show that the proposed functional spike response organization saves the running time of our model compared with the LSTM, GRU and one of the state-of-the-art time series processing methods.