Channelwise Regional Integrate and Multiple Firing Neuron: Improving the Spatiotemporal Learning of Spiking Neural Networks

Spiking neural networks (SNNs) can be operated in an event-driven manner to save energy consumption of artificial neural networks (ANNs), which has attracted enormous research interests for their high biological plausibility and powerful spatiotemporal information processing. However, representative studies only evaluated SNNs on static temporal tasks or short sequence tasks, which could not fully demonstrate the advantages of SNNs in spatiotemporal learning. In addition, we point out that the existing directly trained SNNs to face the problems of long-term memory, network degeneration, gradient saturation, and heterogeneity learning, these limit the performance of SNNs. In this article, we propose channelwise regional integrate and multiple firing (CRIMF) neuron to improve the spatiotemporal learning of SNNs. First, CRIMF neuron contains a new internal state of regional current that enhances the memory of spiking neurons and facilitates the learning of temporal information over long time steps. Second, CRIMF neuron is implemented with the multiple firing mechanisms; it is able to adjust the distribution of membrane potential and membrane potential gradient in the single firing mechanism, thus mitigating the underactivation and gradient saturation. Third, CRIMF neuron is trained with the channelwise learning strategy for the targeted learning of different types of temporal features, and an index of differentiation degree is proposed to visualize the effectiveness of the channelwise learning strategy. We also introduce the regional current reset equation and normalize the input of postsynaptic neurons in spatiotemporal dimension to avoid network degeneration. Finally, we select two emotion electroencephalogram (EEG) datasets and perform the evaluations based on manual features and raw signals. Experimental results show that CRIMF-based SNNs outperform the state-of-the-art methods in static temporal task, and CRIMF neurons are superior to the advanced spiking neurons and recurrent units of ANNs in dynamic temporal task, using low energy consumption.