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Spiking Neural Network (SNN) is considered more biologically realistic andpower-efficient as it imitates the fundamental mechanism of the human brain.Recently, backpropagation (BP) based SNN learning algorithms that utilize deeplearning frameworks have achieved good performance. However,bio-interpretability is partially neglected in those BP-based algorithms.Toward bio-plausible BP-based SNNs, we consider three properties in modelingspike activities: Multiplicity, Adaptability, and Plasticity (MAP). In terms ofmultiplicity, we propose a Multiple-Spike Pattern (MSP) with multiple spiketransmission to strengthen model robustness in discrete time-iteration. Torealize adaptability, we adopt Spike Frequency Adaption (SFA) under MSP todecrease spike activities for improved efficiency. For plasticity, we propose atrainable convolutional synapse that models spike response current to enhancethe diversity of spiking neurons for temporal feature extraction. The proposedSNN model achieves competitive performances on neuromorphic datasets: N-MNISTand SHD. Furthermore, experimental results demonstrate that the proposed threeaspects are significant to iterative robustness, spike efficiency, and temporalfeature extraction capability of spike activities. In summary, this workproposes a feasible scheme for bio-inspired spike activities with MAP, offeringa new neuromorphic perspective to embed biological characteristics into spikingneural networks.

MAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks