  Spiking Neural Network (SNN) is considered more biologically realistic and
power-efficient as it imitates the fundamental mechanism of the human brain.
Recently, backpropagation (BP) based SNN learning algorithms that utilize deep
learning 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 modeling
spike activities: Multiplicity, Adaptability, and Plasticity (MAP). In terms of
multiplicity, we propose a Multiple-Spike Pattern (MSP) with multiple spike
transmission to strengthen model robustness in discrete time-iteration. To
realize adaptability, we adopt Spike Frequency Adaption (SFA) under MSP to
decrease spike activities for improved efficiency. For plasticity, we propose a
trainable convolutional synapse that models spike response current to enhance
the diversity of spiking neurons for temporal feature extraction. The proposed
SNN model achieves competitive performances on neuromorphic datasets: N-MNIST
and SHD. Furthermore, experimental results demonstrate that the proposed three
aspects are significant to iterative robustness, spike efficiency, and temporal
feature extraction capability of spike activities. In summary, this work
proposes a feasible scheme for bio-inspired spike activities with MAP, offering
a new neuromorphic perspective to embed biological characteristics into spiking
neural networks.
