AUTHOR=Yu Chengting , Du Yangkai , Chen Mufeng , Wang Aili , Wang Gaoang , Li Erping 

TITLE=MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.945037

DOI=10.3389/fnins.2022.945037

ISSN=1662-453X

ABSTRACT=Spiking Neural Networks (SNNs) are considered more biologically realistic and power-efficient as they imitate 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 the capability to extract spikes' temporal features.
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.