Network architectures and learning principles are key in forming complex
functions in artificial neural networks (ANNs) and spiking neural networks
(SNNs). SNNs are considered the new-generation artificial networks by
incorporating more biological features than ANNs, including dynamic spiking
neurons, functionally specified architectures, and efficient learning
paradigms. In this paper, we propose a Motif-topology and Reward-learning
improved SNN (MR-SNN) for efficient multi-sensory integration. MR-SNN contains
13 types of 3-node Motif topologies which are first extracted from independent
single-sensory learning paradigms and then integrated for multi-sensory
classification. The experimental results showed higher accuracy and stronger
robustness of the proposed MR-SNN than other conventional SNNs without using
Motifs. Furthermore, the proposed reward learning paradigm was biologically
plausible and can better explain the cognitive McGurk effect caused by
incongruent visual and auditory sensory signals.

本研究提出一种新型神经网络 ——MR-SNN，它采用了基于多肌体拓扑结构和奖励学习策略的动态脉冲神经元，以提高多感知融合的准确性和稳健性，并且在感知认知领域的实验中表现出了更好的效果。