Motif-topology improved Spiking Neural Network for the Cocktail Party Effect and McGurk Effect. (arXiv:2211.07641v1 [cs.NE])

Network architectures and learning principles are playing 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. Network architectures are also considered embodying the function of
the network. Here, we propose a Motif-topology improved SNN (M-SNN) for the
efficient multi-sensory integration and cognitive phenomenon simulations. The
cognitive phenomenon simulation we simulated includes the cocktail party effect
and McGurk effect, which are discussed by many researchers. Our M-SNN
constituted by the meta operator called network motifs. The source of 3-node
network motifs topology from artificial one pre-learned from the spatial or
temporal dataset. In the single-sensory classification task, the results showed
the accuracy of M-SNN using network motif topologies was higher than the pure
feedforward network topology without using them. In the multi-sensory
integration task, the performance of M-SNN using artificial network motif was
better than the state-of-the-art SNN using BRP (biologically-plausible reward
propagation). Furthermore, the M-SNN could better simulate the cocktail party
effect and McGurk effect with lower computational cost. We think the artificial
network motifs could be considered as some prior knowledge that would
contribute to the multi-sensory integration of SNNs and provide more benefits
for simulating the cognitive phenomenon.



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