Spiking neural networks based cortex like mechanism: A case study for facial expression recognition

Abstract

Ongoing efforts within neuroscience and intelligent system have been directed toward the building of artificial computational models using simulated neuron units as basic building blocks. Such efforts, inspired in the standard design of traditional neural networks, are limited by the difficulties arising from single functional performance and computational inconvenience, especially when modeling large scale, complex and dynamic processes such as cognitive recognition. Here, we show that there is a different form of implementing cortex-like mechanism, the motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the visual cortex and developments on spiking neural networks (SNNs), a promising direction for neural networks, as they utilize information representation as trains of spikes, embedded with spatiotemporal characteristics. A practical implementation is presented, which can be simply described as cortical-like feed-forward hierarchy using biologically plausible neural system. As a proof of principle, a prototype model has been testified on the platform of several facial expression dataset. Of note, small structure modifications and different learning schemes allow for implementing more complicated decision system, showing great potential for discovering implicit pattern of interest and further analysis. Our results support the approach of using such hierarchical consortia as an efficient way of complex pattern analysis task not easily solvable using traditional, single functional way of implementations.