Abstract
In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.
Highlights
Understanding how rapid exposure to visual stimuli affects categorical decision by cortical neuron networks is essential for understanding the relationship between implicit neural information encoding and explicit behavior analysis
Motivated by the potential of spiking neural networks (SNNs) and hierarchy model, we address this issue in the context of the neural encoding and neural computing, here we propose a multilayer feedforward, hierarchical network consisting of integrate-andfire neuron model that can successfully detect, analyze and recognize object of interest
We focus on a potential form of cortex like framework of fast categorical decision making for facial expression recognition
Summary
Understanding how rapid exposure to visual stimuli (face, objects) affects categorical decision by cortical neuron networks is essential for understanding the relationship between implicit neural information encoding and explicit behavior analysis. Pioneering attempts include the Neocognitron by Fukushima [3], which processes information with rate-based neural units to deal with transformation invariant features, followed by the emergence of a bunch of functionally similar models, such as hierarchical machine proposed by LeCun and Bengio [4, 5], bottom-up model mechanism by Ullman et al [6, 7], or model by Wersing and Korner [8]. This trend was later followed by a noticeable hierarchical.
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