Convolutional networks for images, speech, and time series
The handbook of brain theory and neural networks
Computation of pattern invariance in brain-like structures
Neural Networks - Special issue on organisation of computation in brain-like systems
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topographic Independent Component Analysis
Neural Computation
EMPATH: A Neural Network that Categorizes Facial Expressions
Journal of Cognitive Neuroscience
Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition in JAFFE dataset based on Gaussian process classification
IEEE Transactions on Neural Networks
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Facial expression recognition using kernel canonical correlation analysis (KCCA)
IEEE Transactions on Neural Networks
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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.