Character recognition with two spiking neural network models on multicore architectures

This paper presents the use of the Izhikevich and Hodgkin Huxley neuron models for image recognition. The former is more biologically accurate than the commonly used integrate and fire neuron model but has similar low computational requirements. Brain scale cortex models tend to use the more biological neuron models. The results of this work show that the Izhikevich model can be used for image recognition and would be a good candidate for a large scale visual cortex model. Neural networks based on these models are developed and applied to character recognition. They were able to identify 48 24times24 images and their noisy versions. The networks were accelerated using modern multicore processors and showed significant speedups. Such processors are likely to be used for developing high performance, large scale implementations of these image recognition networks.