Izhikevich Neuron Model and its Application in Pattern Recognition

In this paper is shown how an Izhikevich neuron can be applied to solve different linear and non- linear pattern recognition problems. Given a set of input patterns belonging to K classes, each input pattern is transformed into an input signal, then the Izhikevich neuron is stimulated during T ms and finally the firing rate is computed. After adjusting the synaptic weights of the neural model, input patterns belonging to the same class will generate almost the same firing rate and input patterns belonging to different classes will generate firing rates different enough to discriminate among the different classes. At last, a comparison between a feed-forward neural network and the Izhikevich neural model is presented when they are applied to solve non-linear and real object recognition problems.

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