Pattern Recognition Using Spiking Neurons and Firing Rates

Different varieties of artificial neural networks have proved their power in several pattern recognition problems, particularly feedforward neural networks. Nevertheless, these kinds of neural networks require of several neurons and layers in order to success when they are applied to solve non-linear problems. In this paper is shown how a spiking neuron can be applied to solve different linear and non-linear pattern recognition problems. A spiking neuron is stimulated during T ms with an input signal and fires when its membrane potential reaches a specific value generating an action potential (spike) or a train of spikes. Given a set of input patterns belonging to K classes, each input pattern is transformed into an input signal, then the spiking neuron is stimulated during T ms and finally the firing rate is computed. After adjusting the synaptic weights of the neuron model, we expect that input patterns belonging to the same class generate almost the same firing rate and input patterns belonging to different classes generate firing rates different enough to discriminate among the different classes. At last, a comparison between a feed-forward neural network and a spiking neuron is presented when they are applied to solve non-linear and real object recognition problems.