Unsupervised Learning with Spiking Neural Networks for Image Classification Tasks

Artificial neural networks based on sigmoidal neurons have achieved undisputed performance in various tasks, including image recognition and classification. The search for more biologically plausible artificial neuron models led to the creation of pulsed models. The new way of encoding information is through discrete pulses, and the timing of these pulses is important for the result. This work proposes the creation of a neural network with pulsed neurons for the task of image classification. The network uses cell models more similar to those found in animal brains, communicating through spikes and relying on a stochastic component for pulse generation. It also applies STDP as an unsupervised learning rule, very similar to human learning. Experiments were run using various parameter sets to study the network's dynamics in the image classification task. The results obtained were analyzed, and their performance indicates a promising method capable of good performance on three known image databases (Caltech 101, ETH-80, and MNIST). The database 1 achieved a classification accuracy of 87%, database 2 achieved 77% on ETH-80, and database 3 achieved 86% on MNIST respectively.