A Synaptic Pruning-Based Spiking Neural Network for Hand-Written Digits Classification - Faghihi, Faramarz; Alashwal, Hany; Moustafa, Ahmed A. (Frontiers in Artificial Intelligence, 2022)
A spiking neural network model inspired by synaptic pruning is developed and trained to extract features of hand-written digits. The network is composed of three spiking neural layers and one output neuron whose firing rate is used for classification. The model detects and learns geometric features of the images from the MNIST database (Modified National Institute of Standards and Technology database). In this work, a novel learning rule is developed to train the network to detect features of different digit classes. For this purpose, randomly initialized synaptic weights between the first and second layers are updated using average firing rates of pre- and post-synaptic neurons. Then using a neuroscience inspired mechanism named ‘synaptic pruning’ and its predefined threshold values, some of the synapses are deleted. Hence, these sparse matrices named ‘information channels’ are constructed so that they show highly specific patterns for each digit class as connections matrices between the first and second layers. The ‘information channels’ are used in the test phase to assign a digit class to each test image. In addition, the role of feed-back inhibition as well as connectivity rates of the second and third neural layers are studied. As similar to humans’ abilities to learn from small training trials, the developed spiking neural network needs a very small dataset for training, compared to conventional deep learning methods that have shown very good performance on the MNIST dataset. This work introduces a new class of brain-inspired spiking neural networks to extract features of complex images data.