Multilayer Self-Organizing Impulse Neural Network For Handwritten Digit Recognition

This paper proposes a multi-layer self-organizing impulse neural network. By adding an improved lateral inhibition mechanism and neuron model, the novel network can extract more information from the pictures, thus improving the expressive ability and elevating the recognition accuracy of the classification model. Besides, we replace the topology of the excitation layer and the inhibition layer with a recurrent connection structure. Several experiments have been carried out based on the proposed neural network and proved that the network can well complete the task of handwritten digit recognition. By tuning the network parameters, the recognition accuracy can reach 92.8%, which is at a high level in the same type of network. Besides, we also verified the robust performance of the network by randomly reducing the number of synapses and the number of neurons. It turns out that the network can still achieve high recognition accuracy after randomly discarding some neurons or synapses.