Review of Spiking Neural Network Architecture for Feature Extraction and Dimensionality Reduction

Abstract

To explore the main components of the future computing machines, the spiking neurons for feature extraction and dimensionality reduction applications. The contribution would be to present a review of the approaches to spiking neural network architecture used for feature extraction and dimensionality reduction applications. To give importance to more realistic neuron models the main objective is to present a general and a comprehensive overview of spiking neurons, ranging from biological neuron features to examples of practical applications in the mentioned field. However, this work will focus on how information can be coded by precisely timed spikes, emitted by different neurons and then this coded information would be processed to produce useful results for feature extraction and dimensionality reduction application. Also, different approaches/algorithm would be studied and compared in terms of computational efficiency as the range of computational problems related to spiking neuron is very large. Therefore, the efforts would also be directed towards the reduction of computational cost.