Dissertation/Thesis Abstract

Spiking Neural Networks and Sparse Deep Learning
by Tavanaei, Amirhossein, Ph.D., University of Louisiana at Lafayette, 2018, 176; 10807940
Abstract (Summary)

This document proposes new methods for training multi-layer and deep spiking neural networks (SNNs), specifically, spiking convolutional neural networks (CNNs). Training a multi-layer spiking network poses difficulties because the output spikes do not have derivatives and the commonly used backpropagation method for non-spiking networks is not easily applied. Our methods use novel versions of the brain-like, local learning rule named spike-timing-dependent plasticity (STDP) that incorporates supervised and unsupervised components. Our method starts with conventional learning methods and converts them to spatio-temporally local rules suited for SNNs.

The training uses two components for unsupervised feature extraction and supervised classification. The first component refers to new STDP rules for spike-based representation learning that trains convolutional filters and initial representations. The second introduces new STDP-based supervised learning rules for spike pattern classification via an approximation to gradient descent by combining the STDP and anti-STDP rules. Specifically, the STDP-based supervised learning model approximates gradient descent by using temporally local STDP rules. Stacking these components implements a novel sparse, spiking deep learning model. Our spiking deep learning model is categorized as a variation of spiking CNNs of integrate-and-fire (IF) neurons with performance comparable with the state-of-the-art deep SNNs. The experimental results show the success of the proposed model for image classification. Our network architecture is the only spiking CNN which provides bio-inspired STDP rules in a hierarchy of feature extraction and classification in an entirely spike-based framework.

Indexing (document details)
Advisor: Maida, Anthony S.
Commitee: Chu, Chee-Hung Henry, Loganantharaj, Rasiah, Raghavan, Vijay V.
School: University of Louisiana at Lafayette
Department: Computer Science
School Location: United States -- Louisiana
Source: DAI-B 80/07(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Artificial intelligence, Computer science
Keywords: Deep learning, Sparse learning, Spiking neural network, Synaptic plasticity
Publication Number: 10807940
ISBN: 9780438970960
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