Pattern Recognition Using Spiking Neural Networks

Deep learning believed to be a promising approach for solving specific problems in the field of artificial intelligence whenever a large amount of data and computation is available. However, tasks that require immediate yet robust decisions in the presence of small data are not suited for such an approach. The superior performance of the human brain in specific tasks like pattern recognition in comparison to traditional neural networks convinced neuroscientists to introduce a biologically plausible model of the neuron, which is known as spiking neurons. In opposition to conventional neuron, spiking neurons use a short electrical pulse known as a spike to transfer the information. The complexity and dynamic of these neurons allow them to perform complex computational tasks. However, training a spiking neural network does not follow the rule of conventional ANN, and we need to devise new methods of training that are compatible with the unsupervised nature of these networks. This thesis aims to investigate the unsupervised approaches of training spiking networks using spike time-dependent plasticity (STDP) and assess their performance on real-world machine learning applications like handwritten digit recognition.

Name thesis:degree_name

M.A.Sc.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Windsor

Year dc:date.issued

2020

Author dc:creator

• Talaei, Amir Javid

Advisor dc:contributor.advisor

• Majid Ahmadi

Contributors dc:contributor

• mita@uwindsor.ca

Licence

• http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en_CA

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14776/8917

OAI identifier oai:identifier

oai:uwindsor.scholaris.ca:20.500.14776/8917