Implementation of Accurate Bio-Inspired Spiking Neural Network Using Fuzzy Methods, M.Sc. Thesis Sharif University of Technology ; Bagheri Shouraki, Saeed (Supervisor) ; Haj Sadeghi, Khosrow (Supervisor)
Abstract
Neuron models are the elementary units, which determine the performance of an Artificial Spiking Neural Network (ASNN) as they are known to be a particular class of machine learning methods. The ASNNs that are inspired by the features of biological neurons and organizational structure of biological nervous system as the third generation of Artificial Neural Networks (ANN). This thesis concentrates on study of biologically plausible neuron; based on Fuzzy approach and tries to develop fuzzy state of Leaky Integrate and Fire (LIF) model, in order to resemble closely the neuron-electrical dynamics for ASNN in most efficient way. In this study, the Fuzzy methods including TAKAGI-SUGENO-KANG...
Cataloging briefImplementation of Accurate Bio-Inspired Spiking Neural Network Using Fuzzy Methods, M.Sc. Thesis Sharif University of Technology ; Bagheri Shouraki, Saeed (Supervisor) ; Haj Sadeghi, Khosrow (Supervisor)
Abstract
Neuron models are the elementary units, which determine the performance of an Artificial Spiking Neural Network (ASNN) as they are known to be a particular class of machine learning methods. The ASNNs that are inspired by the features of biological neurons and organizational structure of biological nervous system as the third generation of Artificial Neural Networks (ANN). This thesis concentrates on study of biologically plausible neuron; based on Fuzzy approach and tries to develop fuzzy state of Leaky Integrate and Fire (LIF) model, in order to resemble closely the neuron-electrical dynamics for ASNN in most efficient way. In this study, the Fuzzy methods including TAKAGI-SUGENO-KANG...
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