Brain- and Quantum Inspired Mathematical and Computational Models of Spiking Neural Networks for Deep Learning of Spatio-Temporal Data

Brain- and Quantum Inspired Mathematical and Computational Models of Spiking Neural Networks for Deep Learning of Spatio-Temporal Data

Spiking neural networks (SNN) represent the third generation of the neural networks. They are inspired by the information processing principles in the human brain. The theory and applications of SNN can further benefit from: a) The use of new brain-inspired learning principles for more efficient lea...

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Bibliographic Details
Main Author: Bahrami, Helena (Author)
Other Authors: Kasabov, Nikola (Contributor), Vellasco, Marley (Contributor)
Format: Others
Published: Auckland University of Technology, 2021-11-22T00:17:08Z.
Subjects:
Brain-ispired computational model
Quantum-inspired computational model
Hybrid model of brain-like and quantum-inspired SNN
Chaotic quantum model
NeuCube
Two-level hierarchical learning framework
SNN
Brain neurodegenerative diseases
Optimisation
Prediction and classification of Spatiotemporal data
EEG
Quantum associative memory
Neuromodulatory feedback
Backpropagation
Supervised learning
Semi-supervised learning
Feature selection
Fuzzy-rough set
Quantum neuron
Quantum synapse
Neurotransmitor
Neurotransmitters
Thesis
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