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Title: Bio-Inspired Autonomous Hardware Neuro-controller Device on an FPGA Inspired by the Hippocampus
Author: Mokhtar, Maizura
ISNI:       0000 0001 3414 0552
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2008
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One method in achieving artificial intelligence is by emulating biological concepts onto an electronic device, specifically how a biological organism governs its behaviour. This research project investigates how the hippocampus works; and attempts to model this region of the brain onto an electronic device. The hippocampus is chosen because this is one of the regions in the brain responsible for learning and memory. This study uses models of the pyramidal neurons in the hippocampus as well as its spatial representation as the design components for a hardware neuro-controller module. The method chosen to model the individual neurons is the two-dimensional bio-inspired Izhikevich algorithm that has the ability to describe a variety of neuron dynamic behaviours observed in the brain. The hippocampus-inspired spiking neural network architecture also includes place cells/place field representation, a rate-based representation that provides spatial representation of the environment to the hippocampus. A biological nervous system is a dynamical system; it is governed by the learning rules that adjust the strength of connectivity between the neurons in the neural network. These learning rules are implemented to the hippocampus-inspired spiking neural network to allow the neural network to perform its task of path navigation. Following successful simulations of the software prototype of the neural network architecture in performing its desired task, this architecture is then synthesized onto a Field Programmable Gate Array (FPGA) device. This is to allow the neural network architecture to be utilized as a neuro-controller device for the purpose of path navigation, creates memories, and thus achieving autonomy.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available