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Title: Computing with Simulated and Cultured Neuronal Networks
Authors: JU HAN
Keywords: liquid state machine, neuronal networks, simulation, neurocomputer, MEA, music classification
Issue Date: 5-Aug-2013
Citation: JU HAN (2013-08-05). Computing with Simulated and Cultured Neuronal Networks. ScholarBank@NUS Repository.
Abstract: The brain has extraordinary ability to process large amount of information in parallel and in real-time. The main structure of the brain is nearly deterministic, while cortical circuitry in a local area is rather random. This raises a fundamental question: are random networks capable to process information? In light of the liquid state machine (LSM) paradigm that is a real-time computing framework, this thesis investigated the ability of randomly connected spiking neuronal networks to process spatiotemporal information through both computer simulations and in vitro experiments. Results suggest that random networks have an intrinsic ability to classify spatiotemporal patterns, and the classification performance depends on synaptic connectivity. In addition, this thesis demonstrates that dissociated neuronal cultures possess a fading memory that can last for longer than 4 seconds, allowing the implementation of a neurocomputer: a neuronal-culture version of the LSM.
Appears in Collections:Ph.D Theses (Open)

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