Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/49436
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dc.contributor.authorKoh, Lynn.
dc.date.accessioned2012-05-18T07:19:24Z
dc.date.available2012-05-18T07:19:24Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10356/49436
dc.description.abstractThe brain has always been known to be a powerful computational tool as it possesses problem solving and exceptional calculating abilities. As such, neuroscientists and researchers are always fascinated by how the brain works as this knowledge will help in treating brain disorder and create better machines that use brain-like computing principles. With the recent research development in spiking neural network models, we have come to know that unlike the classic neural network models, these models communicate though the precise timing of neuron spikes, hence making them more biologically realistic. To support the field of artificial intelligence which implements spiking neural network models, this paper presents different phase locking behaviour of two popular formal spiking neural network models - Leaky Integrate and Fire and Resonate and Fire neurons to various periodic input stimulus. Differences in computation are also observed when both neuron models are coupled to form a Winner Take All circuit. All interpretations of neural responses presented in this paper are based on results obtained from numerical simulations performed using MATLAB.en_US
dc.format.extent80 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineeringen_US
dc.titleComputation in spiking neural networksen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
dc.contributor.supervisor2Arindam Basuen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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