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https://hdl.handle.net/10356/46821
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Vivek Shankar Gunasekaran | en_US |
dc.date.accessioned | 2011-12-23T09:58:24Z | |
dc.date.available | 2011-12-23T09:58:24Z | |
dc.date.copyright | 2010 | en_US |
dc.date.issued | 2010 | |
dc.identifier.uri | http://hdl.handle.net/10356/46821 | |
dc.description | 47 p. | en_US |
dc.description.abstract | This dissertation attempts to analyze the obstacle in training the spike propagation networks. The obstacle is nothing but surge, which is caused by nature of spike propagation network. The surges are steep rises of error. Networks of spiking neurons, with action potentials, can perform non linear classification with temporal coding. This is a kind of supervised learning algorithm which trains the network of spiking neurons. The neurons in this algorithm generate action potentials or spikes, when the state variable of neuron crosses the threshold value. | en_US |
dc.rights | Nanyang Technological University | en_US |
dc.subject | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems | en_US |
dc.title | Error-backpropagation in temporally encoded network of spiking neurons | en_US |
dc.type | Thesis | en_US |
dc.contributor.supervisor | Song Qing | en_US |
dc.contributor.school | School of Electrical and Electronic Engineering | en_US |
dc.description.degree | Master of Science (Computer Control and Automation) | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | restricted | - |
Appears in Collections: | EEE Theses |
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File | Description | Size | Format | |
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EEE_THESES_165.pdf Restricted Access | 4.51 MB | Adobe PDF | View/Open |
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