Error-backpropagation in temporally encoded network of spiking neurons
47 p.
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2011
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sg-ntu-dr.10356-468212023-07-04T15:31:15Z Error-backpropagation in temporally encoded network of spiking neurons Vivek Shankar Gunasekaran Song Qing School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems 47 p. 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. Master of Science (Computer Control and Automation) 2011-12-23T09:58:24Z 2011-12-23T09:58:24Z 2010 2010 Thesis http://hdl.handle.net/10356/46821 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Vivek Shankar Gunasekaran Error-backpropagation in temporally encoded network of spiking neurons |
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47 p. |
author2 |
Song Qing |
author_facet |
Song Qing Vivek Shankar Gunasekaran |
format |
Theses and Dissertations |
author |
Vivek Shankar Gunasekaran |
author_sort |
Vivek Shankar Gunasekaran |
title |
Error-backpropagation in temporally encoded network of spiking neurons |
title_short |
Error-backpropagation in temporally encoded network of spiking neurons |
title_full |
Error-backpropagation in temporally encoded network of spiking neurons |
title_fullStr |
Error-backpropagation in temporally encoded network of spiking neurons |
title_full_unstemmed |
Error-backpropagation in temporally encoded network of spiking neurons |
title_sort |
error-backpropagation in temporally encoded network of spiking neurons |
publishDate |
2011 |
url |
http://hdl.handle.net/10356/46821 |
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1772825153578729472 |