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Synaptic weight update in deep spiking neural networks

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dc.contributor.author Arora, Tushar
dc.contributor.author Vatsa, Mayank (Advisor)
dc.contributor.author Singh, Richa (Advisor)
dc.date.accessioned 2019-10-09T07:25:34Z
dc.date.available 2019-10-09T07:25:34Z
dc.date.issued 2019-11
dc.identifier.uri http://repository.iiitd.edu.in/xmlui/handle/123456789/775
dc.description.abstract Biological Neurons show a very rich range of dynamic properties and working, whereas the neurons in Artifi cial Neural Networks though being a very crude approximation of these biological networks are nowhere near as versatile as the biological neurons. Besides this argument, there are many reasons like biologically implausible weight updating algorithm Back-propagation which refers to a notion of derivative/gradient of a neuron that is not biologically synonymous to neural networks. The main idea of this research is to use the power Dynamical System representation of a neuron for an e active synaptic weight update algorithm for developing a biologically plausible Spiking neural network algorithm. Proof of concept is shown empirically by showing that the proposed SNN architecture is able to learn image patterns reasonably well. en_US
dc.language.iso en_US en_US
dc.publisher IIITD-Delhi en_US
dc.subject Spiking Neural Networks en_US
dc.subject Neural Networks en_US
dc.subject Neuromorphic Networks en_US
dc.subject Dynamical Systems en_US
dc.subject Synaptic Plasticity en_US
dc.title Synaptic weight update in deep spiking neural networks en_US
dc.type Other en_US

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