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 |