Character recognition using spiking neural networks

Ankur Gupta, Lyle N. Long

Research output: Chapter in Book/Report/Conference proceedingConference contribution

74 Scopus citations


A spiking neural network model is used to identify characters in a character set. The network is a two layered structure consisting of integrate-and-fire and active dendrite neurons. There are both excitatory and inhibitory connections in the network. Spike time dependent plasticity (STDP) is used for training. The winner take all mechanism is enforced by the lateral inhibitory connections. It is found that most of the characters are recognized in a character set consisting of 48 characters. The network is trained successfully with increased resolution of the characters. Also, addition of uniform random noise does not decrease its recognition capability.

Original languageEnglish (US)
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Number of pages6
StatePublished - 2007
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: Aug 12 2007Aug 17 2007

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576


Other2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/TerritoryUnited States
CityOrlando, FL

All Science Journal Classification (ASJC) codes

  • Software


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