Live Demonstration: Image Classification Using Bio-inspired Spiking Neural Networks

Shruti R. Kulkarni, John M. Alexiades, Bipin Rajendran

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a live demonstration of an image classification system using bio-inspired Spiking Neural Networks. Our network is three-layered and is trained with the images from the MNIST database, achieving an accuracy of 98.06%. Synapses connecting the output layer neurons obey the spike based weight-adaptation rule using the supervised learning algorithm called NormAD. This network, implemented on a graphical processing unit (GPU), is used to classify digits drawn by users on a touch-screen interface in real-time. The spike propagation maps generated and displayed by the platform reveal key insights about information processing mechanisms of the brain.

Original languageEnglish
Title of host publication2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538648810
DOIs
StatePublished - Apr 26 2018
Externally publishedYes
Event2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy
Duration: May 27 2018May 30 2018

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2018-May
ISSN (Print)0271-4310

Conference

Conference2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Country/TerritoryItaly
CityFlorence
Period05/27/1805/30/18

Funding

ACKNOWLEDGMENT The authors would like to thank Ravindu Gunawardane and Jack Mcweeney from NJIT for their valuable contributions towards this work. This work was partially supported by grants from Semiconductor Research Corporation and CISCO.

FundersFunder number
Semiconductor Research Corporation
Cisco Systems

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