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

Document Type

Conference Proceeding

Publication Date

4-26-2018

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.

Identifier

85057086448 (Scopus)

ISBN

[9781538648810]

Publication Title

Proceedings IEEE International Symposium on Circuits and Systems

External Full Text Location

https://doi.org/10.1109/ISCAS.2018.8351810

ISSN

02714310

Volume

2018-May

Fund Ref

Semiconductor Research Corporation

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