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 language | English |
---|---|
Title of host publication | 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781538648810 |
DOIs | |
State | Published - Apr 26 2018 |
Externally published | Yes |
Event | 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy Duration: May 27 2018 → May 30 2018 |
Publication series
Name | Proceedings - IEEE International Symposium on Circuits and Systems |
---|---|
Volume | 2018-May |
ISSN (Print) | 0271-4310 |
Conference
Conference | 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 |
---|---|
Country/Territory | Italy |
City | Florence |
Period | 05/27/18 → 05/30/18 |
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.