Scope and Aim
Spiking Neural Networks using Neuromorphic Technologies offer significant reduction in system size, weight and power (SWaP) requirements compared to conventional neural network architectures. The use of spike information, flowing through a neural network that is closer aligned to human information processing than conventional neural networks, represents new challenges to designers of machine learning algorithms and systems. Questions such as ‘which spiking neural network architectures are best for specific applications ?’ , ‘ how does one train spiking neural networks using back propagation related approaches ?’ and ‘what sensors can one use to drive a spiking neural network ?’ are subjects of significant research at the present time. The scope of this special session is to look at the state of art research in the area of neuromorphic technology based spiking neural networks. The special session will look at neuromorphic sensors, training neuromorphic based spiking neural network architectures and use of such architectures in a range of applications.
Topics Covered
The aim of this special session on ‘Neuromorphic Sensing, Processing and Applications’ is to provide a forum for engineers, scientists, and researchers of this field to exchange the latest advances in architectures, processing and applications on neuromorphic sensing technologies and related processing. Topics that are of interest to this session include, but are not limited to:
· Deadline of Full Paper Submission: January 15, 2020
· Notification of Paper Acceptance: March 15, 2020
· Camera Ready Submission of Accepted Papers: April 15, 2020
· IEEE IJCNN/WCCI 2020, Glasgow, Scotland, UK : July 19-24, 2020
Submission Guidelines
This special session will be held in 2020 International Joint Conference on Neural Networks-IJCNN (wcci2020.org/ijcnn-sessions/), as part of 2020 IEEE World Congress on Computational Intelligence (https://wcci2020.org/ ) (Glasgow, Scotland, United Kingdom, July 19-24, 2020). All papers should be prepared according to the IJCNN 2020 policy and should be submitted electronically using the conference website (https://wcci2020.org/ ) . To submit your paper to this special session, you will choose our special session on the submission page " Neuromorphic Sensing, Processing and Applications ". All papers accepted and presented at IEEE IJCNN/WCCI 2020 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.
Organizers:
Biographies:
John J. Soraghan (S’83–M’84–SM’96) received the B.Eng. (Hons.) and M.Eng.Sc. degrees in electronic engineering from University College Dublin, Dublin, Ireland, and the Ph.D. degree in electronic engineering from the University of Southampton, Southampton, U.K. His doctoral research focused on synthetic aperture radar processing on the distributed array processor. After graduating, he worked with the Electricity Supply Board in Ireland and with Westinghouse Electric Corporation in the U.S. In 1986, he joined the Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.K as a lecturer. He was a Manager of the Scottish Transputer Centre from 1988 to 1991, Manager with the DTI Parallel Signal Processing Centre from 1991 to 1995 and Head of the ICSP from 2005-2007. He became a Professor in Signal Processing in 2003 and held the Texas Instruments Chair in Signal Processing from 2004-2016. He is currently the Director of the Sensor Signal Processing Research Groups and Director of the Centre for Neuromorphic Sensing and Processing within the Centre for Signal and Image Processing (CeSIP) at Strathclyde. His main research interests are signal processing and machine learning theories, algorithms, with applications to radar, sonar and acoustics, biomedical signal and image processing, video & speech analytics, and condition monitoring. Professor Soraghan has supervised 52 researchers to PhD graduation and has published over 340 technical publications.
Dr Gaetano Di Caterina received his BEng degree in Computer Engineering from the University of Naples in 2005, and his MEng degree in Computer and Electronic System and PhD degree from the University of Strathclyde in 2009 and 2013 respectively, with the focus of his PhD work being on image and video processing in the context of CCTV system, with an interest in embedded solutions. He is currently the Leonardo Lecturer in the Electronic and Electrical Engineering Department at the University of Strathclyde, where he is part of the Centre for Signal and Image Processing group. Dr Di Caterina is also involved in the Neuromorphic Sensing and Processing Centre, in the EEE department at Strathclyde, where he currently supervises undergraduate and PhD students, and regularly engages with industry to disseminate the work of the group on Deep Learning and Neuromorphic technologies. Dr Di Caterina is the course director of the MSc in Machine Learning and Deep Learning at Strathclyde. Dr Di Caterina has published his work on Machine Learning, Deep Learning, Neuromorphic technologies and Signal Processing at several international conferences and in academic journals (https://orcid.org/0000-0002-7256-0897).