Spiking Neural Networks are a rapidly emerging means of neural information processing, drawing inspiration from biological processes. There is presently considerable interest in this topic, especially with the recent announcement of large scale projects such as the “BRAIN Initiative” (US) and the “Human Brain Project” (EU). Due to their inspiration from human brain processes, SNN have the potential to advance technologies and techniques in fields as diverse as medicine, finance, computing, and indeed any field that involves complex spatio-temporal data. SNN can operate on noisy data, in changing environments at low power and with high effectiveness. We believe that this area is quickly establishing itself as an effective alternative to traditional machine learning technologies, and that interest in this area of research is growing rapidly. In this special session we intend to provide a platform for the discussion of contemporary areas of SNN, including theory, applications, and emerging technologies such as neuromorphic hardware.
Scope and Topics:Topics of interest include, but are not limited to the following:
- Novel architectures
- SNN applications and case studies
- Learning algorithms for SNN, including Deep Learning
- Big data and stream data processing in SNN
- Theory or practice in biologically realistic neural simulation or biomimetic models
- Neuromorphic hardware systems and applications
- Robotic applications of SNN
- Theory of SNN
- Optimisation of SNN
- Evolving SNN
- Any other topics relating to Spiking Neural Networks, their theory, or application.
See http://www.wcci2016.org for conference and submission details.
The paper submission deadline is the 15th of January, 2016