Recent Advances of Deep Spiking Neural Networks and Neuromorphic Computation for Computer Vision

Deep Neural Networks (DNNs) and their applications for computer vision have been actively developed in recent years. Along with them, methods and systems for neuromorphic computation and Spiking Neural Networks (SNNs) in particular are rapidly emerging means of neural information processing, drawing inspiration from brain processes. The prominent background of this Research Topic is that SNNs have the potential to advance technologies and techniques in fields as neuromorphic systems, deep neural networks, computer vision, image and image processing, intelligent surveillance, artificial intelligence, and indeed any fields that involve complex temporal or spatiotemporal data. SNN, as the third generation of neural networks, is able to operate on noisy data, in changing environments at low power with high effectiveness. Due to the biological foundation principles, SNN research outcomes are strongly positioned to benefit from advances made in the fields of evolutionary and cognitive neuroscience. We believe that this area is quickly establishing itself as an effective alternative to traditional machine learning, computational vision, digital imaging, and the research interest in this area is growing rapidly.

This Research Topic aims to bring together research works of contemporary areas of SNNs for computer vision, including theoretical, computational, application-oriented, experimental studies, and emerging technologies in deep neural networks, neuromorphic systems, computer vision, imaging, and video technology.

The subjects related to this Research Topic include, but are not limited to:
• New theories of spike information representation
• Learning algorithms for Deep SNNs
• Theory and analytics of SNNs
• Optimization and analysis of SNNs
• Knowledge transfer between humans and SNN machines
• Theory or practice in biologically realistic neural simulation and biometrics models
• Spike encoding methods for computer vision
• SNN model visualization and visual scene understanding
• SNN models for visual information processing
• Evolving SNN for integrated audio-visual information processing
• SNN for brain-inspired computer vision
• SNN for brain-inspired dynamic imaging
• SNN for brain-inspired video technology
• SNN for brain-inspired image processing and artifact removal
• SNN for brain-inspired time series analysis
• SNN for brain-inspired autonomous vehicles
• SNN for brain-inspired artificial intelligence
• SNN applications in neuroinformatics and bioinformatics
• SNN applications in human expression and emotion recognition
• NeuCube and its applications
• SNN in neuro-robotics
• SNNs for interactions between humans and machines through vision and control
• Modelling brain EEG signals with AR/VR technology
• Any other topics related to SNN, relevant theory, and applications.