Brain-inspired Navigation and Sensing for Robots

Recent years have witnessed the advent of the era of brain-inspired computing or neuromorphic computing. While deep learning methods have gained great success in a variety of tasks and applications, they often require a massive amount of computational resources. Brain-inspired computing is highly computationally efficient, and is an emerging interdisciplinary field that has great potential to build a more powerful next-generation computing paradigm of machine intelligence. Recently, some breakthroughs have been made in brain-inspired computing, including neuromorphic chips (e.g., Loihi, TrueNorth, Tianjic), brain-inspired algorithms (e.g., Spiking Neural Networks, Attractor Networks), event-based sensors (e.g., DAVIS event cameras, NeuTouch tactile sensor). However, there are still many challenges remained, such as how to develop new algorithms or models that are more powerful and energy-saving, and how to make the advances in neuroscience into real-world applications. Therefore, it is necessary to gather ideas and efforts from researchers to address these challenges.

This Research Topic aims to investigate the recent advances and development of brain-inspired computing, including brain-inspired hardware (e.g., neuromorphic chips), , algorithms (e.g., training algorithms of spiking neural networks), models (e.g., spiking neural networks), and event-based applications (e.g., brain-inspired navigation, segmentation, object recognition). It is expected that the answers to the following questions will be investigated in this Research Topic: 1) What are the present advances and future trends of neuromorphic computing? 2) How can new findings in neuroscience promote the development of new brain-inspired algorithms and models? 3) How to develop a brain-inspired algorithm for real-world applications?

This Research Topic will provide a platform for scientists and researchers to exchange ideas for promoting the development of brain-inspired computing, and targeting the next generation of artificial intelligence. All the original works related to brain-inspired computing or neuromorphic computing are welcome and encouraged. The primary list of topics for this Research Topic issue includes, but is not limited to:

• Brain-inspired navigation;

• Spiking neural networks;

• Neuromorphic computing theory, and algorithms;

• Brain-inspired robots;

• Robot perception;

• Spiking information encoding and decoding;

• Event-based learning;

• Brain-inspired models and algorithms;

• Applications of neuromorphic machine intelligence