About this Research Topic
Over the past decade, artificial intelligence (AI) technology with bio-inspired neural networks has found widespread application in various aspects of our lives, from speech recognition to autonomous driving. However, it is inefficient to compute most AI algorithms by current computing systems with serial information processing since they are structurally far from the biological nervous system. For bridging this gap between algorithm and computing system, bio-inspired computing system, so-called neuromorphic system, has been intensively developed with neuroscience-driven brain-like processing manners as a suitable candidate. Nevertheless, challenges persist in achieving practical implementation, including hardware imperfections and the development of bio-plausible learning algorithms. Additionally, the longstanding aspiration of direct communication between biological and modern computing systems holds promise for enhancing our understanding of brain function and guiding bio-plausible computing technologies. Recent progress in nanoscale neural interfaces and biomimetic electronic devices can offer potential avenues for realizing this dream.
This research topic aims to consolidate interdisciplinary efforts focusing on, but not limited to:
- Emerging electronic devices, circuits and applications for neuromorphic computing
- Bio-inspired algorithms encompassing learning methods, network architectures, and information coding
- Hardware considerations for neuromorphic systems addressing faulty devices and signal noise
- Nanoscale neural interfaces, and bio-realistic functional devices facilitating communication between biological and artificial computing systems
- Processing-in-memory (PIM) technologies such as analog and stochastic computing to enhance computational efficiency
This research topic aims to consolidate interdisciplinary efforts focusing on, but not limited to:
- Emerging electronic devices, circuits and applications for neuromorphic computing
- Bio-inspired algorithms encompassing learning methods, network architectures, and information coding
- Hardware considerations for neuromorphic systems addressing faulty devices and signal noise
- Nanoscale neural interfaces, and bio-realistic functional devices facilitating communication between biological and artificial computing systems
- Processing-in-memory (PIM) technologies such as analog and stochastic computing to enhance computational efficiency
Keywords: Neuromorphic Computing, AI, Bio-inspired algorithms, Nanoscale neural interfaces
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
