Neuromorphic computing facilitates deep brain-machine fusion for high-performance neuroprosthesis

Frontiers in Neuroscience

Neuroscience

Yu Qi¹  Jiajun Chen¹  Yueming Wang² 

[1] Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of Medicine, Hangzhou, China;Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China;

关键词: brain-machine interface;    brain-computer interface;    neuromorphic model;    brain-like computing;    neuroprosthesis;    brain-machine fusion;   

DOI  :  10.3389/fnins.2023.1153985

 received in 2023-01-30, accepted in 2023-04-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

Brain-machine interfaces (BMI) have developed rapidly in recent years, but still face critical issues such as accuracy and stability. Ideally, a BMI system would be an implantable neuroprosthesis that would be tightly connected and integrated into the brain. However, the heterogeneity of brains and machines hinders deep fusion between the two. Neuromorphic computing models, which mimic the structure and mechanism of biological nervous systems, present a promising approach to developing high-performance neuroprosthesis. The biologically plausible property of neuromorphic models enables homogeneous information representation and computation in the form of discrete spikes between the brain and the machine, promoting deep brain-machine fusion and bringing new breakthroughs for high-performance and long-term usable BMI systems. Furthermore, neuromorphic models can be computed at ultra-low energy costs and thus are suitable for brain-implantable neuroprosthesis devices. The intersection of neuromorphic computing and BMI has great potential to lead the development of reliable, low-power implantable BMI devices and advance the development and application of BMI.

【 授权许可】

Unknown   
Copyright © 2023 Qi, Chen and Wang.