2020/12/06 by Rajesh P. N. Rao, Rao, Rajesh P. N. · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Neuroscience · Physics and Astronomy · Psychology · #Advanced Memory and Neural Computing #Algorithm #Artificial intelligence #Artificial neural network #Brain activity and meditation #Brain stimulation #Brain–computer interface #Computer science #Decoding methods #EEG and Brain-Computer Interfaces #Electroencephalography #Encoding (memory) #Hebbian theory #Neural decoding #Neural engineering #Neuroscience #Neuroscience and Neural Engineering #Psychology #Sensory system #Stimulation #cs.AI #cs.NE #nlin.AO #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2012.03378
published in arXiv (Cornell University) (Cornell University) · arXiv admin note: text overlap with arXiv:1811.11876
arxiv created 2020/12/06 · openalex publication_date 2020/12/06 · arxiv updated 2020/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Brain-computer interfaces (BCIs) use decoding algorithms to control prosthetic devices based on brain signals for restoration of lost function. Computer-brain interfaces (CBIs), on the other hand, use encoding algorithms to transform external sensory signals into neural stimulation patterns for restoring sensation or providing sensory feedback for closed-loop prosthetic control. In this article, we introduce brain co-processors, devices that combine decoding and encoding in a unified framework using artificial intelligence (AI) to supplement or augment brain function. Brain co-processors can be used for a range of applications, from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory. A key challenge is simultaneous multi-channel neural decoding and encoding for optimization of external behavioral or task-related goals. We describe a new framework for developing brain co-processors based on artificial neural networks, deep learning and reinforcement learning. These "neural co-processors" allow joint optimization of cost functions with the nervous system to achieve desired behaviors. By coupling artificial neural networks with their biological counterparts, neural co-processors offer a new way of restoring and augmenting the brain, as well as a new scientific tool for brain research. We conclude by discussing the potential applications and ethical implications of brain co-processors.