# Brain Co-Processors: Using AI to Restore and Augment Brain Function

arXiv: https://arxiv.org/abs/2012.03378 · HTML: https://mlsanity.com/paper/2012.03378/

## Key facts

- Cited by 5 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 6 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation](https://mlsanity.com/paper/2406.10395/) (2024)
- [Neural Co-Processors for Restoring Brain Function: Results from a Cortical Model of Grasping](https://mlsanity.com/paper/2210.11478/) (2022)
- [Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance Analysis](https://mlsanity.com/paper/2408.15121/) (2024)
- [Evaluating Deep Human-in-the-Loop Optimization for Retinal Implants Using Sighted Participants](https://mlsanity.com/paper/2502.00177/) (2025)
- [Temporal Basis Function Models for Closed-Loop Neural Stimulation](https://mlsanity.com/paper/2507.15274/) (2025)

## Abstract (arXiv)

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.