# Neural Co-Processors for Restoring Brain Function: Results from a Cortical Model of Grasping

arXiv: https://arxiv.org/abs/2210.11478 · HTML: https://mlsanity.com/paper/2210.11478/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 55 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Modeling the dynamics of human brain activity with recurrent neural networks](https://mlsanity.com/paper/1606.03071/) (2016)
- [Brain Co-Processors: Using AI to Restore and Augment Brain Function](https://mlsanity.com/paper/2012.03378/) (2020)

## Built on by (most cited first, 3 of 3)

- [Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation](https://mlsanity.com/paper/2406.06714/) (2024)
- [Temporal Basis Function Models for Closed-Loop Neural Stimulation](https://mlsanity.com/paper/2507.15274/) (2025)
- [Robust Neural Stimulation Response Modeling Through Meta-Learning and Pretraining](https://mlsanity.com/paper/2608.26649/) (2026)

## Abstract (arXiv)

Objective: A major challenge in designing closed-loop brain-computer interfaces is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and objectives. Approach: To achieve goal-directed closed-loop neurostimulation, we propose "neural co-processors" which use artificial neural networks and deep learning to learn optimal closed-loop stimulation policies, shaping neural activity and bridging injured neural circuits for targeted repair and rehabilitation. The co-processor adapts the stimulation policy as the biological circuit itself adapts to the stimulation, achieving a form of brain-device co-adaptation. Here we use simulations to lay the groundwork for future in vivo tests of neural co-processors. We leverage a cortical model of grasping, to which we applied various forms of simulated lesions, allowing us to develop the critical learning algorithms and study adaptations to non-stationarity. Main results: Our simulations show the ability of a neural co-processor to learn a stimulation policy using a supervised learning approach, and to adapt that policy as the underlying brain and sensors change. Our co-processor successfully co-adapted with the simulated brain to accomplish the reach-and-grasp task after a variety of lesions were applied, achieving recovery towards healthy function. Significance: Our results provide the first proof-of-concept demonstration of a co-processor for adaptive activity-dependent closed-loop neurostimulation, optimizing for a rehabilitation goal. While a gap remains between simulations and applications, our results provide insights on how co-processors may be developed for learning complex adaptive stimulation policies for a variety of neural rehabilitation and neuroprosthetic applications.