Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces

TL;DR

This paper discusses the development of neural co-processors that integrate decoding and encoding in brain-computer interfaces, enabling applications like rehabilitation, reanimation, and memory enhancement through deep learning frameworks.

Contribution

It introduces a unifying deep learning framework for simultaneous decoding and encoding, advancing the design of neuroprosthetic co-processors for diverse brain applications.

Findings

Proposes a neural network-based framework for co-processing in BCIs

Demonstrates potential for targeted neuro-rehabilitation and augmentation

Addresses multi-channel decoding and encoding challenges

Abstract

The field of brain-computer interfaces is poised to advance from the traditional goal of controlling prosthetic devices using brain signals to combining neural decoding and encoding within a single neuroprosthetic device. Such a device acts as a "co-processor" for the brain, with applications ranging from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory. We review recent progress in simultaneous decoding and encoding for closed-loop control and plasticity induction. To address the challenge of multi-channel decoding and encoding, we introduce a unifying framework for developing brain co-processors based on artificial neural networks and deep learning. These "neural co-processors" can be used to jointly optimize cost functions with the nervous system to achieve desired behaviors ranging from targeted neuro-rehabilitation to augmentation of brain function.