Integrating artificial and biological neural networks to improve animal task performance using deep reinforcement learning

Abstract

Artificial neural networks have performed remarkable feats in a wide variety of domains. However, artificial intelligence algorithms lack the flexibility, robustness, and generalization power of biological neural networks. Given the different capabilities of artificial and biological neural networks, it would be advantageous to build systems where the two types of networks are directly connected and can synergistically interact. As proof of principle, here we show how to create such a hybrid system and how it can be harnessed to improve animal performance on biologically relevant tasks. Using optogenetics, we interfaced the nervous system of the nematode Caenorhabditis elegans with a deep reinforcement learning agent, enabling the animal to navigate to targets and enhancing its natural ability to search for food. Agents adapted to strikingly different sites of neural integration and learned site-specific activation patterns to improve performance on a target-finding task. The combined animal and agent displayed cooperative computation between artificial and biological neural networks by generalizing target-finding to novel environments. This work constitutes an initial demonstration of how to robustly improve task performance in animals using artificial intelligence interfaced with a living nervous system.