Convergent temperature representations in artificial and biological neural networks

Abstract

While discoveries in biological neural networks (BNN) shaped artificial neural networks (ANN) it is unclear if representations and algorithms are shared between ANNs and BNNs performing similar tasks. Here, we designed and trained an ANN to perform heat gradient navigation and found striking similarities in computation and heat representation to a known zebrafish BNN. This included shared ON and OFF type representations of absolute temperature and rates of change. Importantly, ANN function critically relied on zebrafish like units. We could furthermore use the accessibility of the ANN to discover a new temperature responsive cell type in the zebrafish cerebellum. Finally, our approach generalized since training the same ANN constrained by the C. elegans motor repertoire resulted in distinct neural representations matching features observed in the worm. Together, these results emphasize convergence of ANNs and BNNs on canonical representations and that ANNs form a powerful tool to understand their biological counterparts.