Harnessing behavioral diversity to understand circuits for cognition

TL;DR

This paper discusses how analyzing neural data during complex behaviors, combined with artificial neural networks, can improve understanding of neural circuits underlying cognition and behavioral diversity.

Contribution

It highlights the importance of rich behavioral data and artificial neural networks in modeling cognitive processes and understanding neural diversity.

Findings

Recording during rich behaviors enhances neural understanding

Artificial neural networks help link neural activity to behavior

Behavioral diversity can be explained through neural modeling

Abstract

With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we review emerging conceptual and technological advances that begin to address this challenge, garnering insights from both biological and artificial neural networks. We argue that neural data should be recorded during rich behavioral tasks, to model cognitive processes and estimate latent behavioral variables. Careful quantification of animal movements can also provide a more complete picture of how movements shape neural dynamics and reflect changes in brain state, such as arousal or stress. Artificial neural networks (ANNs) could serve as an important tool to connect neural dynamics and rich behavioral data. ANNs have already begun to reveal how particular behaviors can be optimally solved, generating hypotheses about how observed neural activity might drive behavior and explaining diversity in behavioral strategies.