Large-scale neural recordings call for new insights to link brain and behavior

TL;DR

The paper discusses the rise of large-scale neural recordings, the challenges they pose to traditional theories, and the need for new models to better understand brain-behavior relationships.

Contribution

It reviews emerging tools for large-scale neural data collection, highlights insights from diverse models, and advocates for novel theoretical frameworks to interpret these complex datasets.

Findings

Large-scale recordings reveal complex brain activity patterns.

Traditional theories struggle to explain new neural data.

New modeling approaches are needed for better understanding.

Abstract

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. Here, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modelling frameworks to interpret these data, and argue that interpreting brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding at stake. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.