Decoding the brain: From neural representations to mechanistic models.

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their function relies on distributed circuits to continuously encode relevant environmental and body-state features, enabling other areas to decode (interpret) these representations for computing meaningful decisions and executing precise movements. Thus, the distributed brain can be thought of as a series of computations that act to encode and decode information. In this perspective, we detail important concepts of neural encoding and decoding and highlight the mathematical tools used to measure them, including deep learning methods. We provide case studies where decoding concepts enable foundational and translational science in motor, visual, and language processing. Understanding how the brain works has been a formidable challenge in neuroscience, but advances at the computational level are now driving us closer. This perspective discusses core concepts of neural encoding and decoding and how moving toward causal modeling can enable translational and foundational insights into the neural code. [ABSTRACT FROM AUTHOR]