A Deep Learning Framework for Neuroscience

July 13, 2024 Jeremiah Hendren

A Deep Learning Framework for Neuroscience

Richards, Blake A., Timothy Lillicrap, et al.
Nature Neuroscience 22, no. 11 (2019): 1761-1770.
https://doi.org/10.1038/s41593-019-0520-2

Major technical advances are revolutionizing our ability to observe and manipulate brains at a large scale and to quantify complex behaviors. How should we use this data to develop models of the brain? When the classical framework for systems neuroscience was developed, we could only record from small sets of neurons. In this framework, a researcher observes neural activity, develops a theory of what individual neurons compute, then assembles a circuit-level theory of how the neurons combine their operations. This approach has worked well for simple computations. For example, we know how central pattern generators control rhythmic movements, how the vestibulo-ocular reflex promotes gaze stabilization and how the retina computes motion. But can this classical framework scale up to recordings of thousands of neurons and all of the behaviors that we may wish to account for? Arguably, we have not had as much success with the classical approach in large neural circuits that perform a multitude of functions, like the neocortex or hippocampus. In such circuits, researchers often find neurons with response properties that are difficult to summarize in a succinct manner.

Black Richards, Timothy Lillicrap, et al.

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