Sean R. Bittner

Interrogating theoretical models of neural computation with emergent property inference

This paper introduces emergent property inference (EPI), a method using deep learning to solve inverse problems in theoretical neuroscience by learning parameter distributions consistent with computational properties.

Neural trajectories in the supplementary motor area and primary motor cortex exhibit distinct geometries, compatible with different classes of computation

The paper compares trajectory geometries in the supplementary motor area (SMA) and primary motor cortex (M1) to infer different computational roles, finding SMA trajectories are helical and M1 are elliptical.