Trisha Jha

Models of cross-region neural dynamics have largely stayed linear, while each region's recorded activity mixes local dynamics with dynamics shared across regions. CroP-DYNO sets learning directly on predicting target-population activity from source activity, so shared dynamics are extracted first, and it lets each model component be specified as linear or nonlinear. On macaque S1–M1 and rat CA1–CA3 data it predicts target activity better than a non-prioritized nonlinear model and a prioritized linear model, and it localizes nonlinear structure and yields interaction strengths consistent with known circuit anatomy.

Models of cross-region neural dynamics have largely stayed linear, while each region's recorded activity mixes local dynamics with dynamics shared across regions. CroP-DYNO sets learning directly on predicting target-population activity from source activity, so shared dynamics are extracted first, and it lets each model component be specified as linear or nonlinear. On macaque S1–M1 and rat CA1–CA3 data it predicts target activity better than a non-prioritized nonlinear model and a prioritized linear model, and it localizes nonlinear structure and yields interaction strengths consistent with known circuit anatomy.