Predicting perturbation effects from resting activity using functional causal flow

Abstract

Targeted manipulation of neural activity will be greatly facilitated by under-standing causal interactions within neural ensembles. Here, we introduce a novel statistical method to infer a network’s “functional causal flow” (FCF) from ensemble neural recordings. Using ground truth data from models of cortical circuits, we show that FCF captures functional hierarchies in the ensemble and reliably predicts the effects of perturbing individual neurons or neural clusters. Critically, FCF is robust to noise and can be inferred from the activity of even a small fraction of neurons in the circuit. It thereby permits accurate prediction of circuit perturbation effects with existing recording technologies for the primate brain. We confirm this prediction by recording changes in the prefrontal ensemble spiking activity of alert monkeys in response to single-electrode microstimulation. Our results provide a foundation for using targeted circuit manipulations to develop new brain-machine interfaces or ameliorate cognitive dysfunctions in the human brain.