Towards model-based design of causal manipulations of brain circuits with high spatiotemporal precision

TL;DR

This paper proposes a model-based approach to design precise spatiotemporal neural stimulation patterns that can steer brain activity towards desired targets, using minimal observations and advanced control methods.

Contribution

It introduces a two-step method combining data-driven identification of key neural sites with control theory to optimize multi-site stimulation patterns.

Findings

Feasibility demonstrated in recurrent network models

Efficient identification of key neural hubs from short observations

Potential to improve targeted neural interventions

Abstract

Recent advancements in neurotechnology enable precise spatiotemporal patterns of microstimulations with single-cell resolution. The choice of perturbation sites must satisfy two key criteria: efficacy in evoking significant responses and selectivity for the desired target effects. This choice is currently based on laborious trial-and-error procedures, unfeasible for sequences of multi-site stimulations. Efficient methods to design complex perturbation patterns are urgently needed. Can we design a spatiotemporal pattern of stimulation to steer neural activity and behavior towards a desired target? We outline a method for achieving this goal in two steps. First, we identify the most effective perturbation sites, or hubs, only based on short observations of spontaneous neural activity. Second, we provide an efficient method to design multi-site stimulation patterns by combining approaches from nonlinear dynamical systems, control theory and data-driven methods. We demonstrate the feasibility of our approach using multi-site stimulation patterns in recurrent network models.