Imagine you have a fancy machine with two buttons, one that triggers lots of activity and one that calms it down. Now, to make things interesting, imagine that this machine represents a biophysically realistic model of the brain, consisting of excitatory and inhibitory neurons. Scientists are trying to figure out the most efficient way to control the activity levels of these neurons. Using the framework of nonlinear optimal control, they explore different strategies to find the best control function. They focus on a region in the brain’s state space where there are two stable activity states: high-activity and low-activity. The goal is to switch between these two states with the least amount of effort or ‘strength’ of control. By analyzing various cost functions, which measure the deviation from the desired activity state and the strength of the control signal, they discover that the most efficient strategy involves applying a brief pulse of control to nudge the system towards the target state. However, this approach becomes less effective when time constraints come into play. Ultimately, this study demonstrates how nonlinear optimal control can shed light on how the brain processes information under different conditions. To dive deeper into the fascinating research behind neural population dynamics and optimal control, check out the full article!

We apply the framework of nonlinear optimal control to a biophysically realistic neural mass model, which consists of two mutually coupled populations of deterministic excitatory and inhibitory neurons. External control signals are realized by time-dependent inputs to both populations. Optimality is defined by two alternative cost functions that trade the deviation of the controlled variable from its target value against the “strength” of the control, which is quantified by the integrated 1- and 2-norms of the control signal. We focus on a bistable region in state space where one low- (“down state”) and one high-activity (“up state”) stable fixed points coexist. With methods of nonlinear optimal control, we search for the most cost-efficient control function to switch between both activity states. For a broad range of parameters, we find that cost-efficient control strategies consist of a pulse of finite duration to push the state variables only minimally into the basin of attraction of the target state. This strategy only breaks down once we impose time constraints that force the system to switch on a time scale comparable to the duration of the control pulse. Penalizing control strength via the integrated 1-norm (2-norm) yields control inputs targeting one or both populations. However, whether control inputs to the excitatory or the inhibitory population dominate, depends on the location in state space relative to the bifurcation lines. Our study highlights the applicability of nonlinear optimal control to understand neuronal processing under constraints better.

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