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Precise control of neural systems is essential to experimental investigationsof how the brain controls behavior and holds the potential for therapeuticmanipulations to correct aberrant network states like epilepsy. Although purelyreactive controllers work well with single neurons and other simple neuralsystems, more sophisticated techniques are likely to be needed to controlcomplex circuits and systems with nonlinear recurrent dynamics, high levels ofexogenous noise, and limited information about the connectivity and state ofthe network. One potential approach is model predictive control (MPC), whichuses a dynamical model to find optimal control inputs by forecasting how thesystem will respond. However, the challenge still remains of selecting theright model, constraining its parameters, and synchronizing to the neuralsystem. As a proof of principle, we used recent advances in data-drivenforecasting to construct a nonlinear machine-learning model of a Hodgkin-Huxleytype neuron when only the membrane voltage is observable and there are anunknown number of intrinsic currents. We show that this approach is able tolearn the dynamics of different neuron types and can be used with MPC to forcethe neuron to engage in arbitrary, researcher-defined spiking behaviors.

Nonlinear Model Predictive Control of a Conductance-Based Neuron Model via Data-Driven Forecasting