Firing Rate Neural Network Implementations of Model Predictive Control
This provides insights into neural mechanisms for planning, relevant to neuroscience and robotics, but is incremental as it adapts existing MPC methods to neural models.
The authors tackled the problem of how brains implement model predictive control (MPC) by translating MPC into firing rate neural networks, showing that sparse networks can effectively balance an inverted pendulum in simulations.
Human and animal brains perform planning to enable complex movements and behaviors. This process can be effectively described using model predictive control (MPC); that is, brains can be thought of as implementing some version of MPC. How is this done? In this work, we translate model predictive controllers into firing rate neural networks, offering insights into the nonlinear neural dynamics that underpin planning. This is done by first applying the projected gradient method to the dual problem, then generating alternative networks through factorization and contraction analysis. This allows us to explore many biologically plausible implementations of MPC. We present a series of numerical simulations to study different neural networks performing MPC to balance an inverted pendulum on a cart (i.e., balancing a stick on a hand). We illustrate that sparse neural networks can effectively implement MPC; this observation aligns with the sparse nature of the brain.