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A major problem in motor control is understanding how the brain plans andexecutes proper movements in the face of delayed and noisy stimuli. A prominentframework for addressing such control problems is Optimal Feedback Control(OFC). OFC generates control actions that optimize behaviorally relevantcriteria by integrating noisy sensory stimuli and the predictions of aninternal model using the Kalman filter or its extensions. However, asatisfactory neural model of Kalman filtering and control is lacking becauseexisting proposals have the following limitations: not considering the delay ofsensory feedback, training in alternating phases, and requiring knowledge ofthe noise covariance matrices, as well as that of systems dynamics. Moreover,the majority of these studies considered Kalman filtering in isolation, and notjointly with control. To address these shortcomings, we introduce a novelonline algorithm which combines adaptive Kalman filtering with a model freecontrol approach (i.e., policy gradient algorithm). We implement this algorithmin a biologically plausible neural network with local synaptic plasticityrules. This network performs system identification and Kalman filtering,without the need for multiple phases with distinct update rules or theknowledge of the noise covariances. It can perform state estimation withdelayed sensory feedback, with the help of an internal model. It learns thecontrol policy without requiring any knowledge of the dynamics, thus avoidingthe need for weight transport. In this way, our implementation of OFC solvesthe credit assignment problem needed to produce the appropriate sensory-motorcontrol in the presence of stimulus delay.

Neural optimal feedback control with local learning rules