A model of motor learning in closed-loop brain-machine interfaces: predicting neural tuning changes

This paper presents a model of the learning process occurring during operation of a closed-loop brain-machine interface (BMI). The learning model updates neuron firing properties based on a feedback-error learning scheme, featuring feedforward and feedback controllers. Our goal is to replicate in simulation experimental results showing functional reorganization of neuronal ensembles during BMI experiments. We show that the proposed model can simulate motor learning, and that the predicted changes in neuronal tuning are consistent with experimental observations. We believe that being able to simulate motor learning in a BMI context will allow designing decoders that would facilitate the learning process in real world experiments.