Learning to use a brain-machine interface: Model, simulation and analysis

This paper presents a model of the learning process occurring during operation of a closed-loop brain-machine interface. The model consists of a population of simulated cortical neurons, a decoder that transforms neural activity into motor output, a feedback controller whose role is to reduce the error based on an error-descent algorithm, and an open-loop controller whose parameters are updated based on the corrections made by the feedback controller. We present evidence of the convergence of the internal model to the decoder´s inverse model and use global sensitivity analysis to study the convergence´s dependence on the parameters of the overall learning model. This model can be used as a simulation tool that predicts the outcome of closed-loop BMI experiments.