title: Learning to use a brain-machine interface: model, simulation and analysis.
abstract: 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.
authors: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.
authors: Jessica
authors: J
authors: Jimenez
authors: Rodolphe
authors: R
authors: Heliot
authors: Jose M
authors: JM
authors: Carmena
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D007858:Learning / Q000502:physiology*; D008328:Man-Machine Systems; D008954:Models, Biological; D009010:Monte Carlo Method; D012680:Sensitivity and Specificity