Modeling and experimental validation of the learning process during closed-loop BMI operation

This paper presents a model and experimental validation of the learning process 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. Using this approach, we show that the population of neurons can learn the inverse model of the decoder. Then, we validate the model by comparing its predictions with real experimental data recorded from a macaque monkey. Such a simulation tool will be useful to predict the behavior of a closed-loop BMI and in the design of optimal decoders.