Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Modeling Sensorimotor Learning with Linear Dynamical Systems
Neural Computation
An implementation of reinforcement learning based on spike timing dependent plasticity
Biological Cybernetics
Learning flexible sensori-motor mappings in a complex network
Biological Cybernetics
Stochastic optimal control as a theory of brain-machine interface operation
Neural Computation
Dynamic analysis of naive adaptive brain-machine interfaces
Neural Computation
Dynamic analysis of naive adaptive brain-machine interfaces
Neural Computation
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Closed-loop operation of a brain-machine interface (BMI) relies on the subject's ability to learn an inverse transformation of the plant to be controlled. In this paper, we propose a model of the learning process that undergoes closed-loop BMI operation. We first explore the properties of the model and show that it is able to learn an inverse model of the controlled plant. Then, we compare the model predictions to actual experimental neural and behavioral data from nonhuman primates operating a BMI, which demonstrate high accordance of the model with the experimental data. Applying tools from control theory to this learning model will help in the design of a new generation of neural information decoders which will maximize learning speed for BMI users.