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Improving the control of neuroprosthetics to achieve biomimetic movements would significantly increase their utility and greatly improve the quality of life of their users. One potential addition to today's neuroprosthetics control systems would be an inclusion of the reward-based signal from motor or somatosensory cortex. The reward signal present in these cortices could indicate if a movement goal,...
Movement decoding algorithms used in today's brain-machine interface (BMI) technologies require movement-related neural activity in large quantities as training data to decode with sufficient accuracy the intended movements of the user. Because of physical disability the end users of BMI systems may be unable to readily provide such training data. Moreover, variability in the neural control of movements...
Our overall goal is to develop a reinforcement learning (RL) based decoder for brain machine interfaces. As an important step in this process, we determine the basic stability and convergence properties of a Temporal Difference (TD) RL architecture being driven by a simulated motor cortex.
In this work, we develop an experimental primate test bed for a center-out reaching task to test the performance of reinforcement learning based decoders for Brain-Machine Interfaces. Neural recordings obtained from the primary motor cortex were used to adapt a decoder using only sequences of neuronal activation and reinforced interaction with the environment. From a naïve state, the system was able...
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