The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Reinforcement learning (RL)-based decoders in brain–machine interfaces (BMIs) interpret dynamic neural activity without patients’ real limb movements. In conventional RL, the goal state is selected by the user or defined by the physics of the problem, and the decoder finds an optimal policy essentially by assigning credit over time, which is normally very time-consuming. However, BMI tasks require...
Reinforcement learning is an effective algorithm for brain machine interfaces (BMIs) which interprets the mapping between neural activities with plasticity and the kinematics. Exploring large state-action space is difficulty when the complicated BMIs needs to assign credits over both time and space. For BMIs attention gated reinforcement learning (AGREL) has been developed to classify multi-actions...
Many practical data streams are typically composed of several states known as regimes. In this paper, we invoke phase space reconstruction methods from non-linear time series and dynamical systems for regime detection. But the data collected from sensors is normally noisy, does not have constant amplitude and is sometimes plagued by shifts in the mean. All these aspects make modeling even more difficult...
Visualizing the collective modulation of multiple neurons during a known behavioral task is useful for exploratory analysis, but handling the large dimensionality of neural recordings is challenging. We further investigate using static dimensionality reduction techniques on neural firing rate data during an arm movement task. This lower-dimensional representation of the data is able to capture the...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.