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Application of the neural data transformer to non-autonomous dynamical systems

dc.contributor.authorMifsud, Domenick M., author
dc.contributor.authorOrtega, Francisco R., advisor
dc.contributor.authorAnderson, Charles, advisor
dc.contributor.authorThomas, Micheal, committee member
dc.contributor.authorBarreto, Armando, committee member
dc.description2023 Summer.
dc.descriptionIncludes bibliographical references.
dc.description.abstractThe Neural Data Transformer (NDT) is a novel non-recurrent neural network designed to model neural population activity, offering faster inference times and the potential to advance real-time applications in neuroscience. In this study, we expand the applicability of the NDT to non-autonomous dynamical systems by investigating its performance on modeling data from the Chaotic Recurrent Neural Network (RNN) with delta pulse inputs. Through adjustments to the NDT architecture, we demonstrate its capability to accurately capture non-autonomous neural population dynamics, making it suitable for a broader range of Brain-Computer Inter-face (BCI) control applications. Additionally, we introduce a modification to the model that enables the extraction of interpretable inferred inputs, further enhancing the utility of the NDT as a powerful and versatile tool for real-time BCI applications.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.publisherColorado State University. Libraries
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see
dc.titleApplication of the neural data transformer to non-autonomous dynamical systems
dcterms.rights.dplaThis Item is protected by copyright and/or related rights ( You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). Science State University of Science (M.S.)


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