Realtime decoding with state space models of neural activity

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dc.contributor.advisorKemere, Caleb T.en_US
dc.creatorChu, Joshua P.en_US
dc.date.accessioned2024-01-22T21:18:23Zen_US
dc.date.available2024-01-22T21:18:23Zen_US
dc.date.created2023-12en_US
dc.date.issued2023-10-06en_US
dc.date.submittedDecember 2023en_US
dc.date.updated2024-01-22T21:18:23Zen_US
dc.descriptionEMBARGO NOTE: This item is embargoed until 2024-12-01en_US
dc.description.abstractDecoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing neural activity. That in turn allows experimenters to test hypotheses about the role of that content in driving downstream activity patterns and behaviors. While multiple real-time systems have been developed, they are typically implemented in C++ and are locked to a specific data acquisition system, making them difficult to adapt to new experiments. Here we present a Python software system that implements online clusterless decoding, using state space models in a manner independent of data acquisition systems. The parallelized system processes neural data with temporal resolution of 6 ms and median computational latency < 50 ms for medium- to large-scale (32+ tetrodes) rodent hippocampus recordings without the need for spike sorting. It also executes auxiliary functions such as detecting sharp wave ripple and multiunit population burst events. Compared against state-of-the-art solutions which use compiled programming languages, performance is not significantly impacted. Next, we demonstrate its use in a live rat behavior experiment in which the decoder allowed closed loop neurofeedback based on decoding hippocampal activity. Early results indicate the possibility of strengthening neural representations ("replay") of spatial locations experienced in the real world.en_US
dc.embargo.lift2024-12-01en_US
dc.embargo.terms2024-12-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationChu, Joshua P. "Realtime decoding with state space models of neural activity." (2023) PhD diss., Rice University. https://hdl.handle.net/1911/115334en_US
dc.identifier.urihttps://hdl.handle.net/1911/115334en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectreal timeen_US
dc.subjectclusterless decodingen_US
dc.subjectparallel programmingen_US
dc.subjectreplayen_US
dc.titleRealtime decoding with state space models of neural activityen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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