Learning precise spatiotemporal sequences via biophysically realistic neural circuits with modular structure

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dc.contributor.advisorShouval, Harelen_US
dc.contributor.advisorPitkow, Xaqen_US
dc.creatorCone, Ianen_US
dc.date.accessioned2020-06-05T17:24:04Zen_US
dc.date.available2020-06-05T17:24:04Zen_US
dc.date.created2020-08en_US
dc.date.issued2020-05-27en_US
dc.date.submittedAugust 2020en_US
dc.date.updated2020-06-05T17:24:04Zen_US
dc.description.abstractThe ability to express and learn temporal sequences is an essential part of neural learning and memory. Learned temporal sequences are expressed in multiple brain regions and as such there may be common design in the circuits that mediate it. This thesis proposes a substrate for such representations, via a biophysically realistic network model that can robustly learn and recall discrete sequences of variable order and duration. The model consists of a network of spiking leaky-integrate-and-fire model neurons placed in a modular architecture designed to resemble cortical microcolumns. Learning is performed via a learning rule with “eligibility traces”, which hold a history of synaptic activity before being converted into changes in synaptic strength upon neuromodulator activation. Before training, the network responds to incoming stimuli, and contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. This model provides a possible framework for biologically realistic sequence learning and memory, and is in agreement with recent experimental results, which have shown sequence dependent plasticity in sensory cortex.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationCone, Ian. "Learning precise spatiotemporal sequences via biophysically realistic neural circuits with modular structure." (2020) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/108773">https://hdl.handle.net/1911/108773</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/108773en_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.subjectsequence learningen_US
dc.subjectsequence recallen_US
dc.subjectneural circuitsen_US
dc.subjectnon-Markovian sequencesen_US
dc.subjectbiophysically realistic modelsen_US
dc.subjectrecurrent neural networksen_US
dc.titleLearning precise spatiotemporal sequences via biophysically realistic neural circuits with modular structureen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentApplied Physics/Electrical Engen_US
thesis.degree.disciplineNatural Sciencesen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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