{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129979"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129979","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Computing over in-vitro predictive coding neural cultures","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Shrusti Jain, accepted the attached license on 2025-07-22 at 20:52.","The student, Shrusti Jain, submitted this Thesis for approval on 2025-07-22 at 20:58.","This Thesis was approved for publication on 2025-07-23 at 09:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22701 on 2025-10-20 at 20:15:41","While artificial neural networks are gaining in popularity, they still fall behind biological neural substrates in terms of energy efficiency and performance on certain computational tasks. However, the mechanisms by which neurons operate are still not well understood, making it difficult to harness their computational power for arbitrary tasks. Predictive coding is an influential theory of learning and inference within neuroscience, positing that neural systems adapt to best predict sensory input across time, thus representing the sensory distribution within an internal generative model encoded through synaptic connections. However, previous work on predictive coding has been limited to modeling relations between higher-level units of the brain. Here, we present a biologically plausible model of predictive coding generalized to arbitrary topologies of in-vitro cultures. In addition, we present a novel framework to harness neural cultures that implement predictive coding for computational tasks."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Computing over in-vitro predictive coding neural cultures"]}]}],"canonical_facts":{"dc:contributor":["Rauchwerger, Lawrence"],"dc:creator":["Jain, Shrusti"],"dc:date":["2025-07-23","2025-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Shrusti Jain, accepted the attached license on 2025-07-22 at 20:52.","The student, Shrusti Jain, submitted this Thesis for approval on 2025-07-22 at 20:58.","This Thesis was approved for publication on 2025-07-23 at 09:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22701 on 2025-10-20 at 20:15:41","While artificial neural networks are gaining in popularity, they still fall behind biological neural substrates in terms of energy efficiency and performance on certain computational tasks. However, the mechanisms by which neurons operate are still not well understood, making it difficult to harness their computational power for arbitrary tasks. Predictive coding is an influential theory of learning and inference within neuroscience, positing that neural systems adapt to best predict sensory input across time, thus representing the sensory distribution within an internal generative model encoded through synaptic connections. However, previous work on predictive coding has been limited to modeling relations between higher-level units of the brain. Here, we present a biologically plausible model of predictive coding generalized to arbitrary topologies of in-vitro cultures. In addition, we present a novel framework to harness neural cultures that implement predictive coding for computational tasks."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129979"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Shrusti Jain"],"dc:subject":["Predictive Coding","Neuroscience","Bnns"],"dc:title":["Computing over in-vitro predictive coding neural cultures"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}