Computing over in-vitro predictive coding neural cultures
Jain, Shrusti
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https://hdl.handle.net/2142/129979
Description
Title
Computing over in-vitro predictive coding neural cultures
Author(s)
Jain, Shrusti
Issue Date
2025-07-23
Director of Research (if dissertation) or Advisor (if thesis)
Rauchwerger, Lawrence
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Predictive Coding
Neuroscience
Bnns
Language
eng
Abstract
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.
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