Home
Resource
Non-neuronal computational principles for increased performance in brain-inspired networks
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(31.89 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Ivanov, Vladimir Alexandrovich. Non-neuronal computational principles for increased performance in brain-inspired networks. Retrieved from https://doi.org/doi:10.7282/t3-bm6a-7m07

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleNon-neuronal computational principles for increased performance in brain-inspired networks
NameIvanov, Vladimir Alexandrovich (author); Michmizos, Konstantinos (chair); Kulikowski, Casimir A (member); Metaxas, Dimitris N (member); Siettos, Constantinos I (member); Rutgers University; School of Graduate Studies
Date Created2022
Other Date2022-10 (degree)
SubjectArtificial intelligence, Neurosciences, Astrocytes, Critical brain dynamics, Edge of chaos, Liquid state machine, Oligodendrocytes, Spiking neural networks
Extent1 online resource (133 pages) : illustrations
DescriptionBrain-inspired neural networks promise to bring human-like machine learning and intelligence by exploiting our understanding of how the brain computes. Yet, current theories of brain information processing are solely focused on neuronal cells as the fundamental unit of computation, which is contradicted by increasing experimental evidence indicating that non-neuronal cells play a key role in brain computation. In this thesis, we used computational methods to investigate the role of two ubiquitous non-neuronal cells, oligodendrocytes and astrocytes, in neuronal network information processing. Our results suggest that oligodendrocytic impact on connection delays can significantly alter network synchrony and oscillation frequency. We propose experimentally testable hypotheses suggesting that astrocytes can signal when network dynamics deviate away from a computationally optimal regime and regulate network activity by modulating synaptic plasticity. Translating these findings to a brain-inspired learning framework that uses local learning unlike backpropagation methods, we present the neuron-astrocyte liquid state machine as a biologically plausible learning method that achieves comparable performance to feed-forward multi-layer spiking neural networks trained via backpropagation. Overall, these works explore non-neuronal computation in the brain and translate these insights to biologically plausible machine learning.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
Persistent URLhttps://doi.org/doi:10.7282/t3-bm6a-7m07
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024