Low-Order Model of Biological Neural Networks

dc.contributor.authorWang, Huachuan
dc.contributor.authorTing-Ho Lo, James
dc.date.accessioned2021-01-20T18:04:06Z
dc.date.available2021-01-20T18:04:06Z
dc.date.issued2020-12-12
dc.description.abstractA biologically plausible low-order model (LOM) of biological neural networks is a recurrent hierarchical network of dendritic nodes/trees, spiking/nonspiking neurons, unsupervised/ supervised covariance/accumulative learning mechanisms, feedback connections, and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect and recognize multiple/hierarchical corrupted, distorted, and occluded temporal and spatial patterns.en_US
dc.description.sponsorshipThis project is supported by National Science Foundation.en_US
dc.description.urihttps://arxiv.org/abs/2012.06720en_US
dc.format.extent7 pagesen_US
dc.genrejournal article preprintsen_US
dc.identifierdoi:10.13016/m2vqcw-rzj5
dc.identifier.citationWang, Huachuan; Ting-Ho Lo, James; Low-Order Model of Biological Neural Networks; Optimization and Control (2020); https://arxiv.org/abs/2012.06720en_US
dc.identifier.urihttp://hdl.handle.net/11603/20560
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleLow-Order Model of Biological Neural Networksen_US
dc.typeTexten_US

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