Low-Order Model of Biological Neural Networks
dc.contributor.author | Wang, Huachuan | |
dc.contributor.author | Ting-Ho Lo, James | |
dc.date.accessioned | 2021-01-20T18:04:06Z | |
dc.date.available | 2021-01-20T18:04:06Z | |
dc.date.issued | 2020-12-12 | |
dc.description.abstract | A 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.sponsorship | This project is supported by National Science Foundation. | en_US |
dc.description.uri | https://arxiv.org/abs/2012.06720 | en_US |
dc.format.extent | 7 pages | en_US |
dc.genre | journal article preprints | en_US |
dc.identifier | doi:10.13016/m2vqcw-rzj5 | |
dc.identifier.citation | Wang, Huachuan; Ting-Ho Lo, James; Low-Order Model of Biological Neural Networks; Optimization and Control (2020); https://arxiv.org/abs/2012.06720 | en_US |
dc.identifier.uri | http://hdl.handle.net/11603/20560 | |
dc.language.iso | en_US | en_US |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Mathematics Department Collection | |
dc.relation.ispartof | UMBC Faculty Collection | |
dc.rights | This 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.rights | Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) | * |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Low-Order Model of Biological Neural Networks | en_US |
dc.type | Text | en_US |