Low-Order Model of Biological Neural Networks

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2012.06720.pdf (705.28 KB)

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https://arxiv.org/abs/2012.06720

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http://hdl.handle.net/11603/20560

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UMBC Mathematics and Statistics Department
UMBC Faculty Collection

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2020-12-12

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journal article preprints
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Wang, Huachuan; Ting-Ho Lo, James; Low-Order Model of Biological Neural Networks; Optimization and Control (2020); https://arxiv.org/abs/2012.06720

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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.