Functional model of biological neural networks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-04-20

AUTHORS

James Ting-Ho Lo

ABSTRACT

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks. More... »

PAGES

295-313

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11571-010-9110-4

DOI

http://dx.doi.org/10.1007/s11571-010-9110-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023822231

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22132040


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