Simulation of neural function in an artificial Hebbian network

Artificial neural networks have diverged far from their early inspiration in neurology. In spite of their technological and commercial success, they have several shortcomings, most notably the need for a large number of training examples and the resulting computation resources required for iterative...

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Bibliographic Details
Published inarXiv.org
Main Authors J Campbell Scott, Hayes, Thomas F, Ozcan, Ahmet S, Wilcke, Winfried W
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 02.12.2019
Subjects
Artificial neural networks
Computer simulation
Iterative methods
Neurology
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Summary:Artificial neural networks have diverged far from their early inspiration in neurology. In spite of their technological and commercial success, they have several shortcomings, most notably the need for a large number of training examples and the resulting computation resources required for iterative learning. Here we describe an approach to neurological network simulation, both architectural and algorithmic, that adheres more closely to established biological principles and overcomes some of the shortcomings of conventional networks.
ISSN:2331-8422