A neurologically plausible artificial neural network computational architecture of episodic memory and recall

Craig M. Vineyard, Michael L. Bernard, Shawn E. Taylor, Thomas P. Caudell, Patrick Watson, Stephen Verzi, Neal J. Cohen, Howard Eichenbaum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Episodic memory is supported by the relational memory functions of the hippocampus. Building upon extensive neuroscience research on hippocampal processing, neural density, and connectivity we have implemented a computational architecture using variants of adaptive resonance theory artificial neural networks. Consequently, this model is capable of encoding, storing and processing multi-modal sensory inputs as well as simulating qualitative memory phenomena such as auto-association and recall. The performance of the model is compared with human subject performance. Thus, in this paper we present a neurologically plausible artificial neural network computational architecture of episodic memory and recall modeled after cortical-hippocampal structure and function.

Original languageEnglish (US)
Title of host publicationBiologically Inspired Cognitive Architectures 2010
PublisherIOS Press
Pages175-180
Number of pages6
ISBN (Print)9781607506607
DOIs
StatePublished - Jan 1 2010

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume221
ISSN (Print)0922-6389

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Keywords

  • Artificial neural network
  • computational model
  • hippocampus

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Vineyard, C. M., Bernard, M. L., Taylor, S. E., Caudell, T. P., Watson, P., Verzi, S., Cohen, N. J., & Eichenbaum, H. (2010). A neurologically plausible artificial neural network computational architecture of episodic memory and recall. In Biologically Inspired Cognitive Architectures 2010 (pp. 175-180). (Frontiers in Artificial Intelligence and Applications; Vol. 221). IOS Press. https://doi.org/10.3233/978-1-60750-661-4-175