A Neurologically Plausible Artificial Neural Network Computational Architecture of Episodic Memory and Recall

  • Authors:

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

  • Affiliations:

  • Sandia National Laboratories, PO Box 5800 Albuquerque, NM 87185 1188 and University of New Mexico;Sandia National Laboratories, PO Box 5800 Albuquerque, NM 87185 1188;Sandia National Laboratories, PO Box 5800 Albuquerque, NM 87185 1188;University of New Mexico;University of Illinois at Urbana-Champaign Beckman Institute;Sandia National Laboratories, PO Box 5800 Albuquerque, NM 87185 1188;University of Illinois at Urbana-Champaign Beckman Institute;Boston University

  • Venue:
  • Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
  • Year:
  • 2010

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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.