@inproceedings{d2ea8937d83541879ac6de982a4d3dd4,
title = "A neurologically plausible artificial neural network computational architecture of episodic memory and recall",
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.",
keywords = "Artificial neural network, computational model, hippocampus",
author = "Vineyard, {Craig M.} and Bernard, {Michael L.} and Taylor, {Shawn E.} and Caudell, {Thomas P.} and Patrick Watson and Stephen Verzi and Cohen, {Neal J.} and Howard Eichenbaum",
year = "2010",
doi = "10.3233/978-1-60750-661-4-175",
language = "English (US)",
isbn = "9781607506607",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "175--180",
booktitle = "Biologically Inspired Cognitive Architectures 2010",
}