Convergence-Zone Episodic Memory: Analysis and Simulations

Moll, Mark; Miikkulainen, Risto

doi:10.1016/s0893-6080(97)00016-6

Cited by 51 publications

(33 citation statements)

References 60 publications

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“…The hippocampal component of the CLS model is part of a long tradition of hippocampal modeling (e.g., Marr, 1971;McNaughton & Morris, 1987;Rolls, 1989;Levy, 1989;Touretzky & Redish, 1996;Burgess & O'Keefe, 1996;Wu, Baxter, & Levy, 1996;Treves & Rolls, 1994;Moll & Miikkulainen, 1997;Hasselmo & Wyble, 1997). Although different hippocampal models may differ slightly in the functions they ascribe to particular hippocampal subcomponents, a remarkable consensus has emerged regarding how the hippocampus supports episodic memory (i.e., by assigning minimally overlapping CA3 representations to different episodes, with recurrent connectivity serving to bind together the constituent features of those episodes).…”

Section: Models Of Hippocampusmentioning

confidence: 99%

Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach.

Norman¹,

O’Reilly²

2003

Psychological Review

1,194

121

1,342

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We present a computational neural network model of recognition memory based on the biological structures of the hippocampus and medial temporal lobe cortex (MTLC), which perform complementary learning functions. The hippocampal component of the model contributes to recognition by recalling specific studied details. MTLC can not support recall, but it is possible to extract a scalar familiarity signal from MTLC that tracks how well the test item matches studied items. We present simulations that establish key qualitative differences in the operating characteristics of the hippocampal recall and MTLC familiarity signals, and we identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the two signals. We also use the model to address the stochastic relationship between recall and familiarity (i.e., are they independent), and the effects of partial vs. complete hippocampal lesions on recognition.

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Section: Models Of Hippocampusmentioning

confidence: 99%

Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach.

Norman¹,

O’Reilly²

2003

Psychological Review

1,194

121

1,342

View full text Add to dashboard Cite

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“…Some models have addressed how the hippocampus interacts with the neocortex, for example, in memory consolidation (McClelland et al, 1995;McClelland and Goddard, 1996;Murre, 1999;Kali and Dayan, 2000a). Other theories have focused on the role of the hippocampus as a binding site for storage and reactivation of widely distributed memories (e.g., Bibbig et al, 1995;Moll and Miikkulainen, 1997). Recently, some modelers have begun to address the complexities of what controls memory storage and retrieval (e.g., Becker and Lim, 2003).…”

Section: Hippocampal-cortical Interactionsmentioning

confidence: 99%

A computational principle for hippocampal learning and neurogenesis

2005

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In the three decades since Marr put forward his computational theory of hippocampal coding, many computational models have been built on the same key principles proposed by Marr: sparse representations, rapid Hebbian storage, associative recall and consolidation. Most of these models have focused on either the CA3 or CA1 fields, using "off-the-shelf" learning algorithms such as competitive learning or Hebbian pattern association. Here, we propose a novel coding principle that is common to all hippocampal regions, and from this one principal, we derive learning rules for each of the major pathways within the hippocampus. The learning rules turn out to have much in common with several models of CA3 and CA1 in the literature, and provide a unifying framework in which to view these models. Simulations of the complete circuit confirm that both recognition memory and recall are superior relative to a hippocampally lesioned model, consistent with human data. Further, we propose a functional role for neurogenesis in the dentate gyrus (DG), namely, to create distinct memory traces for highly similar items. Our simulation results support our prediction that memory capacity increases with the number of dentate granule cells, while neuronal turnover with a fixed dentate layer size improves recall, by minimizing interference between highly similar items.

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“…Models of long term memory that are based upon this theory have been built. Some of these models exhibit multiple, familiar properties of episodic memory (Moll and Miikkulainen 1997). While we are not aware of any of these models being used by an AI agent, these experiments provide further evidence for a distinction between episodic and semantic.…”

Section: Psychologymentioning

confidence: 72%