Hippocampal mediation of stimulus representation: A computational theory

By Mark A. Gluck, Catherine E. Myers. 1993

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Citations in the corpus

Interactive memory systems in the human brain (2001, background): “However, this dependence rapidly declined with training, as predicted by previous computational models of associative learnin”
Revision of Retrieval Theory of Forgetting: What does Make Information Context-Specific? (2006, unknown): “…might receive some attention -the assumption of Myers and Gluck about decreasing attention to the context with increasing the number of trials (Gluck & Myers, 1993; Myers & Gluck, 1994) might explain why acquisition context is processed, but it is not used until the outcome is changed during…”
Context and behavioral processes in extinction. (2004, background): “One possibility, therefore, is that it is in part encoded as a feature of the context, making it difficult to extract it from that context when it is paired with the US in phase 2 (cf. Gluck and Myers 1993).”
A computational principle for hippocampal learning and neurogenesis (2005, methodology): “Gluck and Meyers (1993) used the auto-encoder as the basis of their hippocampal model, as a means of generating compressed representations , though it was not intended to be taken literally as a model of hippocampal neural circuitry.”
The Division of Labor Between the Neocortex and Hippocampus (2010, background): “There are two basic mechanisms that have been discussed in the literature, Hebbian and error-driven learning (e.g., Marr, 1971; McNaughton & Morris, 1987; Gluck & Myers, 1993; Schmajuk & Di- Carlo, 1992).”
Considerations arising from a complementary learning systems perspective on hippocampus and neocortex (1996, methodology): “…tractable model thar allowed ro consider the effecrs of these variables in a system containing an~' number of neurons and synapses, overcoming the praCtical limitations of small-scale computer simulations of the SOrt used b~' many other investigators (Gluck and Myers, 1993; Hasselmo and Schnell.”
Compression Artifacts Reduction by a Deep Convolutional Network (2015, background): “Inter-estingly, the concept “easy-hard transfer” has already been pointed out in neuro-computation study [7], where the prior training on an easy discrimination can help learn a second harder one.”
A Bayesian framework for concept learning (1999, background): “The distributionalapproaches include prototype models (e.g. Fried & Holyoak, 1984), exemplar mod-els (e.g. Nosofsky, 1986), and neural networks trained with unsupervised methods(e.g. Gluck & Myers, 1993).”
Retrograde amnesia and memory consolidation: a neurobiological perspective. (1995, background): “Several proposals have been developed about how the medial temporal lobe might interact with neocortex to establish representations in memory [70-75], but most of these do not address directly the notion of consohdation and gradual change.”
Integrating Memories in the Human Brain: Hippocampal–Midbrain Encoding of Overlapping Events (2008, background): “It has been proposed that effective generalization may depend on integrating discrete experiences into a rich, cohesive representation (Eichenbaum, 2000; Gluck and Myers, 1993).”
Theta rhythm of navigation: Link between path integration and landmark navigation, episodic and semantic memory (2005, unknown): “With the introduction of the term ‘‘voluntary,’’ theta oscillation research unintentionally entered the territory of ‘‘intentionality,’’ a label that refers to the ‘‘substance’’ of all subjective mental activity (Dennett, 1987).”