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• W776855874 endingPage "138" @default.
• W776855874 startingPage "116" @default.
• W776855874 abstract "Unsupervised stimulus-stimulus redundancy compression, one component of Gluck and Myers’s (1993) representational theory of the hippocampal-region function, could emerge from the anatomy and physiology of the entorhinal cortex. This hypothesis is suggested by a physiologically and anatomically realistic model of the entorhinal cortex derived from a similar model of the olfactory cortex previously proposed by Ambros-Ingerson, Granger, and Lynch (1990). To the extent that entorhinal function can survive damage strictly limited to the hippocampal formation (the H lesion), this has implications for interpreting the behavioral consequences of lesions which either do or do not spare overlying cortical areas. In particular, we expect that the H lesion should not interrupt stimulus-stimulus redundancy compression, thereby sparing conditioning behaviors, such as latent inhibition, which are eliminated by broader (H++) lesions to the hippocampal region. However, such other behaviors as the context sensitivity of latent inhibition and of learned responses are expected to be affected by the H lesion. These predictions are consistent with empirical data. The theory also leads to several novel predictions for behavioral comparisons of intact, H-lesioned, and H++-lesioned animals on tasks such as sensory preconditioning, compound preconditioning, and easy-hard transfer. A major theme of this paper is to illustrate how a bottom-up model of cortical processing can be integrated with a top-down model of hippocampal-region function to yield a more complete mapping from physiology to behavior." @default.
• W776855874 created "2016-06-24" @default.
• W776855874 creator A5037690594 @default.
• W776855874 creator A5048541000 @default.
• W776855874 creator A5073735097 @default.
• W776855874 date "1995-06-01" @default.
• W776855874 modified "2023-10-17" @default.
• W776855874 title "Dissociation of hippocampal and entorhinal function in associative learning: A computational approach" @default.
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