Transitivity, flexibility, conjunctive representations, and the hippocampus. II. A computational analysis.
A computational neural network model is presented that explains how the hippocampus can contribute to transitive inference performance observed in rats (Dusek and Eichenbaum, 1997. Proc Natl Acad Sci U S A 94:7109-7114; Van Elzakker et al., 2003. Hippocampus 12:this issue). In contrast to existing theories that emphasize the idea that the hippocampus contributes by flexibly relating previously encoded memories, we find that the hippocampus contributes by altering the elemental associative weights of individual stimulus elements during learning. We use this model to account for a range of existing data and to make a number of distinctive predictions that clearly contrast these two views.
Department of Psychology, University of Colorado, Boulder, Colorado 80309, USA.
Michael J
MJ
Frank
Jerry W
JW
Rudy
Randall C
RC
O'Reilly
D000818:Animals; D001522:Behavior, Animal / Q000502:physiology; D003071:Cognition / Q000502:physiology*; D003463:Cues; D018891:Dentate Gyrus / Q000293:injuries / Q000473:pathology / Q000503:physiopathology; D006624:Hippocampus / Q000502:physiology*; D007858:Learning / Q000502:physiology*; D007859:Learning Disabilities / Q000473:pathology / Q000503:physiopathology; D008959:Models, Neurological; D016571:Neural Networks, Computer; D051381:Rats