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A Hippocampal-Like Neural Network Model Solves the Transitive Inference Problem

  • Xiangbao Wu
  • William B Levy

Abstract

Both rats and humans can solve configural learning problems. Based on lesion experiments in rats, configural learning is regarded as a hippocampally dependent function1 when reconfigurability is critical. We have previously shown that a hippocampal-like neural network model2,3 solves the configural problem of transitive inference (TI)4. Here we confirm this result and investigate the robustness of this demonstration as a function of network activity levels.

Keywords

Transitive Inference Synaptic Modification Feedforward Inhibition Configural Learning Noticeable Preference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Xiangbao Wu
    • 1
  • William B Levy
    • 1
  1. 1.Department of Neurological SurgeryUniversity of Virginia Health Sciences CenterCharlottesvilleUSA

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