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

  • Xiangbao Wu
  • William B Levy


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.


Transitive Inference Synaptic Modification Feedforward Inhibition Configural Learning Noticeable Preference 
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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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