A Hippocampal-Like Neural Network Model Solves the Transitive Inference Problem
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.
KeywordsTransitive Inference Synaptic Modification Feedforward Inhibition Configural Learning Noticeable Preference
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