Simulating the transverse non-patterning problem

The hippocampus is needed to store memories that are reconfigurable. We have previously shown that a hippocampal-like computational model solves the transverse patterning (TP) problem and transitive inference problems. Here, we show that the same model with the same parameters which solve the TP pro...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2002, Vol.44, p.1029-1034
Main Authors: Wu, Xiangbao, Levy, William B
Format: Article
Language:English
Subjects:
Hippocampus
Recurrent networks
Sequence learning
Transverse non-patterning
Transverse patterning
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Summary:The hippocampus is needed to store memories that are reconfigurable. We have previously shown that a hippocampal-like computational model solves the transverse patterning (TP) problem and transitive inference problems. Here, we show that the same model with the same parameters which solve the TP problem and transitive inference problems reproduces another interesting problem—transverse non-patterning (TNP) problem investigated by Alvarado and Rudy (J. Exp. Psychol.: Anim. Behav. Process 18 (1992) 145). By turning TNP into a problem of sequence learning (stimuli–decision–outcome), a sequence learning, hippocampal-like neural network finds that the TNP problem is unlearnable with the progressive learning paradigm. This unlearnability is what Alvarado and Rudy observed. Thus, both rats and the model learn TP, but fail to learn TNP.
ISSN:0925-2312
1872-8286
DOI:10.1016/S0925-2312(02)00507-6