Simulating the transverse non-patterning problem

Abstract

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.