A Computational Theory for the Learning of Equivalence Relations - Lew, Sergio E.; Zanutto, Silvano B. (Frontiers in Human Neuroscience, 2011)
Equivalence relations are logical entities that emerge concurrently with the development of language capabilities. In this work we propose a computational model that learns to build equivalence relations by learning simple conditional rules. The model includes visual areas, dopaminergic and noradrenergic structures as well as prefrontal and motor areas, each of them modeled as a group of continuous valued units that simulate clusters of real neurons. In the model, lateral interaction between neurons of visual structures and top-down modulation of prefrontal/premotor structures over the activity of neurons in visual structures are necessary conditions for learning the paradigm. In terms of the number of neurons and their interaction, we show that a minimal structural complexity is required for learning equivalence relations among conditioned stimuli. Paradoxically, the emergence of the equivalence relation drives a reduction in the number of neurons needed to maintain those previously specific stimulus-response learned rules, allowing an efficient use of neuronal resources.