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Modeling the formation process of grouping stimuli sets through cortical columns and microcircuits to feature neurons

: Klefenz, F.; Williamson, A.

Fulltext ()

Computational Intelligence and Neuroscience 2013 (2013), Art.290358, 10 pp.
ISSN: 1687-5265
ISSN: 1687-5273
Journal Article, Electronic Publication
Fraunhofer IDMT ()

A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transform is discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures o f neuron ensembles to condition them to learn and execute a mathematical transform.