title: Modeling the formation process of grouping stimuli sets through cortical columns and microcircuits to feature neurons.
abstract: 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 of neuron ensembles to condition them to learn and execute a mathematical transform.
authors.affiliation: Division of Bio-Inspired Computing, Fraunhofer IDMT, 98693 Ilmenau, Germany.
authors.forename: Frank
authors.initials: F
authors.lastname: Klefenz
authors.affiliation: Department of Nano-Biosystem Technology, Ilmenau University of Technology, 98693 Ilmenau, Germany.
authors.forename: Adam
authors.initials: A
authors.lastname: Williamson
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology