Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons
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 cla...
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2013/290358 |
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doaj-bbf5344809db4c17b7019f054b75fed42020-11-24T22:55:19ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732013-01-01201310.1155/2013/290358290358Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature NeuronsFrank Klefenz0Adam Williamson1Division of Bio-Inspired Computing, Fraunhofer IDMT, 98693 Ilmenau, GermanyDepartment of Nano-Biosystem Technology, Ilmenau University of Technology, 98693 Ilmenau, GermanyA 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.http://dx.doi.org/10.1155/2013/290358 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Frank Klefenz Adam Williamson |
spellingShingle |
Frank Klefenz Adam Williamson Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons Computational Intelligence and Neuroscience |
author_facet |
Frank Klefenz Adam Williamson |
author_sort |
Frank Klefenz |
title |
Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons |
title_short |
Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons |
title_full |
Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons |
title_fullStr |
Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons |
title_full_unstemmed |
Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons |
title_sort |
modeling the formation process of grouping stimuli sets through cortical columns and microcircuits to feature neurons |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2013-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2013/290358 |
work_keys_str_mv |
AT frankklefenz modelingtheformationprocessofgroupingstimulisetsthroughcorticalcolumnsandmicrocircuitstofeatureneurons AT adamwilliamson modelingtheformationprocessofgroupingstimulisetsthroughcorticalcolumnsandmicrocircuitstofeatureneurons |
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