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Review
. 2013:2013:290358.
doi: 10.1155/2013/290358. Epub 2013 Nov 28.

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

Frank Klefenz  1 , Adam Williamson  2
Affiliations
Review

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

Frank Klefenz et al. Comput Intell Neurosci. 2013.

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.

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Figures

Figure 1
Figure 1
A feedforward neural network model with an input layer at the bottom and feature classifier output neurons at the right. The network topology is regular with repetitive elementary building block segment microcircuits. (Reprinted with permission from Brückmann et al., [31].)
Figure 2
Figure 2
The elementary building block segment microcircuit. Path following is self-learned by the settings of the weights at the signal bifurcation. (Reprinted with permission from Brückmann et al., [31].)
Figure 3
Figure 3
Nine training patterns of bars of different slopes. Each time step one image row is consecutively applied to the input layer. (Reprinted with permission from Brückmann et al., [31].)
Figure 4
Figure 4
Micro-CT image of a microtower with integrated electrodes which forms a 2D MEA (a) and which forms a 3D MEA (b) when placed in series with a baseplate compatible with commercial amplifier systems.
Figure 5
Figure 5
Two spatiotemporal stimulation sequences to be provided by the microelectrodes of the 3D MEA.
See this image and copyright information in PMC

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