Improving fuzzy-based axon segmentation with genetic algorithms: The IEEE Congress on Evolutionary Computation

In the course of neurobiological studies the following discovery has been made: Extracted rat nerve cells which show no physical connections start combining and connecting each other to functional, active networks without any further influence. During this process the interconnection of neighboring as well as more distant nerve cells to smaller or global networks is guaranteed by axonal growth. Furthermore, during the process of connecting and synchronizing of networks, the inactive synapses became active once, the cell function may be transfigured from a catalyzing to a blocking one, that allows the conclusion of axonal growth as being an important modifier and influence in the process. Considering the discoveries, the axonal growth needs to be followed and analyzed in order to draw more scientific and detailed conclusions about the self-organizational potentials of nerve cells, the focusing on blocking and catalyzing aspects and their importance for the development of independent networks. A software is needed which enables the scientists to evaluate the nerve cell connections and applicate a statistical analysis of axonal growth. The results of this analysis may be used to create a model which simulates the self-organizational abilities of biological networks. [11] proposes a usage of those models as templates for artificial neuronal networks displaying the biological aspects more detailed than the currently available models. In this work we present a axon segmentation algorithm, based on a fuzzy-controlled system. The problems that appear, is that a correct setting of the rule-set can hardly be known, so we prove to optimize the rule-set with evolutionary algorithms.