Abstract
Virtual neurons are digitized representations of biological neurons, with an emphasis on their morphology. In previous research we presented a proof of principle of reconstructing virtual neuronal morphologies by means of Genetic L-Systems Programming (GLP) [13]. However, the results were limited due to a hard evolutionary search process and a minimalistic fitness function. In this work we analyzed the search process and optimized the GLP configuration to enhance the search process. In addition, we designed a neuron type-specific fitness function which provides an incremental assessment of the evolved structures. The results are significantly better and relevant issues are discussed.
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Torben-Nielsen, B. (2007). Evolving Virtual Neuronal Morphologies: A Case Study in Genetic L-Systems Programming. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds) Advances in Artificial Life. ECAL 2007. Lecture Notes in Computer Science(), vol 4648. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74913-4_109
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DOI: https://doi.org/10.1007/978-3-540-74913-4_109
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