Journal
NEUROCOMPUTING
Volume 71, Issue 4-6, Pages 963-972Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2007.02.016
Keywords
virtual neuron; neuronal morphology; computational neuroanatomy
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Virtual neurons are essential in computational neuroscience to study the relation between neuronal form and function. One way of obtaining virtual neurons is by algorithmic generation from scratch. However, a main disadvantage of current available generation methods is that they impose a priori limitations on the outcomes of the algorithms. We present a new tool, EvOL-NEURON, that overcomes this problem by putting a posteriori constraints on generated virtual neurons. We present a proof of principle and show that our method is particularly suited to investigate the neuronal form-function relation. (c) 2007 Elsevier B.V. All rights reserved.
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