Using probabilistic dependencies improves the search of conductance-based compartmental neuron models

Using probabilistic dependencies improves the search of conductance-based compartmental neuron models” by Roberto Santana, C. Bielza, and P. Larrañaga. In Proceedings of the 8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, (Clara Pizzuti, Marylyn D. Ritchie, and Mario Giacobini, eds.), 2010, pp. 170-181.

Abstract

Conductance-based compartmental neuron models are traditionally used to investigate the electrophysiological properties of neurons. These models require a number of parameters to be adjusted to biological experimental data and this question can be posed as an optimization problem. In this paper we investigate the behavior of different estimation of distribution algorithms (EDAs) for this problem. We focus on studying the influence that the interactions between the neuron model conductances have in the complexity of the optimization problem. We support evidence that the use of these interactions during the optimization process can improve the EDA behavior.

BibTeX entry:

@inproceedings{Santana_et_al:2010c,
   author = {Roberto Santana and C. Bielza and P. Larra{\~n}aga},
   editor = {Clara Pizzuti and Marylyn D. Ritchie and Mario Giacobini},
   title = {Using probabilistic dependencies improves the search of
	conductance-based compartmental neuron models},
   booktitle = {Proceedings of the 8th European Conference on Evolutionary
	Computation, Machine Learning and Data Mining in Bioinformatics},
   series = {Lecture Notes in Artificial Intelligence},
   volume = {6023},
   pages = {170-181},
   publisher = {Springer},
   year = {2010},
   url = {http://dx.doi.org/10.1007/978-3-642-12211-8_15}
}

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