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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Santana, R., Bielza, C., & Larrañaga, P. (2010). Using probabilistic dependencies improves the search of conductance-based compartmental neuron models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6023 LNCS, pp. 170–181). Springer Verlag. https://doi.org/10.1007/978-3-642-12211-8_15
Mendeley helps you to discover research relevant for your work.