Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models

被引：0

作者：

Santana, Roberto ^{[1
]}

Bielza, Concha ^{[1
]}

Larranaga, Pedro ^{[1
]}

机构：

[1] Univ Politecn Madrid, Dept Artificial Intelligence, E-28660 Madrid, Spain

来源：

EVOLUTIONARY COMPUTATION, MACHINE LEARNING AND DATA MINING IN BIOINFORMATICS, PROCEEDINGS | 2010年 / 6023卷

关键词：

Conductance-based compartmental neuron models; estimation of distribution algorithm; probabilistic models; CONSTRUCTION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 ate 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.

引用

页码：170 / 181

页数：12

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