期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 106, 期 39, 页码 16829-16834出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.0903766106
关键词
-
资金
- National Institutes of Health [NS-046064]
Realistic computational models of single neurons require component ion channels that reproduce experimental findings. Here, a topology-mutating genetic algorithm that searches for the best state diagram and transition-rate parameters to model macroscopic ion-channel behavior is described. Important features of the algorithm include a topology-altering strategy, automatic satisfaction of equilibrium constraints (microscopic reversibility), and multiple-protocol fitting using sequential goal programming rather than explicit weighting. Application of this genetic algorithm to design a sodium-channel model exhibiting both fast and prolonged inactivation yields a six-state model that produces realistic activity-dependent attenuation of action-potential backpropagation in current-clamp simulations of a CA1 pyramidal neuron.
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