- Poster presentation
- Open Access
Conductance estimation of a conductance-based neuron model by the differential evolution algorithm
© Suzuka et al; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Differential Evolution
- Model Neuron
- Differential Evolution Algorithm
- Spike Frequency
- Frequency Adaptation
In the studies by a biologically detailed computational model of a neural circuit, it is an important step to construct the conductance-based model of a single neuron that reproduces electrophysiological data. Fitting model parameters such as the maximal conductances of ionic currents can be formulated as an optimization problem in which we should minimize the error between the data experimentally recorded and simulated by the model neuron. We here applied the differential evolution (DE) algorithm [1, 2], one of evolutionary computation algorithms, to the parameter fitting problem based on the objective function defined by characteristics of an action potential profile.
We applied the DE algorithm to the conductance fitting to the synthetic data generated by a conductance-based model neuron. The model neuron consisted of spike-generating sodium, delayed rectifier potassium, A-type potassium and muscarinic potassium channels, conductance of which should be estimated. The objective function to be minimized was defined as a sum of six scores (distances to the target values) on action potential characteristics: firing rate, action potential peak, action potential width, depth of afterhyperpolarization, a latency to the first spike and degree of spike frequency adaptation . We then compared its performance with that of the real-coded genetic algorithm (RCGA) as a benchmark under the same condition of population size and the number of generations.
- Storn R, Price K: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim. 1997, 11: 341-359. 10.1023/A:1008202821328.View ArticleGoogle Scholar
- Buhry L, Grassia F, Giremus A, Grivel E, Renaud S, Saïghi S: Automated parameter estimation of the Hodgkin-Huxley model using the differential evolution algorithm: application to neuromimetic analog integrated circuites. Neural Comput. 2011, 23: 2599-2625. 10.1162/NECO_a_00170.View ArticlePubMedGoogle Scholar
- Druckmann S, Banitt Y, Gidon A, Schürmann F, Markram H, Idan Segev: A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci. 2007, 1: 7-18. 10.3389/neuro.01.1.1.001.2007.PubMed CentralView ArticlePubMedGoogle Scholar
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