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Hybrid Parameter Optimization Methods

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Encyclopedia of Computational Neuroscience

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References

  • Achard P, De Schutter E (2006) Complex parameter landscape for a complex neuron model. PLoS Comput Biol 2:e94

    Article  PubMed Central  PubMed  Google Scholar 

  • Achard P, Van Geit W, LeMasson G (2010) Parameter Searching. In: De Schutter E (ed) Computational modeling methods for neuroscientists. MIT Press, Cambridge, pp 31–60

    Google Scholar 

  • Druckmann S, Banitt Y, Gidon A, Schürmann F, Markram H, Segev I (2007) A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci 1(1):7

    Article  PubMed Central  PubMed  Google Scholar 

  • Keren N, Bar-Yehuda D, Korngreen A (2009) Experimentally guided modelling of dendritic excitability in rat neocortical pyramidal neurones. J Physiol 587(7):1413–1437

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Maeda Y, Li Q (2007) Fuzzy adaptive search method for parallel genetic algorithm tuned by evolution degree based on diversity measure. In: Castillo et al (eds) Foundations of fuzzy logic and soft computing. Springer, Berlin/Heidelberg, pp 677–687

    Chapter  Google Scholar 

  • Mitra A, Manitius A, Sauer T (2013) A new technique to optimize single neuron models using experimental spike train data. In: American control conference (ACC), IEEE, Washington, DC, pp 346–351

    Google Scholar 

  • Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313

    Article  Google Scholar 

  • Prinz AA, Billimoria CP, Marder E (2003) Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol 90:3998–4015

    Article  PubMed  Google Scholar 

  • Roth A, Bahl A (2009) Divide et impera: optimizing compartmental models of neurons step by step. J Physiol 587(7):1369–1370

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Vossou CG, Koukoulis IN, Provatidis CG (2007) Genetic combined with a simplex algorithm as an efficient method for the detection of a depressed ellipsoidal flaw using the boundary element method. Int J Appl Math Comput Sci 4(2):88–93

    Google Scholar 

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Authors and Affiliations

  1. Blue Brain Project, EPFL, Lausanne, Switzerland

    Werner Van Geit

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  1. Werner Van Geit
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Correspondence to Werner Van Geit .

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Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, Florida, USA

    Ranu Jung

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Van Geit, W. (2014). Hybrid Parameter Optimization Methods. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_164-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_164-1

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7320-6

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