Skip to main content
Springer Nature Link
Log in

Neuronal Model Output Fitness Function

  • Living reference work entry
  • First Online:
Encyclopedia of Computational Neuroscience

  • 305 Accesses

Synonyms

Objective, distance, error, cost, or match functions in neuronal model optimization

Definition

A fitness function is a procedure, typically analytical, which quantifies the degree to which a data set or solution meets a given goal. In neuronal modeling, fitness functions typically compare the result of a computer simulation against biological activity for the purpose of guiding parameter optimization.

Detailed Description

Background

Computational models are widely used in neuroscience. These models vary greatly in the level of biological verisimilitude. At one extreme are detailed conductance-based compartment models that model neuron conductances with physiologically derived differential equations, appropriately distributed on accurately reproduced neuron anatomies (Skinner 2006). At the other extreme are highly abstract models with greatly simplified individual neurons and synapses, e.g., integrate and fire neurons with simple on/off synapses. All such models have free...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Achard P, De Schutter E (2006) Complex parameter landscape for a complex neuron model. PLoS Comput Biol 2:e94. doi: 06-PLCB-RA-0109R2 [pii]; 10.1371/journal.pcbi.0020094

    Google Scholar 

  • Ambros-Ingerson J, Grover LM, Holmes WR (2008) A classification method to distinguish cell-specific responses elicited by current pulses in hippocampal CA1 pyramidal cells. Neural Comput 20:1512–1536. doi:10.1162/neco.2007.07-07-564

    Article  PubMed Central  PubMed  Google Scholar 

  • Bhalla US, Bower JM (1993) Exploring parameter space in detailed single neuron models: simulations of the mitral and granule cells of the olfactory bulb. J Neurophysiol 69:1948–1965

    CAS  PubMed  Google Scholar 

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Hoboken

    Google Scholar 

  • Dorigo M, Di Caro G, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5:137–172

    Article  CAS  PubMed  Google Scholar 

  • Druckmann S, Banitt Y, Gideon A, Schurmann F, Markram H, Segev I (2007) A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci 1:7–18

    Article  PubMed Central  PubMed  Google Scholar 

  • Ellner SP, Guckenheimer JC (2006) Dynamic models in biology. Princeton University Press, Princeton

    Google Scholar 

  • Gray RM (1990) Entropy and information theory. Springer, New York

    Book  Google Scholar 

  • Hanrahan G (2011) Swarm intelligence metaheuristics for enhanced data analysis and optimization. Analyst 136:3587–3594. doi:10.1039/c1an15369b

    Article  CAS  PubMed  Google Scholar 

  • Houghton C, Victor JD (2012) Measuring representational distances – the spike-train metrics approach. In: Kriegeskorte N, Kreima G (eds) Visual population codes: towards a common multivariate framework for cell recording and functional imaging. MIT Press, Boston, pp 213–244

    Google Scholar 

  • Keren N, Peled N, Korngreen A (2005) Constraining compartmental models using multiple voltage recordings and genetic algorithms. J Neurophysiol 94:3730–3742. doi:00408.2005 [pii]; 10.1152/jn.00408.2005

    Google Scholar 

  • Koutsou A, Kanev J, Christodoulou C (2013) Measuring input synchrony in the Ornstein-Uhlenbeck neuronal model through input parameter estimation. Brain Res 1536:97–106

    CAS  PubMed  Google Scholar 

  • Kreuz T (2011) Measures of spike train synchrony. Scholarpedia 6:11934

    Article  Google Scholar 

  • Kreuz T (2013) Measures of neuronal signal synchrony. Scholarpedia 6:11922

    Article  Google Scholar 

  • Kreuz T, Haas JS, Morelli A, Abarbanel HD, Politi A (2007) Measuring spike train synchrony. J Neurosci Methods 165:151–161. doi:S0165-0270(07)00267-1 [pii]; 10.1016/j.jneumeth.2007.05.031

    Google Scholar 

  • LeMasson G, Maex R (2001) Introduction to equation solving and parameter fitting. In: De Schutter E (ed) Computational neuroscience: realistic modeling for experimentalists. CRC Press, London, pp 1–24

    Google Scholar 

  • Moles CG, Mendes P, Banga JR (2003) Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 13:2467–2474

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Morris C, Lecar H (1981) Voltage oscillations in the barnacle giant muscle fiber. Biophys J 35:193–213. doi: S0006-3495(81)84782-0 [pii]; 10.1016/S0006-3495(81)84782-0

    Google Scholar 

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

    Article  Google Scholar 

  • Nowak L, Sanchez-Vives MV, McCorkick DA (1997) Influence of low and high frequency inputs on spike timing in visual cortical neurons. Cereb Cortex 7:487–501

    Article  CAS  PubMed  Google Scholar 

  • Osman IH, Laporte G (1996) Metaheuristics: a bibliography. Ann Oper Res 63:513–623

    Article  Google Scholar 

  • Prinz AA (2007) Neuronal parameter optimization. Scholarpedia 2:1903

    Article  Google Scholar 

  • Prinz AA, Bucher D, Marder E (2004) Similar network activity from disparate circuit parameters. Nat Neurosci 7:1345–1352. doi:nn1352 [pii]; 10.1038/nn1352

    Google Scholar 

  • Saha SK, Ghoshal SP, Kar R, Mandal D (2013) Cat swarm optimization algorithm for optimal linear phase FIR filter design. ISA Trans http://dx.doi.org/10.1016/j.isatra.2013.07.009i. doi:S0019-0578(13)00105-5 [pii]; 10.1016/j.isatra.2013.07.009

  • Schreiber S, Fellous JM, Whitmer D, Tiesinga P, Sejnowski TJ (2003) A new correlation-based measure of spike timing reliability. Neurocomputing 52–54:925–931. doi:10.1016/S0925-2312(02)00838-X

    Article  PubMed Central  PubMed  Google Scholar 

  • Schulz DJ, Goaillard JM, Marder E (2006) Variable channel expression in identified single and electrically coupled neurons in different animals. Nat Neurosci 9:356–362. doi:nn1639 [pii]; 10.1038/nn1639

    Google Scholar 

  • Skinner FK (2006) Conductance-based models. Scholarpedia 1:1408

    Article  Google Scholar 

  • Tateno T, Pakdaman K (2004) Random dynamics of the Morris-Lecar neural model. Chaos 14:511–530

    Article  PubMed  Google Scholar 

  • Tuckwell HC, Wan FY, Rospars JP (2002) A spatial stochastic neuron model with Ornstein-Uhlenbeck input current. Biol Cybern 86:137–145

    Article  PubMed  Google Scholar 

  • Van Geit W, De Schutter E, Achard P (2008) Automated neuron model optimization techniques: a review. Biol Cybern 99:241–251. doi:10.1007/s00422-008-0257-6

    Article  PubMed  Google Scholar 

  • van Rossum MC (2001) A novel spike distance. Neural Comput 13:751–763

    Article  PubMed  Google Scholar 

  • Victor JD (2005) Spike train metrics. Curr Opin Neurobiol 15:585–592. doi:S0959-4388(05)00123-6 [pii]; 10.1016/j.conb.2005.08.002

    Google Scholar 

  • Victor JD, Purpura KP (1996) Nature and precision of temporal coding in visual cortex: a metric-space analysis. J Neurophysiol 76:1310–1326

    CAS  PubMed  Google Scholar 

  • Victor JD, Purpura KP (2010) Spike metrics. In: Grun S, Rotter S (eds) Analysis of parallel spike trains. Springer, New York, pp 129–156

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biological Sciences, Ohio University, Athens, OH, USA

    William White & Scott Hooper

Authors
  1. William White
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Scott Hooper
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William White .

Editor information

Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

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

    Ranu Jung

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

White, W., Hooper, S. (2014). Neuronal Model Output Fitness Function. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_160-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_160-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7320-6

  • eBook Packages: Living Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences

Publish with us

Policies and ethics