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Neuronal Model Output Fitness Function

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Encyclopedia of Computational Neuroscience

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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...

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Author information

Authors and Affiliations

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

    William White & Scott Hooper

Authors
  1. William White
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  2. Scott Hooper
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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

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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

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

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7320-6

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