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Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data.

Shaul Druckmann1, Thomas K Berger, Sean Hill

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Model fitting procedures excel with surrogate data but falter with real experimental data. Feature-based distance functions, not whole traces, better approximate experimental neuron model parameters.

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Area of Science:

  • Computational Neuroscience
  • Biophysics

Background:

  • Neuron models require parameter optimization due to experimental limitations.
  • Model-to-model comparisons using surrogate data are common for evaluating fitting procedures.

Purpose of the Study:

  • To investigate the efficacy of model fitting procedures on experimental versus surrogate data.
  • To identify reasons for discrepancies in fitting performance between data types.

Main Methods:

  • Generated surrogate neuron data from a known model.
  • Applied model fitting procedures to both surrogate and experimental data.
  • Evaluated fitting performance using feature-based distance functions versus whole-trace fitting.

Main Results:

  • Fitting procedures performed well on surrogate data but poorly on experimental data.
  • Surrogate data do not adequately test the ability to find best approximations when perfect solutions are absent.
  • Feature-based distance functions successfully identified good approximations for experimental data.

Conclusions:

  • Current model fitting practices may not translate well from surrogate to experimental data.
  • Optimization procedures need to account for the absence of perfect solutions in experimental neuroscience.
  • Feature extraction is a promising approach for fitting neuron models to experimental data.