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Related Experiment Videos

Parameter estimation for bursting neural models.

Joseph H Tien1, John Guckenheimer

  • 1Center for Applied Mathematics, Cornell University, Ithaca, NY 14853, USA. joetien@gmail.com

Journal of Computational Neuroscience
|November 14, 2007
PubMed
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This study introduces novel parameter estimation methods for bursting neural models using geometrical features and automatic differentiation. These methods accurately fit neural models to empirical data, revealing insights into neuromodulatory mechanisms.

Area of Science:

  • Computational Neuroscience
  • Mathematical Biology
  • Systems Neuroscience

Background:

  • Bursting neural models are crucial for understanding neuronal dynamics.
  • Accurate parameter estimation is essential for validating these models against experimental data.
  • Existing methods may not fully capture the complex dynamics of bursting phenomena.

Purpose of the Study:

  • To develop and apply novel parameter estimation methods for bursting neural models.
  • To leverage geometrical features of bursting and automatic differentiation for enhanced accuracy.
  • To investigate the modulatory effects of norepinephrine on preBötzinger complex neurons.

Main Methods:

  • Utilizing geometrical features of bursting, including periodic orbits and bifurcations.

Related Experiment Videos

  • Introducing defining equations for burst initiation and termination.
  • Employing automatic differentiation to compute parameter sensitivities for burst timing and period.
  • Applying gradient-based optimization algorithms to fit model parameters to empirical data.

    • Main Results:

    • Successfully fitted the preBötzinger complex neuron model to empirical data under control and norepinephrine-modulated conditions.
    • Demonstrated the utility of geometrical insights and automatic differentiation in parameter estimation.
    • Identified potential modulatory mechanisms, such as the persistent sodium current, in the preBötzinger complex.

    Conclusions:

    • The developed parameter estimation framework provides an accurate and efficient approach for bursting neural models.
    • The findings offer insights into the neuromodulation of neuronal bursting, specifically in the preBötzinger complex.
    • This work advances the integration of computational modeling and experimental neuroscience for understanding neural function.