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Creating and Constraining Compartmental Models of Neurons Using Experimental Data

  • Stefanos S. Stefanou
  • George Kastellakis
  • Panayiota PoiraziEmail author
Protocol
Part of the Neuromethods book series (NM, volume 113)

Abstract

In order to understand the information processing at the level of individual nerve cells, detailed information is required about the complex interactions between the anatomical structure of the neurons, their physiological properties, and synaptic input. Embodying such information in a formal theoretical model is useful as a predictor as it can simulate the electrical behavior of neurons in ways or conditions that may not be possible experimentally. In order for a model neuron to perform as closely as a real neuron, it needs to be constrained against all the available experimental data. The calibration of the parameters in each compartment requires a careful examination of the relevant literature, experimental evidence, as well as the intuition of the experimenter. However the hand tuning of model parameters can be extremely tricky due to the increased complexity of today’s compartmental neuron models. Consequently the choices of structural model and the level of detail at which these models are constructed are critical considerations and because of our limited knowledge of the system the predictive value of each model is also limited. Therefore, a neuronal model is as good predictor as it was created to be. In this chapter we will address how a realistic conductance-based compartmental model is constructed and what are the common restrictions that prevent a model from reproducing the desired biological dynamics.

Key words

Cable theory Compartmental model Morphology Ion channels Software Active properties Passive properties Synapse Optimisation Neural network 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Stefanos S. Stefanou
    • 1
    • 2
  • George Kastellakis
    • 1
    • 2
  • Panayiota Poirazi
    • 2
    • 3
    Email author
  1. 1.Institute of Molecular Biology and Biotechnology (IMBB)Foundation for Research and Technology, Hellas (FORTH)HeraklionGreece
  2. 2.Department of BiologyUniversity of CreteHeraklionGreece
  3. 3.Computational Biology LabIMBB-FORTHHeraklionGreece

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