Extracting non-linear integrate-and-fire models from experimental data using dynamic I-V curves

The dynamic I-V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-d...

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Bibliographic Details
Published in:Biological cybernetics 2008-11, Vol.99 (4-5), p.361-370
Main Authors: Badel, Laurent, Lefort, Sandrine, Berger, Thomas K, Petersen, Carl C. H, Gerstner, Wulfram, Richardson, Magnus J. E
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Animals
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Biophysics
Brain - physiology
Classification
Complex Systems
Computer Appl. in Life Sciences
Construction equipment
Cybernetics
Exponential integrate-and-fire
I-V curve
Mathematical models
Models, Neurological
Neurobiology
Neurons
Neurons - physiology
Neurosciences
Nonlinear dynamics
Nonlinearity
Original Paper
Rats
Refractoriness
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Summary:The dynamic I-V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current-voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models--of the refractory exponential integrate-and-fire type--provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons.
ISSN:0340-1200
1432-0770
DOI:10.1007/s00422-008-0259-4