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-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.
Badel
Laurent
L
Laboratory of Computational Neuroscience, School of Computer and Communications Sciences and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland. laurent.badel@epfl.ch
Lefort
Sandrine
S
Berger
Thomas K
TK
Petersen
Carl C H
CC
Gerstner
Wulfram
W
Richardson
Magnus J E
MJ
D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001921:Brain / Q000502:physiology; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D051381:Rats