The content of this web page is associated with the following publications:
Automated high-throughput characterization of single neurons by means of simplified spiking neuron models
C. Pozzorini*, S. Mensi*, O. Hagens, R. Naud, C. Koch and W. Gerstner
PLOS Computational Biology 2015
This paper introduces an experimental protocol and a set of computational tools to characterize the electrophysiological properties of neurons by fitting a Generalized Integrate-and-Fire (GIF) model to data.
Instructions on how to use the code and fit the GIF model to data can be found here.
Enhanced sensitivity to rapid input fluctuations by nonlinear threshold dynamics in neocortical pyramidal neurons
S. Mensi, O. Hagens, W. Gerstner and C. Pozzorini
PLOS Computational Biology 2016
In this paper, the GIF model and the fitting procedure described in Pozzorini et al. 2015 are extended. A new model, called inactivating Generalized Integrate-and-Fire (iGIF), is introduced that captures the spiking activity of single neurons over an broad range of input statistics.
Instructions on how to fit iGIF models to data can be found here.
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