The content of this web page is associated with the following publications:
C. Pozzorini, S. Mensi, O. Hagens, R. Naud, C. Koch and W. Gerstner, Automated high-throughput characterization of single neurons by means of simplified spiking neuron models,PLOS Computational Biology 2015
S. Mensi, O. Hagens, W. Gerstner and C. Pozzorini, Enhanced sensitivity to rapid input fluctuations by nonlinear threshold dynamics in neocortical pyramidal neurons, PLOS Computational Biology 2016
The first 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.
In the second 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 use the code and fit GIF models to data can be found on the wiki. Some examples are provided in src/examples.