GIF Fitting Toolbox

The content of this web page is associated with the publication:

 

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

* equal contribution

 

More tools to fit neuron models to data are avialable on github: https://github.com/pozzorin/GIFFittingToolbox

 

Click here to download the Python code as well as some current-clamp recordings acquired by O. Hagens from an L5 Pyr neuron of the mouse somatosensory cortex. The data have been collected according to the following experimental protocol (further details on the experimental methods can be found in the original manuscript).

 

 

The folder GIF_Toolbox contains two folders:

• Data
• Code

 

The folder Code contains several .py files. The file Main_TestGIF.py explains how the code can be used to fit a GIF model to the experimental data in Data. To run the code, open a terminal and type:

 

python Main_TestGIF.py

 

During the first execution, some files will be compiled (ignore the warnings).

 

To write a new script, follow this simple 4-step procedure:

 

• Step 1: Load the experimental data

• Step 2: Perform Active Electrode Compensation (data preprocessing)

• Step 3: Fit a GIF model to the data

• Step 4: Evaluate the GIF model performance

 

The mathematical details of the computational methods implemented in the code are provided in the original manuscript (Pozzorini et al. 2015). For questions or remarks, you can write an email to: christian.pozzorini@epfl.ch.