Automated high throughput single neuron characterization

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

Download the Python code from the ./src directory as well as the current-clamp recordings (acquired by O. Hagens) from an L5 Pyr neuron of the mouse somatosensory cortex. The data are stored in the folder ./data/gif_test have been collected according to the following experimental protocol (further details on the experimental methods can be found in the original manuscript).

The folder ./src/examples 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/gif_test. 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 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.

For questions or remarks, write to: [email protected].