Active Electrode Compensation (AEC) module for RTXI

This module implemets the AEC method from Brette et al. (2008)

• A RTXI module to inject white noise in the system and obtain the data for the calibration is also included
• The calibration is done in python, with a code also included. This calibration creates a kernel file
• Finally, the AEC module can be used with the calculated electrode kernel

How to use it

Data for train

• Install our white noise module:

cd white-noise-module-rtxi && sudo make install

• Insert white noise & read the voltage
• Save the data (recorded voltage & white noise injection)
• Our RTXI workspace is included (white_generator.set)

Calculating the kernel

• Generate the electrode kernel (check line 86 to define the trial of the h5 file):

py aec_train.py -p file.h5 -m intra

• The c++ convolution code used in RTXI can be tested standalone using:

g++ -std=c++1y -O3 -Wall -pedantic -pthread convolution_test.cpp && ./a.out

Using the AEC module

• The calculated kernel is in:

aec_kernel.txt

• Check line 151 to define your kernel path:

aec-module-rtxi/aec-module-rtxi.cpp

• Install our AEC module:

cd aec-module-rtxi && sudo make install

• Open the AEC module in RTXI
• Connect AEC module inputs (recorded voltage & current injection) and output (clean voltage)
• Our RTXI workspace is included (aec_test.set)

Credits

Brette, R., Piwkowska, Z., Monier, C., Rudolph-Lilith, M., Fournier, J., Levy, M., Frégnac, Y., Bal, T., Destexhe, A. (2008). High-resolution intracellular recordings using a real-time computational model of the electrode. Neuron, 59(3), 379-391.