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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.

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