Oversampling method to extract excitatory and inhibitory conductances (Bedard et al. 2012)

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" ... We present here a new method that allows extracting estimates of the full time course of excitatory and inhibitory conductances from single-trial Vm recordings. This method is based on oversampling of the Vm . We test the method numerically using models of increasing complexity. Finally, the method is evaluated using controlled conductance injection in cortical neurons in vitro using the dynamic-clamp technique. ..."
1 . Bedard C, Behuret S, Deleuze C, Bal T, Destexhe A (2012) Oversampling method to extract excitatory and inhibitory conductances from single-trial membrane potential recordings Journal of Neuroscience Methods 210(1):3-14 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Parameter Fitting; Methods;
Implementer(s): Bedard, Claude [bedard at unic.cnrs-gif.fr];

This demo program is related to the following publication:

Bedard, C., Behuret, S., Deleuze, C., Bal, T. and Destexhe,
A. Oversampling method to extract excitatory and inhibitory
conductances from single-trial membrane potential recordings. Journal
of Neuroscience Methods, 2011.

This MATLAB demo program performs the extraction of conductances and
preconductances, as explained in the paper. All examples shown here
corresponds to testing of the method using models, as shown in
Figs. 4, 5 and 7 of the paper. The program first reads the
conductances from data files, and uses these conductances to compute
the oversampled Vm from the membrane equation. It then extracts the
"preconductances" from this oversampled Vm, and next removes the
singularities from the preconductances (see paper). Finally, the
preconductances are converted to conductances, which are compared to
the original conductances.

General remarks:

1. the "data" folder contains line vectors for the excitatory
conductance (gge.dat), the inhibitory conductance (ggi.dat) and time
(tt.dat). The parameters for the cell and thresholds to remove
singularities, as well as the initial voltage, are in a line vector of
dimension 8 which name is "Parametres.dat" - everything must be in
ascii in those files.

The localisation of data files (in ascii) is


The demo has several examples, corresponding to some of the figures of
the paper. To get "exemple1", write "exemplex=exemple1" (see
Extraction.RR at lines 44,47,48 and 49)

2. The organization of the program is the following

ExtractionRR.m : main function

In a first step, the main program generates the oversampled voltage
from the conductances located in data/exemplex/... and compares them
in a graph. Note that the program actually computes two Vm: the
oversampled Vm, and a "normal" Vm at the same sampling frequency as
the conductances. The latter is used for comparison and to make sure
that the oversampling factor is enough.

In a second step, the program extracts preconductances from the
oversampled Vm, remove singularities, convert to conductances, which
are then compared to the original conductances.

signalRR.m : function to generate the oversampled Vm from the
conductances using the function eqdifferentielleRR.m

The oversampling factor can be set in line 65 in the main function

algorithmeRR.m : function to extract conductances and preconductances;
the singularities are removed in the last part of this function.

graphRR: function to graph the results.
graph 1 : Compares the initial Vm with the oversampled Vm.
graph 2 : Compares the original preconductances with the ones
graph 3 : Compares the original conductances with the ones extracted.
graph 4 : Compares the extracted preconductances before removing
          singularities, with the ones with all singularities removed.

3. The "data" folder includes five examples. Each example consists of
one file (ANote.txt), who explains the organization of

example1 : conductances generated using a shot-noise stochastic
           process (similar to Fig. 7 of the paper)
example2 : conductances following a periodic function (Fig. 5 of the
example3 : conductances followsing an oscillatory exponential
example4 : conductances generated using the "sinc" function (sin(x)/x)
example5 : constant conductances (Fig. 4 of the paper)

Claude Bedard
Unite de Neurosciences, Information et Complexite (UNIC),
CNRS, 91198 Gif-sur-Yvette, France

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