Python demo of the VmT method to extract conductances from single Vm traces (Pospischil et al. 2009)

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Accession:116870
This python code implements a method to estimate synaptic conductances from single membrane potential traces (the "VmT method"), as described in Pospischil et al. (2009). The method uses a maximum likelihood procedure and was successfully tested using models and dynamic-clamp experiments in vitro (see paper for details).
Reference:
1 . Pospischil M, Piwkowska Z, Bal T, Destexhe A (2009) Extracting synaptic conductances from single membrane potential traces. Neuroscience 158:545-52 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Python;
Model Concept(s): Methods;
Implementer(s): Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]; Pospischil, Martin ;
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demo_VmT
readme.txt
header.py
methods.py
simplex.py
vm_trace.txt
VmT.py
                            
# target values of the file 'vm_trace.txt'
# ge=0.032057
# gi=0.096171
# se=0.008014
# si=0.024043


vmFile = 'vm_trace.txt'         # input file
resfile = 'g_dist_parms.txt'    # output file

Iext = 0.                   # constant injected current in nA
gtot = 0.146928             # total input conductance in uS
C = 0.33                    # capacitance in nF
gl = 0.0187                 # leak conductance in uS
Vl = -99.                   # leak reversal potential in mV
Ve = 0.                     # rev. potential of exc. in mV
Vi = -75.                   # rev. potential of inh. in mV
te = 2.728                  # exc. corr. time constant in ms 
ti = 10.49                  # inh. corr. time constant in ms 


vt=-20.                     # threshold for spike detection
dt=0.048                    # time step in ms
t_pre = 5.                  # excluded time preceding spike
t_post = 10.                # excluded time after spike

n_smooth = 3
n_ival = 100                # nb of intervals analysed

n_minISI = 1000             # min nb of datapoints in interval
n_maxISI = 10000            # max nb of datapoints in interval

g_start = [0.03, 0.001, 0.001]

pre=int(t_pre/dt)           # excluded pre spike steps
ahp=int(t_post/dt)          # excluded post spike steps

he1 = 1.-dt/te
he2 = dt/te
hi1 = 1.-dt/ti
hi2 = dt/ti