################################################################### # STA analysis # # # # based on the publication # # # # Martin Pospischil, Zuzanna Piwkowska, Michelle Rudolph, # # Thierry Bal and Alain Destexhe "Calculating Event-Triggered # # Average Synaptic Conductances From the Membrane Potential", # # J Neurophysiol 97:2544-2552, 2007. # # # ################################################################### This application performs a conductance analysis of spike-triggered average voltage traces. A file containing this voltage STA is loaded, and a short adjustable interval before the spike is removed from the trace. If necessary, the voltage STA can be smoothed in order to minimise the effect of recording noise. All parameters are provided by the file 'header.py', the call 'python main.py' from the system prompt starts the analysis. The time courses of the excitatory and inhibitory STAs are then written to the files 'ge_sta.txt' and 'gi_sta.txt', respectively. In order for this to work, the python scripting language along with the packages 'Numeric' and 'pylab' need to be installed. Modifications need only be done to the 'header.py'-file. The adjustable parameters contained therein are as follows: vmFile - file containing the voltage STA in a single column dt - time step (inverse of sampling frequency) in ms t_cut - length of interval before spike in ms that is removed from the analysis n_smooth - SD in timesteps of the Gaussian filter that is used for smoothing Iext - current level in nA ge - mean of exc. conductance distribution in uS gi - mean of inh. conductance distribution in uS se - standard deviation (SD) of exc. conductance distribution in uS si - SD of inh. conductance distribution in uS gl - leak conductance in uS vl - leak reversal potential in mV cap - capacitance in nF te - correlation time constant of excitation in ms ti - correlation time constant of inhibition in ms ve - reversal potential of excitation in mV vi - reversal potential of inhibition in mV