Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008)

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Neocortical neurons are classified by current–frequency relationship. This is a static description and it may be inadequate to interpret neuronal responses to time-varying stimuli. Theoretical studies (Brunel et al., 2001; Fourcaud-Trocmé et al. 2003; Fourcaud-Trocmé and Brunel 2005; Naundorf et al. 2005) suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. In order to allow a reader to explore such simulations, we prepared a simple NEURON implementation of the experiments performed in Köndgen et al., 2008 (see also Fourcaud-Trocmé al. 2003; Fourcaud-Trocmé and Brunel 2005). In addition, we provide sample MATLAB routines for exploring the sandwich model proposed in Köndgen et al., 2008, employing a simple frequdency-domain filtering. The simulations and the MATLAB routines are based on the linear response properties of layer 5 pyramidal cells estimated by injecting a superposition of a small-amplitude sinusoidal wave and a background noise, as in Köndgen et al., 2008.
1 . Koendgen H, Geisler C, Wang XJ, Fusi S, Luescher HR, Giugliano M (2004) The dynamical response of single cells to noisy time-varying currents Soc Neurosci Abstr :640
2 . Köndgen H, Geisler C, Fusi S, Wang XJ, Lüscher HR, Giugliano M (2008) The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro. Cereb Cortex 18:2086-97 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Axon;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Abstract Wang-Buzsaki neuron;
Channel(s): I Na,t; I K;
Gap Junctions:
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Parameter Fitting; Methods; Rate-coding model neurons;
Implementer(s): Giugliano, Michele [mgiugliano at]; Delattre, Vincent;
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,t; I K;
// Single-compartmental model cell, quick and dirty implementation
// In the context of the stimulation of a single cell by a sinusoidal noisy current, this script
// performs the frequency domain analysis.
// It generates a matrix on a file, containing the gain, the phase and the frequency of stimulation.
// You can choose to do many trials for a statistical analysis.
// This script is mean to be run under Linux and is parallelized. 
// by M. Giugliano and V. Delattre(2008)
// Brain Mind Institute, EPFL, 

load_file("nrngui.hoc")                   // Loading of the standard GUI controls...
load_file("mylibs/graphs.hoc")            // Loading some ad-hoc proc for displaying live traces...
load_file("mylibs/singlecompneuron.hoc")  // Loading the model cell template..
load_file("mylibs/IsinunoisyS_proc.hoc")  // Loading the procedure for a "sinunoisy" current injection...
load_file("mylibs/myinit.hoc")            // Loading some ad-hoc proc for file creation and initialization...
load_file("mylibs/TFparams.hoc")          // Settings for the TF...
load_file("mylibs/Functions.hoc")         // Loading some had-hoc function: mysin, err...
load_file("mylibs/FDA_proc.hoc")          // Loading the procedure for the frequency domain analysis...
load_file("mylibs/SetSeed_proc.hoc")      // Loading the Seeding procedure



////////////////////////SIMULATION CONTROL////////////////////////////////////////////////////////////////

Nfreq   = 51                      // Number of frequency to be tested
Ntrials = 15                      // Number of repetitions
Ntot    = Ntrials * Nfreq         // Number of tasks

Vrest              = -67          // [mV]
Amp                = 0.2          // [pA]Sinusoidal input amplitude
tstart             = 0            // [ms] 
tau                = 2.5          // [ms] 
freqmax            = 1.16^Nfreq   // [Hz]Sinusoidal input maximum frequency
freqmin            = 1.16         // [Hz]Sinusoidal input minimum frequency 

tstop              = Tdelay + T1 + T    //Total simulation duration [ms]
t                  = tstart

vth_wb(0.5)     = 0.


//////////////////////////OBJECTS DECLARATION/////////////////////////////////////////////////////////////

objref outspikes, indepvar, myhist, fitsine, vec

outspikes = new Vector()

objref result_file1, result_file2
strdef aptime, linear

result_file1 = new File("result/Sresult_s=0.55_m=-0.25.x")

/////////////////////////// Experimentation //////////////////////////////////////////////////////////////

my_k  = 0
my_i  = 0
freq  = freqmin
mu    = -0.25 
sigma = 0.55 

ccc = 1 // just a counter

for(my_k=0; my_k<= 1; my_k = my_k +1){          // This loops sweeps accross sigmas
	for(my_i=0; my_i<= Ntot-1; my_i = my_i+ 1){ // This loops sweeps accross the frequencies
			SetSeed()                           // We want every trace to differ from the others
			remainder = my_i % Nfreq            // We want Ntrials repetitions of each frequency
			freq = freqmin ^ (remainder + 1)         
			print ccc ,"of ", 2 * Ntot, "simulations completed "
			gain = 0
			phase = 0       
			result_file1.aopen()                 // We append the file
			result_file1.printf("%d %d %f %f %f %f\n",my_k,my_i,freq,gain,phase,R0)

            ccc = ccc + 1
	sigma = sigma + 0.3

printf("The data points of the frequency domain analysis are now available for further analysis and plot!\n")