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Resultados de la simulación de una red

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Resultados de la simulación de una red. // COMPILACION : gcc dynamics.c -o xdynamics -lm -Wall -g // EJECUCION : ./ xdynamics. La dinámica. Dinámica del potencial. Modelo COBA ( Conductance BAsed ). Modelo CUBA ( Current BAsed ).

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Presentation Transcript
slide2

// COMPILACION:

gccdynamics.c-o xdynamics-lm -Wall -g

// EJECUCION:

./xdynamics

slide3

La dinámica

Dinámica del potencial

Modelo COBA (ConductanceBAsed)

Modelo CUBA (CurrentBAsed)

Dinámica de las conductancias (corrientes)

slide4

Explorando algunas propiedades de la red

TP Vogels, LF Abbott - Journal of Neuroscience 25: 10786, 2005

slide6

Traza del potencial de membrana de una neurona

Vth = -50 mVVrest=Vreset= -60 mV

slide10

Distribución del CV en la población

La irregularidad de los disparos es grande debido al balance

slide11

Notar que el rate de la población está en un régimen estacionario, presentando fluctuaciones alrededor de una media

En la simulación no se han realizado “trials”, aquí el promedio se efectúa sobre las neuronas de la población

slide13

Cálculo del CV

Durante la simulación:

Dentro del if:

if (V[i]>VTh){

Evaluamos:

ISI = tRunning - t_previous_spike[i];

// "ISI": ms. "t_previous_spike": el tiempo del spike previo de la neurona i

NetISI += ISI; // Para evaluar la media, en toda la red, de las cantidades ISI

FirstMoment_ISI[i] += ISI;

SecondMoment_ISI[i] += ISI*ISI; //To compute theISI\'s CV

slide14

Al final de la simulación:

//---------- CV

Num_bins_CV_histogram = 30;

CVHistogram = calloc(Num_bins_CV_histogram, sizeof(double));

for (i_bin=0; i<Num_bins_CV_histogram; i_bin++) CVHistogram[i_bin] = 0.0;

Max = 3.;

bin_CV = Max/((double) Num_bins_CV_histogram);

meanCV = 0.;

failure = 0.; //to count the number of cells that did not produce spikes

//those are simply not included in the statistics

slide15

for (i=0; i<cells; i++){

if(spike_counter[i] > 1){

FirstMoment_ISI[i] /= spike_counter[i];

SecondMoment_ISI[i] /= spike_counter[i];

SD_ISI = pow( (SecondMoment_ISI[i] - FirstMoment_ISI[i]*FirstMoment_ISI[i]), 0.5);

CV = SD_ISI/FirstMoment_ISI[i];

meanCV += CV;

iHist = (int) (CV/bin_CV);

if (iHist >= Num_bins_CV_histogram) iHist= Num_bins_CV_histogram - 1;

CVHistogram[iHist] += 1.0;

}else{ failure++; }

}

slide16

//---------- CV (normalizacion)

norm = 0.0;

for (i_bin=0; i_bin<Num_bins_CV_histogram; i_bin++) norm+= CVHistogram[i_bin];

for (i_bin=0; i_bin<Num_bins_CV_histogram; i_bin++) {

CVHistogram[i_bin] /= norm;

fprintf(CV_histogram_File, "%lf %lf \n", i_bin*bin_CV, CVHistogram[i_bin] ) ;

}

meanCV /= (double) (cells-failure);

fprintf(Output_File, "\n Population mean of the CV = %lf \n", meanCV) ;

slide17

Similarmente para V, al final de la simulación:

//---------- V (solo normalizacion)

norm = 0.0;

for (i_bin=0; i_bin<Num_bins_V_histogram; i_bin++) norm += VHistogram[i_bin];

for (i_bin=0; i_bin<Num_bins_V_histogram; i_bin++) {

VHistogram[i_bin] /= norm;

fprintf(V_histogram_File, "%lf %lf \n", Min_V + i_bin*bin_V, VHistogram[i_bin] );

//"bin_V" isalreadygiven in physicalunits (mV)

}

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