A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013)

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Accession:149739
This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations.
Reference:
1 . Li G, Cleland TA (2013) A two-layer biophysical model of cholinergic neuromodulation in olfactory bulb. J Neurosci 33:3037-58 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I Na,p; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I CAN; I Sodium; I Calcium; I Potassium; I_Ks; I Cl, leak; I Ca,p;
Gap Junctions:
Receptor(s): Nicotinic; GabaA; Muscarinic; AMPA; NMDA;
Gene(s):
Transmitter(s): Acetylcholine;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Sensory processing; Sensory coding; Neuromodulation; Olfaction;
Implementer(s): Li, Guoshi [guoshi_li at med.unc.edu];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell; Nicotinic; GabaA; Muscarinic; AMPA; NMDA; I Na,p; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I CAN; I Sodium; I Calcium; I Potassium; I_Ks; I Cl, leak; I Ca,p; Acetylcholine;
% Perform frequency analysis of the sLFP in the OB network
% Written by Guoshi Li, Cornell University, 2013

clc;
clear all;
close all;

load tt;         
load Vm;     % sLFP, mean membrane somatic voltage of all MCs   
% load Vg;
FILORDER = 1000;

DT = 0.2;          % sampling time: ms
Fs = 1/DT*1000;    % sampling frequency: Hz

T1 = 2000;
T2 = 3000;
n1 = T1/DT+1;
n2 = T2/DT;
maxlags = 2000;    % For auto-correlation!  

Fmax = 100;        % maximal frequency to plot
Fc   = [20 100];   % Cut-off frequency
Wc   = Fc/(Fs/2);  % 

t = tt;
t = t(n1:n2);
y = Vm(n1:n2);
y = y-mean(y);


L = length(y);
NFFT = 2^nextpow2(L);     % Next power of 2 from length of y
Y = fft(y,NFFT)/L;
YY = 2*abs(Y(1:NFFT/2));
% YY = abs(Y(1:NFFT/2)).^2;

% Y  = fft(y,NFFT);
% YY = 2*abs(Y)/NFFT;
% YY = YY(1:end/2);

f = Fs/2*linspace(0,1,NFFT/2);

m = Fmax/(0.5*Fs)*(0.5*NFFT);
m = floor(m);

%=================================================
xmin = 1000;
xmax = 2000;

% figure;
% plot(t,y,'b');
% title('Original Signal');
% % axis([xmin, xmax, -80, -20]);

% % Plot single-sided amplitude spectrum.
figure;
plot(f(1:m),YY(1:m));
title('Single-Sided Amplitude Spectrum of sLFP')
xlabel('Frequency (Hz)')
ylabel('|Y(f)|')


%=================================================

h = fir1(FILORDER, Wc);
x = filtfilt(h,1, y);

% figure;
% freqz(h, 1, 512);


%=========================
% [b, a]=butter(3, Wc);
% x = filtfilt(b, a, y);
% x = filter(b, a, y);
% figure;
% freqz(b, a, 512, Fs);

% hd = dfilt.dffir(h);
% x = filter(hd, y);

%=========================

X = fft(x,NFFT)/L;
XX = 2*abs(X(1:NFFT/2));
[Peak, I] = max(XX);
disp('The oscillation frequency is:');
f(I)
disp('The spectrum peak is:');
Peak

%=======================================
%         Auto-correlation 
%=======================================
u  = mean(x);
yn = x-u;

% [cy, lags] = xcorr(y,'unbiased');
[cy, lags] = xcorr(yn, maxlags,'coeff');

% tau = -(L-1):(L-1);
% cc  = xcov(x, 'coef');
% figure;
% plot(tau, cc);
% axis([-2000,2000,-0.4,1]);

for k=(maxlags+2):length(cy)
%     if cy(k)>cy(k-1) && cy(k)>cy(k+1)&& cy(k)>0
      if cy(k)>cy(k-1) && cy(k)>cy(k+1)  
      break;
    end
end

disp('The oscillation index is:');
Power = cy(k)

% disp('The index is:');
% PI=k-maxlags-1

% disp('The oscillation frequency is:');
% fo = 1/(PI*DT)*1000


xmin1 = 2000;
xmax1 = 3000;


figure;
plot(t, x, 'LineWidth',0.5);
xlabel('ms', 'FontSize',14);
ylabel('mV', 'FontSize',14);
title('Filtered LFP', 'FontSize',14);
set(gca, 'FontSize',12);
axis([xmin1, xmax1, -10, 10]);
box('off');

% Plot single-sided amplitude spectrum.
figure;
% plot(f, 2*abs(Y(1:NFFT/2))) 
plot(f(1:m),XX(1:m));
title('FFT Spectrum', 'FontSize',14)
xlabel('Frequency (Hz)', 'FontSize',14)
ylabel('Power', 'FontSize',14)
set(gca, 'FontSize',12);
% axis([0, 150, 0, 2]);
box('off');

% Plot auto-correlation of LFP
figure;
plot(lags, cy);
title('Auto-correlation fo sLFP','FontSize',14);


%=======================================
%     Plot LFP and Auto-Correlation
%=======================================

figure;
subplot(3,1,1);
plot(t-2000, x, 'LineWidth',1);
set(gca, 'FontSize',12);
xlabel('ms', 'FontSize',12,'FontWeight','bold');
ylabel('LFP (mV)', 'FontSize',12,'FontWeight','bold');
axis([0, 1000, -6, 6]);
box('off');

subplot(3,1,2);
plot(lags*DT, cy, 'LineWidth',1);
xlabel('Lags (ms)', 'FontSize',12, 'FontWeight','bold')
set(gca, 'FontSize',12);
axis([-400, 400, -1.0, 1]);
box('off');

subplot(3,1,3);
plot(f(1:m),XX(1:m), 'LineWidth',1);
xlabel('Frequency (Hz)', 'FontSize',12,'FontWeight','bold')
ylabel('Power', 'FontSize',12,'FontWeight','bold')
set(gca, 'FontSize',12);
axis([0, 100, 0, 2.0]);
box('off');