Inverse stochastic resonance of cerebellar Purkinje cell (Buchin et al. 2016)

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Accession:206364
This code shows the simulations of the adaptive exponential integrate-and-fire model (http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model) at different stimulus conditions. The parameters of the model were tuned to the Purkinje cell of cerebellum to reproduce the inhibiion of these cells by noisy current injections. Similar experimental protocols were also applied to the detailed biophysical model of Purkinje cells, de Shutter & Bower (1994) model. The repository also includes the XPPaut version of the model with the corresponding bifurcation analysis.
Reference:
1 . Buchin A, Rieubland S, Häusser M, Gutkin BS, Roth A (2016) Inverse Stochastic Resonance in Cerebellar Purkinje Cells. PLoS Comput Biol 12:e1005000 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum Purkinje GABA cell; Abstract integrate-and-fire leaky neuron; Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Channel(s):
Gap Junctions:
Receptor(s): Gaba; Glutamate;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: MATLAB; NEURON; XPP;
Model Concept(s): Information transfer; Activity Patterns; Synaptic noise; Oscillations;
Implementer(s): Roth, Arnd ; Buchin, Anatoly [anat.buchin at gmail.com];
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell; Glutamate; Gaba; Gaba; Glutamate;
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BuchinEtAl2016ISR
.git
deShutter-Bower
XPP
README.md
aeif.m
dendrite.m
dendritenodendrite.m
FI.m
hist_v.m
hist2.m
K.m *
matlabserver.m
newaEIF_parameters.xls
nodendrite.m
par.txt
parsave.m
psth.m
psthhold.m
psthprob.m
psthprobnew.m
psthsigma.m
ramp.m
ramp_test.m
ramp2 (rough method).m
ramp2.m
sevencells.m
sigma_opt.m
SNRprocessing.m
test_orn.m
test_orn_par.m
test_prob.m
test_prob_par.m
trajectory.m
trajectory_increase_noise.fig
trajectory_increase_noise.jpg
trajectory_increase_noise.m
trajectory_rand_noise.eps
trajectory_rand_noise.fig
trajectory_rand_noise.jpg
trajectory_rand_noise.m
trajectory_rand_noise.png
v1-v2.mw
                            
N=100000; % total simulation time, ms 

sw=100; % duration of one sweep
dsw=100; % change of the sweep

av=round(N/sw); % number of sweeps

dt=0.1; %ms


% model parameters
c=217; i=0; gl=12.8;
el=-55.144; vt=-56.252;
delta=0.77; vreset=-68;

a=35.4; tauw=7.5; b=323;


% external intput
Am=10; % pA, optimal for inhibition
taus1=1.5; % ms, rise constant
taus2=10; % ms, decay constant
ts=50;


% number of spikes
A(1)=0;
tT=1; % delta bin, ms
bin=tT; % initial size of a bin, ms

vspike=0;
Ihold=-90;


sigma=50;
corr=2;
temp=0;
j=0;
k=0;


% random initial conditions
% variance = 5% of the mean
v(1)=-56.92;
w(1)=-63.08;
input(1)=Ihold;

t(1)=0;

% zero initial conditions for external stimuli
in(1)=0;
m(1)=0;


for i=2:1:round(N/dt)
     t(i)=(i-1)*dt;              
      
     % additional stimulus
     
     % delta function approximation
     if t(i)==ts        
         stim(i)=1/dt;
     else
         stim(i)=0;
     end;
    
     
     % generate external stimuli
     m(i)=dt/taus1/taus2*(Am*(1-in(i-1))*stim(i)/K(1/taus1,1/taus2)-in(i-1)-(taus1+taus2)*m(i-1)) + m(i-1);
     in(i)=m(i)*dt + in(i-1);
     
     % generate total stimuli
     temp=temp-dt/corr*temp + sqrt(2*dt/corr)*random('normal',0,1,1,1); 
     input(i)=Ihold + temp*sigma + in(i);
     

    % no dendrite
     v(i)=dt/c*(-gl*(v(i-1)-el)+gl*delta*exp((v(i-1)-vt)/delta)-w(i-1)+input(i)) + v(i-1);
     w(i)=dt/tauw*(a*(v(i-1)-el)-w(i-1)) + w(i-1);

     % binning             
if t(i)>=bin;
    bin=bin + tT;
    j=j+1;
    
    if k==0  % check for the first sweep
        A(j)=0;
    end;
    
end;

% sweep
if  t(i)>=sw;
    ts=ts+dsw;
    sw=sw + dsw;
    j=1; % reset the bin number
    k=1; % marker for the first sweep
    % start from the down-state
    v(i)=-56.92;
    w(i)=-63.08;
end;             


     
             if  v(i)>vspike
                 v(i-1)=0;            % add sticks to the previous step
                 v(i)=vreset;
                 w(i)=w(i) + b;
                 % +1 spike to the bin
                 A(j)=A(j)+1/av/tT;   % average number of spikes
             end
             
                       
             
end


save psthS50.mat