Calyx of Held, short term plasticity (Yang Z et al. 2009)

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Accession:118554
This model investigates mechanisms contributing to short term plasticity at the calyx of Held, a giant glutamatergic synapse in the mammalian brainstem auditory system. It is a stochastic version of the model described in: Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2007). A biophysical model of short-term plasticity at the calyx of Held. Neurocomputing, 70:1626-1629. This version introduces stochastic vesicle recycling and release. It has been used to investigate the information transmission properties of this synapse, as detailed in: Yang, Z., Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2008). Wide-band information transmission at the calyx of Held. Neural Computation, 21(4):991-1018.
References:
1 . Yang Z, Hennig MH, Postlethwaite M, Forsythe ID, Graham BP (2009) Wide-band information transmission at the calyx of Held. Neural Comput 21:991-1017 [PubMed]
2 . Hennig MH, Postlethwaite M, Forsythe ID, Graham BP (2007) A biophysical model of short-term plasticity at the calyx of Held, Neurocomputing 70(12):1626-1629
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism: Auditory brainstem;
Cell Type(s): Medial Nucleus of the Trapezoid Body (MNTB) neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: MATLAB;
Model Concept(s): Short-term Synaptic Plasticity; Vestibular;
Implementer(s): Hennig, Matthias H [mhhennig at gmail.com];
Search NeuronDB for information about:  Glutamate;
% Run stochastic CoH synapse model
% Ref: Yang et al, Neural Computation, in press
% Z. Yang, M. Hennig and B. Graham, University of Stirling, 2008

% Stimulus parameters - set to what you want
fre=[10 20 50 100];     % frequencies (Hz)
stimtime=1;             % stimulation time (s)
fstimtype=1;            % Type: (1) regular ISIs, (2) Poisson ISIs

% Time step (no need to change this)
dt = 0.0001; % time step for spike train generation (secs)

% For plotting
syms = ['.', '*', '+', 'o', 's', 'd']';
lines = ['k-','k--','k-.','k:']';
colors = ['k','r','b','m','y','c']';
lwidth = 1;

% Load and plot experimental data
e10 = load('expdata/Ca2mM_10Hz_norm.dat');
plot(e10(:,1), e10(:,2)/100, 'k-');
hold on;
e20 = load('expdata/Ca2mM_20Hz_norm.dat');
plot(e20(:,1), e20(:,2)/100, 'k-');
e50 = load('expdata/Ca2mM_50Hz_norm.dat');
plot(e50(:,1), e50(:,2)/100, 'k-');
e100 = load('expdata/Ca2mM_100Hz_norm.dat');
plot(e100(:,1), e100(:,2)/100, 'k-');

% Do simulations
for i=1:length(fre)
  
  fvec = fre(i)*ones(1, stimtime/dt);
  tvec = dt:dt:stimtime;
  % Stimulus type can be regular or Poisson distributed ISIs
  if fstimtype == 1   % regular ISIs
    [spikes, stimes, isi] = inhreg(tvec, dt, fvec); 
  elseif fstimtype == 2 % Poisson ISIs
    [spikes, stimes, isi] = inhpoiss(tvec, dt, fvec);
  end;
  num = length(isi); 

  % Canonical synapse model
  [psr, npsr] = coh_stoch_mod(isi);

  xtime = stimes(1:num-1);
  resps = npsr(1:num-1);
  
  p=plot(xtime, resps, syms(1,:));
%  p=plot(xtime, resps, syms(mod(i-1,length(syms))+1,:));
  set(p,'Color',colors(mod(i-1,length(colors))+1,:),'LineWidth',lwidth);
  hold on;
end;

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