A simulation method for the firing sequences of motor units (Jiang et al 2006)

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Accession:83320
" ... a novel model based on the Hodgkin–Huxley (HH) system is proposed, which has the ability to simulate the complex neurodynamics of the firing sequences of motor neurons. The model is presented at the cellular level and network level, and some simulation results from a simple 3-neuron network are presented to demonstrate its applications." See paper for more and details.
Reference:
1 . Jiang N, Englehart KB, Parker PA (2007) A simulation method for the firing sequences of motor units. J Electromyogr Kinesiol 17:527-34 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Spinal motoneuron;
Cell Type(s):
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Simplified Models;
Implementer(s): Jiang, Ning [ning.jiang at unb.ca];
Search NeuronDB for information about:  I Na,t; I K;
function cdfun=gene_cdfun(rms,stepsize,duration,option)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%This routine generates a common input (CI) function (for Common Drive purpose)
%Inputs:
%rms:       the rms value of the resuling common input function
%stepsize:  the stepsize for the HH (simulator), in msec
%duration:  the duration of the common input function (simulation data
%length), in seconds.
%option:    option =1 --> detrending included; option=0 -->no detrending
%%lwin_size: the time window width of low pass filter
%hwin_size: the time window width of the highpass filter (de-trending
%filter), as described by DeLuca et al.
%
%output:
%cdfun: the common input function
%
%Written by Ning Jiang, Institute of Biomedical Engineering, Univesity of New
%Brunswick, NB, Canada, E3B 5A3
%Email: ning.jiang@unb.ca
%
%Date: Nov 19, 2006
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargin == 4
    lwin_size=400; %400 ms rectangular time window
    hwin_size=500; %500 ms rectangular time window
end

%filter width considering stepsize
a_lwinsize=lwin_size/stepsize;
a_hwinsize=hwin_size/stepsize;

%normalize rms value
b=hann(a_lwinsize);
b=b/sum(b);
%the variance of the 'cdfun' after lowpass filtering
%denoted by Y, is a function of the variance of the 'cdfun' before lowpass
%filtering, since the lowpass filtering is just a moving average operation:
%Var(y)=Var(x)*sum(ai.^2)
%where ai is the coefficient of the filter of choice (here is hanning
%filter)
a_rms=rms/(sqrt(sum(b.^2)));
cdfun=randn(1,(duration*1000/stepsize)+a_lwinsize+a_hwinsize+1)*a_rms;
cdfun=filter(b,1,cdfun);
if option == 1
    b=ones(1,a_hwinsize)/a_hwinsize;
    tmp=filter(b,1,cdfun);
    cdfun=cdfun-tmp;
end
cdfun=cdfun(a_lwinsize+a_hwinsize+1:length(cdfun));

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