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

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" ... 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.
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:
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;
%Instruction of using Motor uNit INnervation process Generator
%(mNing) to simulate the innervation process of a pool of motoneurons
%The package is written in an object oriented fashion. Two main data
%structures are used: "network" and "para". 
%The first top-level structure "Network" is the structure
%containing various parameters of the motoneuron pools. Three sub-structure
%exists within "network": 
%1) neurons: the individual neurons. Neurons itself is also a structure,
%and its fields are the various parameters in the HH model, such as the
%contances of potassium, sodium, and leak current, the reversal potential,
%etc. There are several fields that are of particular interest within this
%simulation context.
%   a) vna: the reversal potential of Na+. it determins the recruitment
%   threshold of the neuron. The larger this value, the lower the
%   recruitment threshold.
%   b) inpbias: the bias current, or base current of the neuron. Determines
%   the mean firing rate of the neuron. 
%   c) inpsigma: the STD of the input current. Determines the variance of
%   the firing rate of the neuron.
%   d) cdfuncid: The type of Common Input (CI) to the neuorn. This field is
%   an integer (an ID), which corresponds to one of the Common Input
%   functions defined in "network.cdfunc". If it is 0, then there is no CI
%   to this neuron.
%   e) inp: the input current to this neuron. This will be generated during
%   the simulation using related parameters by the runtine called
%   "gene_inp.m"
%2) synapse: the synapse matrix of the network. Generate by routine "MNNsynapse.m";
%3) cdfunc: the common input function that is responsible for the COMMON
%DRIVE effect within the MN pool. Itself is alos a structure array with the
%following fields:
%   a)  type: an integer that determines the type, 1 means generate on the
%   fly; 2 means using a stored data;
%   b)  rms: the rms value of the CI function. it should be zero if "type"
%   is set to 2;
%   c)  name: the full path of the stored data file;
%   d)  func: the CI function. This will be generate by the routine
%   "gene_cdfun.m"
%The second top-level structure "para" is the structure containing various simulation parametres:
%1) duration: the duration of the simulation, in sec
%2) stepsize: the stepsize of the ODE solver, in msec
%3) trial_num: repeatation of simulations
%4) resultfilepath: the path where to store the simulation results
%5) resultfilename: the common file name for the simulation result files

%This is a sample script of using the various functions in mNing package to
%simulate a MN pool of 11 MNs. In this pool, MN(1) is the common excitor
%for the other 10 MNs. MN(2:6) and MN(7:11) are two MN groups, each have a
%CI of their own. There is a recruitment order within each of the group.
%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

%%  Loading neuron template 
%loading template
load template.mat;
clear neurons;
%%  Creating the "neuron" structure
%size of the pool
%range of Vna--->range of recruitment level
%creating MN objects in a batch model. The common excitor is set to have
%the lowest recruitment threshold.
%The first group MN(2:6)
%The second group MN(7:11)
%%  Creating the synaptic matrix
%Updating the ODE functions. This should be done when ever the network
%structure changes, such as parameter changes in any of the "neurons"
%substructure, or the synapse matrix. However, the changes of CI functions
%does not necessarily mean an update of "simulation", since it is not a
%"structrual change"
%%  Defining CD functions
%number of CD functions
%creating the sub-structure of 'cdfun' for later use
% .type: is the type of cdfuns --> 1: created On the fly during simulation.
%                                     it requires non zero value of the field 'rms'; 
%                                  2: use saved funcs. it requires the field 'name' 
%                                     to be a valid pathname/filename
% .rms: the rms value of the CD fun. if type is 1; it is 0 if type is 2;
% .name: the path/filename of the stored cdfun data if type is 1; "N/A" if type
% is 1
for n=1:nCD
%Or if pre-stored CI data is to be used:
% network.cdfunc.type=2;
% network.cdfunc.rms=0;
% network.cdfunc.name=[matlabroot,'\work\mNing\data\cidata\testfunc'];
%Assigning the CI func IDs to individual MNs; the field "cdfunid" in each
%neuron corresponds to the field "cdfunc" in network. If "cdfuncid" is zero,
%meaning there is no cdfun to that neuron. If "cdfuncid" is some non-zero
%number N, meaning the CI func to this neuron is the the cdfun number N.
%In this example, MN(2:6) have a CI func ID of 1, and MN(7:11) have a CI func ID of 2:
%assigncd=ones(1,nMN); %all have the same CI function
%assigncd=zeros(1,nMN); %no CI functions at all
for n=1:nMN
%%  Setting up simulation parameters
%the integration stepsize for the ODE solver, in msec
%duration of simulation, in sec
%number trials;
%full path of result data
%%  begin simulation
%%  Displaying the simulation results
% The input currents to each MN
% The firing times of each MN

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