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;
function synapse=MNNsynapse(preMN,postMN,type,synapse)
%Creating or updating the synapse matrix for the MN network
%preMN: the ID of the pre synaptic MNs, a vector of integers;
%postMN: the ID of the post synaptic MNs, vector of inegers;
%type: type of operation: 'E': excitory; 'I': inhibitory; 'R': reset to
%synapse: the input synapse matrix, or the number MN in the network. It should be either
%a square matrix or a integer. If the input synapse is a square matrix, it is the input matrix; 
%if it is a integer, it is the number MNs of the network.
%synapse: the synapse matrix of the MN network.
%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

%testing if the input argument "synapse" is a integer or a square matrix
if size(synapse,1)==1

%from here on, the variable "synapse" is the synapse matrix
%Removing incorrect preMN and postMN entries, such as zero entries and
%entries that are larger than the size of the network.
preMN=sort(preMN((preMN ~= 0) & (preMN <= size(synapse,1))));
postMN=sort(postMN((postMN ~= 0) & (postMN <= size(synapse,1))));

%excitory synapses
for pre=1:length(preMN)

%inhibitory synapse
if type=='i'

%removing existing synapse
if type=='r'

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