Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013)

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Accession:147117
This entry includes tools for generating and analyzing network structure, and for running the neuronal network simulations on them.
Reference:
1 . Mäki-Marttunen T, Acimovic J, Ruohonen K, Linne ML (2013) Structure-dynamics relationships in bursting neuronal networks revealed using a prediction framework. PLoS One 8:e69373 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Abstract Wang-Buzsaki neuron; Abstract integrate-and-fire leaky neuron;
Channel(s): I Na,p; I Na,t; I K; I K,leak; I M;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEST; MATLAB; Python;
Model Concept(s): Bursting; Synchronization; Short-term Synaptic Plasticity; Methods; Connectivity matrix;
Implementer(s): Maki-Marttunen, Tuomo [tuomomm at uio.no];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; GabaA; AMPA; NMDA; I Na,p; I Na,t; I K; I K,leak; I M; Gaba; Glutamate;
%function [path_lengths, lengths_to_self] = pathlength(M,path_length_max)
%
%  Calculates the path lengths between the nodes of the network
%
%  Input:
%    M - N x N connectivity matrix
%    path_length_max - the maximum considered path length (20 is an ad hoc
%      limit that should be enough for connectivities p larger than 0.1)
%
%  Output:
%    path_lengths - a 1 x path_length_max vector indicating the number of
%                   paths of length 1...path_length_max
%    lengths_to_self - a 1 x path_length_max vector indicating the number of
%                      paths to self of length 1...path_length_max
%
%  Tuomo Mäki-Marttunen
%  Last modified 8.1.2013

function [path_lengths,lengths_to_self] = pathlength(M,path_length_max)

if nargin < 2 || isempty(path_length_max)
    path_length_max = 20;
end

N = size(M,1);

% Calculate the number of paths of length 1...path_length_max from each
% node to each node
Ms = zeros(N,N,path_length_max+1);
Ms(:,:,1) = M;
accounted_for = zeros(N);
pathl = zeros(N); %Save here the shortest path lengths, zero meaning that no path was found
for i=1:path_length_max
    pathl = pathl + i*(Ms(:,:,i) > 0 & ~accounted_for);
    accounted_for = accounted_for | Ms(:,:,i) > 0;
    Ms(:,:,i+1) = Ms(:,:,i)*M;
end

path_lengths = zeros(1,path_length_max);
lengths_to_self = zeros(1,path_length_max);
for i=1:path_length_max
    path_lengths(i) = sum(sum(pathl==i));
    lengths_to_self(i) = sum(diag(pathl==i));
end