Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018)

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Accession:227087
Model for the following publication: Adam C. Frank, Shan Huang, Miou Zhou, Amos Gdalyahu, George Kastellakis, Panayiota Poirazi, Tawnie K. Silva, Ximiao Wen, Joshua T. Trachtenberg, and Alcino J. Silva Hotspots of Dendritic Spine Turnover Facilitate Learning-related Clustered Spine Addition and Network Sparsity
Reference:
1 . Frank AC, Huang S, Zhou M, Gdalyahu A, Kastellakis G, Silva TK, Lu E, Wen X, Poirazi P, Trachtenberg JT, Silva AJ (2018) Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory. Nat Commun 9:422 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron with dendritic subunits;
Channel(s):
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; MATLAB;
Model Concept(s): Active Dendrites; Synaptic Plasticity;
Implementer(s): Kastellakis, George [gkastel at gmail.com];
Search NeuronDB for information about:  NMDA;
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function kurt = trevrolls(actPpost)

mpost   = mean( actPpost(:) );
stdpost = std(  actPpost(:) );

s1 =0;
s2 =0;
n = length(actPpost);

for i=1:length(actPpost)
    s1 = s1 + (actPpost(i)/n);
    s2 = s2 + (actPpost(i)^2)/n;    
end

kurt = (s1^2)/s2;
kurt = 1-kurt;

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