Striatal NN model of MSNs and FSIs investigated effects of dopamine depletion (Damodaran et al 2015)

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Accession:169984
This study investigates the mechanisms that are affected in the striatal network after dopamine depletion and identifies potential therapeutic targets to restore normal activity.
Reference:
1 . Damodaran S, Cressman JR, Jedrzejewski-Szmek Z, Blackwell KT (2015) Desynchronization of Fast-Spiking Interneurons Reduces beta-Band Oscillations and Imbalance in Firing in the Dopamine-Depleted Striatum. J Neurosci 35:1149-59 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell; Axon; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Neostriatum spiny direct pathway neuron; Neostriatum spiny indirect pathway neuron; Neostriatum fast spiking interneuron;
Channel(s): I Sodium; I Potassium; Kir;
Gap Junctions: Gap junctions;
Receptor(s): D1; D2; GabaA; Glutamate;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: GENESIS;
Model Concept(s): Synchronization; Detailed Neuronal Models; Parkinson's;
Implementer(s): Damodaran, Sriraman [dsriraman at gmail.com];
Search NeuronDB for information about:  Neostriatum spiny direct pathway neuron; Neostriatum spiny indirect pathway neuron; D1; D2; GabaA; Glutamate; I Sodium; I Potassium; Kir; Gaba; Glutamate;
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DamodaranEtAl2015
Matlab_files
Inputwithcorrelation.asv *
Inputwithcorrelation.m *
InputwithCorrelation2.m *
makeDaughterInput.m *
makeDaughterInsignal.m *
makeTrainInput.m *
makeTrainInsignal.m *
poissonMaxTime.m *
writeInput.asv *
writeInput.m *
                            
%
% Generates 2Hz square waves with 0.5 dutycycle.
%

function noise = makeTrainInput(corr_syn, percSingleRepeats, nSyn, freq, maxTime)

	nShare = nSyn - sqrt(corr_syn)*(nSyn-1);
	allSpikes = poissonMaxTime(freq*nShare, maxTime);
  
	for i=1:nSyn
		finalSpikes{i} = [];
	end
  
	for i=1:length(allSpikes)
		repeats = nSyn*percSingleRepeats;
		repeats = floor(repeats) + (rand(1) < mod(repeats,1));
		freeTrains = 1:nSyn;
     
		for j=1:repeats
			idx = ceil(length(freeTrains)*rand(1));
			trainIdx = freeTrains(idx);
			freeTrains(idx) = [];
			finalSpikes{trainIdx} = [finalSpikes{trainIdx}; allSpikes(i)];
		end     
	end

	for i=1:nSyn
		trainLen(i) = length(finalSpikes{i}); 
	end

	maxLen = max(trainLen); 
	noise = 0*ones(maxLen,nSyn);

	for i=1:nSyn
		noise(1:length(finalSpikes{i}),i) = finalSpikes{i}; 
	end
  

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