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 *
                            
%Notes for Dr. Blackwell from Sriram:
%1. What this file does:
% This file makes the input trains that would serve as the cortical and fs input
% for the SPcells. The duplicate and unique input signals are now combined in
% the genesis file SimFile.g. This file also has a variable which contains 
% unique random numbers, the function of which is described in the SimFile.g
% file. The noise and inout signals will be combined in this file for the 
% next round once the details of the input connection is finalized. 

function m = Inputwithcorrelation(corr_syn_Glu, corr_syn_GABA, upFreq, ...
                                              noiseFreq, maxTime, ...
				              pMix, randSeed, ...
                                              numCells)

                                  
rand('seed', randSeed);
randSeed = rand('seed');
downFreq = 1e-9;

disp(['Setting random seed to ' num2str(randSeed)])
disp(['All upstate input, freq ' num2str(upFreq)])

path = [pwd '\INPUTDATA\'];
fprintf('%s\n',path);


nAMPA = 72;
nGABA = 117;

dup_number = randperm(ceil(numCells/2)); % decides the number of cells that will have duplicate spikes within each cell
dup_syn_decider = rand(1,dup_number(1)); % decides the percentage of duplication within the cells that will have duplicates

num_selected_dup = 1;

for nCtr = 1:numCells
             yes_unique=1;
	     if num_selected_dup<=dup_number(1)  
	           dup_yes_no = rand(1)<rand(1);
	           if dup_yes_no==1
		     AMPAInsignal{nCtr} = makeTrainInput(corr_syn_Glu, dup_syn_decider(num_selected_dup), nAMPA, ...
                                   upFreq, maxTime);
                       num_selected_dup = num_selected_dup + 1;
                       yes_unique = 0;
		   else
                       yes_unique = 1;
                   end
	     end
	     
             if yes_unique==1
		     AMPAInsignal{nCtr} =  makeDaughterInput(corr_syn_Glu, nAMPA, ...
                                                 upFreq, maxTime); 
             end
end

dup_number = randperm(ceil(numCells/2));% decides the number of cells that will have duplicate spikes within each cell
dup_syn_decider = rand(1,dup_number(1)); % decides the percentage of duplication within the cells that will have duplicates

num_selected_dup = 1;

for nCtr = 1:numCells
             yes_unique=1;
	     if num_selected_dup<=dup_number(1)
	           dup_yes_no = rand(1)<rand(1);
	           if dup_yes_no==1
		     GABAInsignal{nCtr} = makeTrainInput(corr_syn_GABA, dup_syn_decider(num_selected_dup), nGABA, ...
                                   upFreq, maxTime);
                       num_selected_dup = num_selected_dup + 1;
                       yes_unique = 0;
		   else
                       yes_unique = 1;
                   end
	     end
	     
             if yes_unique==1
		     GABAInsignal{nCtr} =  makeDaughterInput(corr_syn_GABA, nGABA, ...
                                                 upFreq, maxTime);
             end
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for nCtr = 1:numCells
         temp_AMPA = ['AMPAinsignal_' num2str(nCtr) '_'];
         temp_GABA = ['GABAinsignal_' num2str(nCtr) '_'];
	     writeInput(temp_AMPA, AMPAInsignal{nCtr});
         writeInput(temp_GABA, GABAInsignal{nCtr});
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

fid = fopen([path 'inputInfo.txt'], 'w');

fprintf(fid, '%s\n', 'Inputwithcorrelation');
fprintf(fid, '%f\n', corr_syn_Glu);
fprintf(fid, '%f\n', corr_syn_GABA);
fprintf(fid, '%f\n', upFreq);
fprintf(fid, '%f\n', noiseFreq);
fprintf(fid, '%f\n', maxTime);
fprintf(fid, '%d\n', randSeed);
fprintf(fid, '%d\n', numCells);

fclose(fid);

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