Synchronicity of fast-spiking interneurons balances medium-spiny neurons (Damodaran et al. 2014)

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Accession:156260
This study investigates the role of feedforward and feedback inhibition in maintaining the balance between D1 and D2 MSNs of the striatum. The synchronized firing of FSIs are found to be critical in this mechanism and specifically the gap junction connections between FSIs.
Reference:
1 . Damodaran S, Evans RC, Blackwell KT (2014) Synchronized firing of fast-spiking interneurons is critical to maintain balanced firing between direct and indirect pathway neurons of the striatum. J Neurophysiol 111:836-48 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Neostriatum spiny direct pathway neuron; Neostriatum spiny indirect pathway neuron; Neostriatum fast spiking interneuron;
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s): NMDA; Gaba;
Gene(s):
Transmitter(s):
Simulation Environment: GENESIS;
Model Concept(s): Detailed Neuronal Models; Parkinson's;
Implementer(s): Blackwell, Avrama [avrama at gmu.edu]; Damodaran, Sriraman [dsriraman at gmail.com];
Search NeuronDB for information about:  Neostriatum spiny direct pathway neuron; Neostriatum spiny indirect pathway neuron; NMDA; Gaba;
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striatalnetwork
Matlab_files
Inputwithcorrelation.asv *
Inputwithcorrelation.m *
Inputwithcorrelation.m~
InputwithCorrelation2.m *
InputwithCorrelation2.m~
makeDaughterInput.m *
makeDaughterInsignal.m
makeDaughterInsignal.m~
makeDaughterInsignal_no_upstate.m *
makeTrainInput.m *
makeTrainInsignal.m *
makeTrainInsignal.m~
poissonMaxTime.m *
writeInput.asv *
writeInput.m *
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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