Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010)

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Accession:114359
The code provided here was written to systematically investigate which of the physical parameters controlled by dendritic morphology underlies the differences in spiking behaviour observed in different realizations of the 'ping-pong'-model. Structurally varying dendritic topology and length in a simplified model allows us to separate out the physical parameters derived from morphology underlying burst firing. To perform the parameter scans we created a new NEURON tool the MultipleRunControl which can be used to easily set up a parameter scan and write the simulation results to file. Using this code we found that not input conductance but the arrival time of the return current, as measured provisionally by the average electrotonic path length, determines whether the pyramidal cell (with ping-pong model dynamics) will burst or fire single spikes.
Reference:
1 . van Elburg RA, van Ooyen A (2010) Impact of dendritic size and dendritic topology on burst firing in pyramidal cells. PLoS Comput Biol 6:e1000781 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic GLU cell;
Channel(s): I Na,t; I K; I M; I K,Ca; I Sodium; I Calcium; I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Activity Patterns; Bursting; Spatio-temporal Activity Patterns; Simplified Models; Active Dendrites; Influence of Dendritic Geometry; Detailed Neuronal Models; Methods;
Implementer(s): van Elburg, Ronald A.J. [R.van.Elburg at ai.rug.nl];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; I Na,t; I K; I M; I K,Ca; I Sodium; I Calcium; I Potassium;
% Filename: Pruning.m
%
% Calculate burstmeasure from several pruned trees, and show traces for
% some example trees.
%
% Author: Ronald A.J. van Elburg ,(RonaldAJ at vanelburg eu)
% Affiliation:
%           Department of Artificial Intelligence
%           Groningen University
%
%
% Set path to location spike analysis script

addpath(genpath('./../analysis'))

FilenameBase='Pruning/Results/Sim';
FiguresNameBase='Pruning/Figures/';
offset=0; % Offset 0 -> indices for .dat filenames start at 1, Offset -1 -> indices for .dat filenames start at 0
Filename='';
ISI_Cutoff=3000;
Analysis_Start=1000;
stimulation={'somatic','dendritic'}

% Specify endianness
machineformat='l';

% Morphological parameter ranges
dendStimRange =[0,1];

% Specify Seeds
Seed_step=1;
Seed_start=1;
Seed_end=20; % Seed_end=20; was used in paper
Seed_simrange=Seed_start:Seed_step:Seed_end;
Seed_plotrange=1:1:length(Seed_simrange);

% Specify Pruning Depths
PruningDepth_step=1;
PruningDepth_start=0;
PruningDepth_end=20;
PruningDepth_simrange=PruningDepth_start:PruningDepth_step:PruningDepth_end;
PruningDepth_plotrange=1:1:length(PruningDepth_simrange);

% Map ranges to reusable variables
x_plotrange=Seed_plotrange;
x_simrange=Seed_simrange;
x_plot_size=length(x_plotrange);

y_plotrange=PruningDepth_plotrange;
y_simrange=PruningDepth_simrange;
y_plot_size=length(y_plotrange);

% Preallocate storage objects
spikes=cell(2,x_plot_size,y_plot_size);
tvec=cell(2,x_plot_size,y_plot_size);
yvec=cell(2,x_plot_size,y_plot_size);

f=zeros(2,x_plot_size,y_plot_size);
B2=zeros(2,x_plot_size,y_plot_size);
MeanISI=zeros(2,x_plot_size,y_plot_size);

Depth=zeros(2,y_plot_size);
B2Mean=zeros(2,y_plot_size);
B2SEM=zeros(2,y_plot_size);
fMean=zeros(2,y_plot_size);
fSEM=zeros(2,y_plot_size);

% Load simulation results and calculate quantities of interest: burstmeasure
% , MeanISI, spikeFrequency. 
for dendStim=dendStimRange+1  
    for y_coord=1:1:y_plot_size     %PruneDepth
        for x_coord=1:1:x_plot_size % Seed

            Filename=[FilenameBase,'_',num2str(dendStim),'_',num2str(x_plotrange(x_coord)+offset),'_',num2str(y_plotrange(y_coord)+offset),'_j4a_spikes_j4a_spikes_soma.dat']        
            j4a_spikes_soma=nrn_vread(Filename,machineformat);                                             
            
            [B2(dendStim,x_coord,y_coord),MeanISI(dendStim,x_coord,y_coord)] = burstMeasure(j4a_spikes_soma(j4a_spikes_soma > Analysis_Start),ISI_Cutoff);
            if(isnan(B2)==1)
                B2=-1;
            end

            spikes{dendStim,x_coord,y_coord}=j4a_spikes_soma;

            f(dendStim,x_coord,y_coord)=spikeFrequency(j4a_spikes_soma(j4a_spikes_soma > Analysis_Start));
            
        end
        [B2Mean(dendStim,y_coord),B2SEM(dendStim,y_coord)]=grpstats(B2(dendStim,:,y_coord)',ones(x_plot_size,1), {'mean','sem'});
        [fMean(dendStim,y_coord),fSEM(dendStim,y_coord)]=grpstats(f(dendStim,:,y_coord)',ones(x_plot_size,1),{'mean','sem'});
        
        Depth(dendStim,y_coord)=y_simrange(y_plotrange(y_coord));
    end
end

%%
figure(3)
trace_indices={[2,6],[2,6];[2,7],[2,7];[17,6],[17,6];[17,7],[17,7]};
for dendStim=dendStimRange+1
    subplot(2,2,dendStim)
    errorbar(Depth(dendStim,:),B2Mean(dendStim,:),B2SEM(dendStim,:),'r.')
    set(gca,'Box','off')
    xlabel('Prune depth')
    ylabel('Burstiness')
    xlim([0,20])
    ylim([0,0.8])
    title(stimulation{dendStim})
end

for row_no=1:2
    for col_no=1:2
        for dendStim=dendStimRange+1
            trace_seed=trace_indices{(row_no-1)*2+col_no,dendStim}(1);
            trace_depth=trace_indices{(row_no-1)*2+col_no,dendStim}(2);
            x_coord=trace_seed;
            y_coord=trace_depth;

            Filename=[FilenameBase,'_',num2str(dendStim),'_',num2str(x_plotrange(x_coord)+offset),'_',num2str(y_plotrange(y_coord)+offset),'_j4a_vtrace_soma_tvec.dat'];         
            tvec{dendStim,x_coord,y_coord}=nrn_vread(Filename,machineformat);

            Filename=[FilenameBase,'_',num2str(dendStim),'_',num2str(x_plotrange(x_coord)+offset),'_',num2str(y_plotrange(y_coord)+offset),'_j4a_vtrace_soma_yvec.dat'];         
            yvec{dendStim,x_coord,y_coord}=nrn_vread(Filename,machineformat);

            position=(row_no-1)*24+(col_no-1)*3+(dendStim-1)*6+61
            subplot(16,6,position:position+2)
            plot(tvec{dendStim,trace_seed,trace_depth},yvec{dendStim,trace_seed,trace_depth},'k-')

            ylim([-80,40])
            set(gca,'Visible','off')
            xlim([2000,3000])           
        end
    end
end

subplot(16,6,position:position+2)
scaleBar(2800,-70,100,50,'100 ms','50 mV')



%%
figure(301)

for dendStim=dendStimRange+1
    subplot(2,2,dendStim)
    hold on        
    for y_coord=1:1:x_plot_size
        A=zeros(y_plot_size,1);
        A(1:end,1)=B2(dendStim,y_coord,1:end);
        plot(Depth(dendStim,:),A,'r.')
    end
    set(gca,'Box','off')
    xlabel('Prune depth')
    ylabel('Burstiness')
    xlim([0,20])
    ylim([0,0.8])
    title(stimulation{dendStim})
end


%% 
figure(302)
clf
A=zeros(y_plot_size,1);
for x_coord=1:1:x_plot_size;
    
    subplot(4,5,x_coord)
    hold on
    A(1:1:y_plot_size,1)=B2(1,x_plotrange(x_coord),:);
    plot(y_plotrange,A,'r+')
    A(1:1:y_plot_size,1)=B2(2,x_plotrange(x_coord),:);
    plot(y_plotrange,A,'bo')
    set(gca,'Box','off')
    xlabel('Index')
    ylabel('Burstiness')
    xlim([0,21])
    ylim([0,0.9])
    myText=['Seed=',num2str(x_coord)];
    text(12,0.7,myText)
end

subplot(4,5,1)

legend(stimulation)

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