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nas_singleNeuron1_sim.m
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nas_singleNeuron1_sim.m
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function varargout = nas_singleNeuron1_sim(varargin)
% NAS_SINGLENEURON1_SIM MATLAB code for nas_singleNeuron1_sim.fig
% NAS_SINGLENEURON1_SIM, by itself, creates a new NAS_SINGLENEURON1_SIM or raises the existing
% singleton*.
%
% H = NAS_SINGLENEURON1_SIM returns the handle to a new NAS_SINGLENEURON1_SIM or the handle to
% the existing singleton*.
%
% NAS_SINGLENEURON1_SIM('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in NAS_SINGLENEURON1_SIM.M with the given input arguments.
%
% NAS_SINGLENEURON1_SIM('Property','Value',...) creates a new NAS_SINGLENEURON1_SIM or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before nas_singleNeuron1_sim_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to nas_singleNeuron1_sim_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help nas_singleNeuron1_sim
% Last Modified by GUIDE v2.5 13-Oct-2017 09:18:38
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @nas_singleNeuron1_sim_OpeningFcn, ...
'gui_OutputFcn', @nas_singleNeuron1_sim_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before nas_singleNeuron1_sim is made visible.
function nas_singleNeuron1_sim_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to nas_singleNeuron1_sim (see VARARGIN)
% Choose default command line output for nas_singleNeuron1_sim
handles.output = hObject;
global NAS;
NAS.guiData.isShowProgress = 1;
NAS.guiData.isSimProgress = 0;
NAS.guiData.isFinishedSim = 0;
NAS.guiData.isSimPause = 0;
NAS.guiData.isSimCancel = 0;
NAS.guiData.changedValue = 0;
set(handles.text15, 'String', sprintf('%.3f', NAS.data.parameters.('simulationParams').('initialTime')));
set(handles.edit2, 'String', sprintf('%.3f', NAS.data.parameters.('simulationParams').('finalTime')));
set(handles.edit3, 'String', sprintf('%.3f', NAS.data.parameters.('simulationParams').('timeStep')));
set(handles.checkbox4, 'Value', 1);
set(handles.checkbox5, 'Value', 1);
set(handles.axes6, 'Visible', 'off');
set(handles.axes4, 'Visible', 'off');
set(handles.text42, 'Visible', 'off');
guidata(hObject, handles);
% Assign a name to appear in the window title.
set(hObject,'Name','Neuron Analyzer and Simulator');
% Move the window to the center of the screen.
movegui(hObject,'center')
[numOfSegments, localData, flagForSpliting] = calculateLocalPropSingleNeuron (NAS.guiData.selectedNeuron, NAS.data.parameters, NAS.data.parameters.geometryType, NAS.data.parameters.maxCompartmentLength);
NAS.data.properties.('custom').('numOfSegments') = numOfSegments;
NAS.data.properties.('custom').('data').('local') = localData;
if flagForSpliting
h = msgbox(sprintf('%s\n%s\n\n %s%d', 'Neuron segments were splited to improve computational accuracy',...
'according to the value specified in the parameters...',...
' Number of splited segments:', numOfSegments));
set(h, 'WindowStyle', 'modal');
uiwait(h);
end
display('.................');
display(sprintf('Processing electrotonics: %s', NAS.guiData.selectedNeuron));
display('.................');
NAS.data.properties.custom = processElectrotonicData2 (NAS.data.properties.custom, NAS.data.parameters.passiveParams, NAS.data.parameters.geometryType);
display('.................');
display(sprintf('Processing Sholl-like analysis: %s', NAS.guiData.selectedNeuron));
display('.................');
NAS.data.properties.custom.('data').('somatofugal').('euclidian') = getSomatofugalProperties3 (NAS.data.properties.custom, NAS.data.parameters, 'euclidian', 0, 0);
NAS.data.properties.custom.('data').('somatofugal').('path') = getSomatofugalProperties3 (NAS.data.properties.custom, NAS.data.parameters, 'path', 0, 0);
NAS.data.properties.custom.('data').('somatofugal').('order') = getSomatofugalProperties3 (NAS.data.properties.custom, NAS.data.parameters, 'order', 0, 0);
display('.................');
display(sprintf('Processing dendritic trees: %s', NAS.guiData.selectedNeuron));
display('.................');
[NAS.data.properties.custom.('data').('branchLevel'), NAS.data.properties.custom.('data').('treeLevel'),...
zeroOrderSegments, NAS.data.properties.custom.('data').('local').segmentsFromUnbranchedSubTrees] = getTreeProperties2 (NAS.data.properties.custom, NAS.data.parameters);
NAS.data.properties.custom.('data').('zeroOrderSegments') = zeroOrderSegments;
display('.................');
display(sprintf('Processing global properties: %s', NAS.guiData.selectedNeuron));
display('.................');
NAS.data.properties.custom.('data').('neuronLevel') = calculateGlobalProp (NAS.data.properties.custom, NAS.data.parameters);
set(handles.text18,'String',sprintf('Simulation of neuron: %s',NAS.guiData.selectedNeuron));
set(handles.text18,'Visible','on');
% Update handles structure
guidata(hObject, handles);
% --- Outputs from this function are returned to the command line.
function varargout = nas_singleNeuron1_sim_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes during object creation, after setting all properties.
function popupmenu1_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function listbox1_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --------------------------------------------------------------------
function menu_home_Callback(hObject, eventdata, handles)
global NAS;
set(NAS.gui.home,'Visible','on');
% set(NAS.gui.single_sim,'Visible','off');
close(gcbf);
% --- Executes on button press in pushbutton5.
function pushbutton5_Callback(hObject, eventdata, handles)
global NAS;
NAS.gui.single_analyze_3DTool = nas_singleNeuron1_analyze_3DTool;
set(NAS.gui.single_analyze_3DTool,'Visible','on');
% --- Executes on button press in pushbutton6.
function pushbutton6_Callback(hObject, eventdata, handles)
global NAS;
str = sprintf(strcat('Measure values:\nNumber of segments: %d\nTotal area of stem dendrites: %.2f um\n',...
'Soma area: %.2f um^2\nTotal area: %.2f um^2\nTotal path length: %.2f um\nMaximum Euclidian distance: %.2f um\n',...
'Maximum path distance: %.2f um\nNumber of bifurcations: %d\nNumber of terminations: %d\nNumber of branches: %d\n',...
'Maximum branch order: %d \nSomatic input impedance: %.2f MOhm'),...
NAS.data.properties.custom.numOfSegments,...
NAS.data.properties.custom.data.neuronLevel.areaTotalStemDendrites,...
NAS.data.properties.custom.data.neuronLevel.areaSoma,...
NAS.data.properties.custom.data.neuronLevel.areaTotal,...
NAS.data.properties.custom.data.neuronLevel.totalPathLength,...
NAS.data.properties.custom.data.neuronLevel.maxEuclidianDistance,...
NAS.data.properties.custom.data.neuronLevel.maxPathDistance,...
NAS.data.properties.custom.data.neuronLevel.numOfBifurcations,...
NAS.data.properties.custom.data.neuronLevel.numOfTerminations,...
NAS.data.properties.custom.data.neuronLevel.numOfBranches,...
NAS.data.properties.custom.data.neuronLevel.maxBranchOrder,...
NAS.data.properties.custom.data.neuronLevel.somaticInputImpedance);
set(handles.text7,'String', str);
set(handles.text7,'Visible','on');
set(handles.pushbutton8,'Visible','on');
set(handles.text8,'Visible','on');
set(handles.text9,'Visible','off');
set(handles.axes2,'Visible','off');
set(handles.popupmenu2,'Visible','on');
% --- Executes on button press in pushbutton7.
function pushbutton7_Callback(hObject, eventdata, handles)
global NAS;
diameter = NAS.data.properties.custom.data.branchLevel.diameter;
[groupName{1:length(diameter)}] = deal('diameter');
boxplot(diameter, groupName', 'notch', 'on', ...
'orientation','horizontal', 'plotstyle', 'compact')
hnl = findobj(gca, 'Tag', 'NotchLo');
set(hnl, 'Marker', '+', 'MarkerSize', 9)
hnh = findobj(gca, 'Tag', 'NotchHi');
set(hnh, 'Marker', '+', 'MarkerSize', 9)
% --- Executes during object creation, after setting all properties.
function edit1_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
global NAS;
h = handles.popupmenu2;
contents = cellstr(get(h,'String'));
str = contents{get(h,'Value')};
if strcmp(str, 'ASCII text file (*.txt)')
disp('ASCII text file (*.txt)')
[FileName,PathName,FilterIndex] = uiputfile('exported_data.txt');
if FilterIndex
fid = fopen(FileName, 'wt+');
a=get(handles.text7, 'String');
for i = 1:size(a, 1)
fprintf(fid, '%s\n',a(i,:));
end
fclose(fid);
end
end
if strcmp(str, 'Matlab data (*.mat)')
disp('Matlab data (*.mat)')
[FileName,PathName,FilterIndex] = uiputfile('exported_data.mat');
if FilterIndex
% a=get(handles.text7, 'String');
a=NAS.data.properties.custom.data.neuronLevel;
save(FileName, 'a');
end
end
% [FileName,PathName] = uiputfile;
% a = handles.text7;
% save(FileName, 'a');
% firstColumn = ['numOfSegments'; NAS.data.properties.custom.data.neuronLevel.names; 'somaticInputImpedance']'
% save('absoluteData.txt','firstColumn','-ascii')
% type('absoluteData.txt')
% --- Executes during object creation, after setting all properties.
function popupmenu2_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton9.
function pushbutton9_Callback(hObject, eventdata, handles)
global NAS;
NAS.gui.single_analyze_configParams = nas_singleNeuron1_analyze_configParams;
% set(NAS.gui.single_analyze,'Visible','on');
set(NAS.gui.single_analyze_configParams,'Visible','on');
% --- Executes on selection change in popupmenu3.
function popupmenu3_Callback(hObject, eventdata, handles)
global NAS;
contents = cellstr(get(hObject,'String'));
chosenProp = contents{get(hObject,'Value')};
prop = NAS.data.properties.custom.data.branchLevel.(chosenProp);
% [groupName{1:length(prop)}] = deal(chosenProp);
boxplot(prop, 'notch', 'on', ...
'orientation','horizontal', 'plotstyle', 'compact')
title(chosenProp)
set(gca,'yticklabel',{[]})
hnl = findobj(gca, 'Tag', 'NotchLo');
set(hnl, 'Marker', '+', 'MarkerSize', 9)
hnh = findobj(gca, 'Tag', 'NotchHi');
set(hnh, 'Marker', '+', 'MarkerSize', 9)
% str = sprintf(strcat('Measure values:\nNumber of segments: %d\nTotal area of stem dendrites: %.2f um\n',...
% 'Soma area: %.2f um^2\nTotal area: %.2f um^2\nTotal path length: %.2f um\nMaximum Euclidian distance: %.2f um\n',...
% 'Maximum path distance: %.2f um\nNumber of bifurcations: %d\nNumber of terminations: %d\nNumber of branches: %d\n',...
% 'Maximum branch order: %d \nSomatic input impedance: %.2f MOhm'),...
% NAS.data.properties.custom.numOfSegments,...
% NAS.data.properties.custom.data.neuronLevel.areaTotalStemDendrites,...
% NAS.data.properties.custom.data.neuronLevel.areaSoma,...
% NAS.data.properties.custom.data.neuronLevel.areaTotal,...
% NAS.data.properties.custom.data.neuronLevel.totalPathLength,...
% NAS.data.properties.custom.data.neuronLevel.maxEuclidianDistance,...
% NAS.data.properties.custom.data.neuronLevel.maxPathDistance,...
% NAS.data.properties.custom.data.neuronLevel.numOfBifurcations,...
% NAS.data.properties.custom.data.neuronLevel.numOfTerminations,...
% NAS.data.properties.custom.data.neuronLevel.numOfBranches,...
% NAS.data.properties.custom.data.neuronLevel.maxBranchOrder,...
% NAS.data.properties.custom.data.neuronLevel.somaticInputImpedance);
%
% set(handles.text9,'String', str);
% set(handles.text9,'Visible','on');
% set(handles.pushbutton8,'Visible','on');
set(handles.text7,'Visible','off');
set(handles.text9,'Visible','on');
set(handles.axes2,'Visible','on');
% --- Executes during object creation, after setting all properties.
function popupmenu3_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function popupmenu4_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function popupmenu5_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function popupmenu6_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton10.
function pushbutton10_Callback(hObject, eventdata, handles)
global NAS;
% set(NAS.gui.single_sim,'Visible','off');
set(NAS.gui.single,'Visible','on');
close(gcbf);
% --- callback resumeSim
function resumeSim(hObject, eventdata, handles)
NAS.guiData.isSimPause = 0;
close(gcf);
% --- callback cancelSim
function cancelSim(hObject, eventdata, handles)
global NAS;
NAS.guiData.isSimCancel = 1;
NAS.guiData.isSimPause = 0;
close(gcf);
% --- callback previewSim
function previewResult(hObject, eventdata, handles)
global NAS;
NAS.guiData.isSimPreview = 1;
NAS.guiData.isSimPause = 0;
close(gcf);
% --- Executes on button press in pushbutton15.
function pushbutton15_Callback(hObject, eventdata, handles)
global NAS;
NAS.guiData.isSimPause = 0;
NAS.guiData.isSimCancel = 0;
NAS.guiData.isSimPreview = 0;
NAS.data.parameters.('simulationParams').('finalTime') = str2double(get(handles.edit2, 'String'));
NAS.data.parameters.('simulationParams').('timeStep') = str2double(get(handles.edit3, 'String'));
set(handles.uipanel6, 'Visible', 'off');
set(handles.uipanel7, 'Visible', 'off');
set(handles.uipanel8, 'Visible', 'off');
set(handles.uipanel9, 'Visible', 'off');
set(handles.pushbutton31, 'Visible', 'on');
numOfSegments = NAS.data.properties.custom.('numOfSegments');
type = NAS.data.properties.custom.('data').('local').('type');
parent = NAS.data.properties.custom.('data').('local').('parent');
childs_cellarray = NAS.data.properties.custom.('data').('local').('childs_cellarray');
radius = NAS.data.properties.custom.('data').('local').('radius');
area = NAS.data.properties.custom.data.local.area;
segment_length = NAS.data.properties.custom.data.local.segment_length;
geometryType = NAS.data.parameters.geometryType;
% Normalized values
Cm = NAS.data.parameters.passiveParams.Cm; % [uF/cm^2]
Rm_soma = NAS.data.parameters.passiveParams.Rm_soma; % [Ohm/cm^2]
Rm_dend = NAS.data.parameters.passiveParams.Rm_dend; % [Ohm/cm^2]
Ra = NAS.data.parameters.passiveParams.Ra; % [Ohm.cm] -- Axial resistance
% Calculate passive parameters for each segment
[cm, rm, ra_prox] = calculatePassiveParams4(Cm, Rm_soma, Rm_dend,...
Ra, numOfSegments, type, parent, radius,...
area, segment_length, geometryType);
% cm in uF
% rm in kOhm
% ra_prox in kOhm
EL = -70.0;
% Parameters for active properties
% gNa = 120 * area(1) * 1e-8; % mS -->testing
gNa = 2000 * area(1) * 1e-8; % mS -->testing
% gKdr = 36 * area(1) * 1e-8; % mS -->testing
gKdr = 10000 * area(1) * 1e-8; % mS -->testing
ENa = 55;
theta_m_Na = -35;
k_m_Na = -7.8;
theta_h_Na = -55;
k_h_Na = 7;
EK = -80;
theta_n_Kdr = -28;
k_n_Kdr = -15;
% Defining dynamic arrays
V = ones(numOfSegments, 1);
h_Na = ones(numOfSegments, 1);
n_Kdr = ones(numOfSegments, 1);
iInj = zeros(numOfSegments, 1);
% subunit kinetics (HH-like)
m_Na_inf = @(V) 1 / (1 + exp((V-theta_m_Na)/k_m_Na));
h_Na_inf = @(V) 1 / (1 + exp((V-theta_h_Na)/k_h_Na));
tau_h_Na = @(V) 120 / (exp((V+50)/15) + exp(-(V+50)/16));
n_Kdr_inf = @(V) 1 / (1 + exp((V-theta_n_Kdr)/k_n_Kdr));
tau_n_Kdr = @(V) 28 / (exp((V+40)/40) + exp(-(V+40)/50));
V = EL*V;
h_Na(1) = h_Na_inf(V(1));
n_Kdr(1) = n_Kdr_inf(V(1));
% setting matrices
A = sparse(numOfSegments, numOfSegments);
B = zeros(numOfSegments, 1);
K = zeros(numOfSegments, numOfSegments);
% setting synaptic occurences (Poisson)
n = 1; % Shape parameter
lambda = 1000; % Rate parameter [Hz]
lambda = lambda/1000; % Converting to ms
% Mean ISI: n/lambda
% meanRate = lambda/n;
% gSyn = @(t) gMaxSyn * (t/tau_syn) * exp(1/(t/tau_syn)); % alpha function
% Synapses as in Destexhe 1994 (not alpha function)
Esyn = 75.0; % (mV)
g_syn_max = 0.00001; % (mS)
r = zeros(numOfSegments,1);
Tmax = 2; % (mM) Maximum concentration of neurotransmitter on the synaptic claft
alpha = 4; % (ms^-1 * mM^-1)
beta = 4; % (ms^-1)
% tau_syn = 1 / ((alpha * Tmax) + beta); % (ms)
% w_syn_inf= (alpha * Tmax) / ((alpha * Tmax) + beta);
pulseDuration = 0.5; % (ms)
g0 = 0;
g1 = 0;
percOfSyn = 0.4;
initialTime = NAS.data.parameters.('simulationParams').('initialTime');
finalTime = NAS.data.parameters.('simulationParams').('finalTime');
timeStep = NAS.data.parameters.('simulationParams').('timeStep');
numOfSteps = (finalTime - initialTime)/timeStep;
timeArray = zeros(numOfSteps+1, 1);
VsArray = V(1) * ones(numOfSteps+1, 1);
VArray = V(1) * ones(numOfSteps+1, numOfSegments);
IArray = zeros(numOfSteps+1, 1); % Injected current (stimulus)
synArray = sparse(numOfSteps+1, numOfSegments);
iSynArray = sparse(numOfSteps+1, numOfSegments);
hasSyn = zeros(numOfSegments,1);
rng(13);
k=1;
for i=1:numOfSegments
if rand() <= percOfSyn
hasSyn(i) = k;
k = k+1;
end
% if i==1
% hasSyn(i) = k;
% k = k+1;
% end
end
numOfSegmentsWithSyn = nnz(hasSyn);
sinapseTimes = sparse(numOfSteps+1, numOfSegmentsWithSyn);
numOfSynapsesOnSegment = zeros(numOfSegmentsWithSyn,1);
for i=1:numOfSegmentsWithSyn
j=1;
first = -log(rand())/lambda;
% first = 0;
if first >= finalTime; continue;
else sinapseTimes(j,i) = first; end
while(1)
tau = -log(rand())/lambda;
newTime = sinapseTimes(j,i)+tau;
if newTime >= finalTime; break; end;
sinapseTimes(j+1,i) = sinapseTimes(j,i) + tau;
j = j+1;
end
numOfSynapsesOnSegment(i) = j;
end
% hasSyn
% numOfSegmentsWithSyn
% full(sinapseTimes)
% numOfSynapsesOnSegment
% neuroTransmitterConc_aux = cell(numOfSteps+1, numOfSegmentsWithSyn);
for i=1:numOfSegmentsWithSyn
for j=1:numOfSynapsesOnSegment(i)
neuroTransmitterConc_aux{j} = zeros(numOfSteps+1, numOfSegmentsWithSyn);
end
end
perc = 0.0;
h = waitbar(perc,'Processing synapses...');
for i=1:numOfSegmentsWithSyn
perc = i/numOfSegmentsWithSyn;
waitbar(perc,h);
for j=1:numOfSynapsesOnSegment(i)
indexOfTimeArray = 0;
for t = initialTime:timeStep:finalTime
indexOfTimeArray = indexOfTimeArray + 1;
timeArray(indexOfTimeArray)=t;
t0 = sinapseTimes(j,i);
t1 = t0 + pulseDuration;
if (t >= t0 && t < t1)
neuroTransmitterConc_aux{j}(indexOfTimeArray, i) = Tmax;
else
neuroTransmitterConc_aux{j}(indexOfTimeArray, i) = 0.0;
end
end
end
end
close(h)
% neuroTransmitterConc_aux{1}
% neuroTransmitterConc_aux{2}
% neuroTransmitterConc_aux{3}
neuroTransmitterConc = sparse(numOfSteps+1, numOfSegmentsWithSyn);
for i=1:numOfSegmentsWithSyn
for j=1:numOfSynapsesOnSegment(i)
indexOfTimeArray = 0;
for t = initialTime:timeStep:finalTime
indexOfTimeArray = indexOfTimeArray + 1;
% timeArray(indexOfTimeArray) = t;
if neuroTransmitterConc(indexOfTimeArray, i) ~= Tmax
neuroTransmitterConc(indexOfTimeArray, i) = neuroTransmitterConc(indexOfTimeArray, i) + neuroTransmitterConc_aux{j}(indexOfTimeArray, i);
end
end
end
end
indexOfTimeArray = 0;
for t = initialTime:timeStep:finalTime
indexOfTimeArray = indexOfTimeArray + 1;
for i=1:numOfSegments
if hasSyn(i) ~= 0
% i
% hasSyn(i)
% alpha
% beta
r_diff = alpha * neuroTransmitterConc(indexOfTimeArray, hasSyn(i)) * (1 - r(i)) - beta * r(i);
r(i) = r(i) + timeStep * r_diff;
end
synArray(indexOfTimeArray, i) = g_syn_max * r(i);
end
end
NAS.data.('results').('synArray') = synArray;
% hasSyn
% full(neuroTransmitterConc)
% whos timeArray
% whos neuroTransmitterConc
% figure
% plot(timeArray, neuroTransmitterConc)
%
% figure
% plot(timeArray, synArray)
%
% figure
% hist(diff(nonzeros(sinapseTimes(:,1))))
% r = zeros(numOfSegments,1);
% synArray = zeros(numOfSteps+1, numOfSegments);
% indexOfTimeArray = 0;
% for t = initialTime:timeStep:finalTime
% indexOfTimeArray = indexOfTimeArray + 1;
% timeArray(indexOfTimeArray)=t;
% for i=1:numOfSegmentsWithSyn
% for j=1:numOfSynapsesOnSegment(i)
% t0 = sinapseTimes(i,j);
% t1 = t0 + pulseDuration;
% if (t >= t0 && t < t1)
% neuroTransmitterConc(indexOfTimeArray, i) = Tmax;
% else
% neuroTransmitterConc(indexOfTimeArray, i) = 0.0;
% end
% end
% end
% end
timeArray = zeros(numOfSteps+1, 1);
tolerance = 0.001; % --> initializing for implicit Euler
numMax = 20; % --> initializing for implicit Euler
simProgress = 0;
indexOfTimeArray = 0;
tic
%% simulation loop
for t = initialTime:timeStep:finalTime
indexOfTimeArray = indexOfTimeArray + 1;
if t >= simProgress*finalTime/100
if NAS.guiData.isShowProgress
set(handles.text42, 'Visible', 'on');
display(sprintf('Progress: %d%%', simProgress));
set(handles.text42, 'String', sprintf('%3.0f %%', simProgress));
v1 = get(handles.checkbox4, 'Value');
if v1
set(handles.text36, 'String', sprintf('%d',round(toc)))
end
v1 = get(handles.checkbox5, 'Value');
if v1
set(handles.text38, 'String', sprintf('%.3f',t))
end
set(handles.text42, 'String', sprintf('%3.0f %%', simProgress));
simProgress = simProgress + 1;
end
drawnow % permits interruption of the loop
end
if NAS.guiData.isSimPause
h_fig1 = figure('Units', 'normalized', 'Position',[0.3,0.3,0.2,0.2],'Toolbar','none',...
'MenuBar','none', 'name', 'Sim. paused', 'NumberTitle','off');
h_panel1 = uipanel(h_fig1,'Units','normalized','Position',[0.05,0.05, 0.9, 0.9]);
h_text1 = uicontrol(h_panel1,'Style','text','Units','normalized',...
'Position',[0.05,0.75,0.95,0.2],'String', 'Please, choose an option:',...
'FontSize', 11, 'ForegroundColor', [0.2, 0.2, 0.2] );
h_push_resume = uicontrol(h_panel1,'Style','pushbutton','Units','normalized',...
'Position',[0.25,0.55,0.5,0.2],...
'String','Resume',...
'TooltipString','Resume simulation',...
'FontSize', 10,...
'Callback',@resumeSim);
h_push_cancel = uicontrol(h_panel1,'Style','pushbutton','Units','normalized',...
'Position',[0.25,0.3,0.5,0.2],...
'String','Cancel',...
'TooltipString','Cancel simulation',...
'FontSize', 10,...
'Callback',@cancelSim);
h_push_preview = uicontrol(h_panel1,'Style','pushbutton','Units','normalized',...
'Position',[0.25,0.05,0.5,0.2],...
'String','Preview result',...
'TooltipString','Preview simulation result','FontSize', 10,...
'Callback',@previewResult);
uiwait(h_fig1);
NAS.guiData.isSimPause = 0;
end
if NAS.guiData.isSimCancel
msgbox('Simulation canceled!');
set(handles.text42, 'String', 'Canceled at 0.0 %');
cla(handles.axes6, 'reset');
set(handles.axes6, 'Visible', 'off');
cla(handles.axes4, 'reset');
set(handles.axes4, 'Visible', 'off');
return;
end
localError = 100.0; % --> initializing for implicit Euler
count = 0;
while (localError > tolerance)
% for aux = 1:155
count = count + 1;
if count >= numMax
display('reached max iterations');
% i
% indexOfTimeArray
% count
count = 1;
break;
end
%% segments loop
for i = 1:numOfSegments
if(type(i) == 1 && parent(i) == 1) % skiping compartments 2 and 3, used only to tell that soma is spherical
continue;
end
if i == 1
h_Na_diff = (h_Na_inf(V(i)) - h_Na(i)) / tau_h_Na(V(i));
n_Kdr_diff = (n_Kdr_inf(V(i)) - n_Kdr(i)) / tau_n_Kdr(V(i));
h_Na(i) = h_Na(i) + timeStep * h_Na_diff;
n_Kdr(i) = n_Kdr(i) + timeStep * n_Kdr_diff;
end
% if hasSyn(i) ~= 0
% % i
% % hasSyn(i)
% % alpha
% % beta
% r_diff = alpha * neuroTransmitterConc(indexOfTimeArray, hasSyn(i)) * (1 - r(i)) - beta * r(i);
% r(i) = r(i) + timeStep * r_diff;
% end
% r_diff = alpha * neuroTransmitterConc(indexOfTimeArray, i) * (1 - r(i)) - beta * r(i);
% r(i) = r(i) + timeStep * r_diff;
if type(i) ~= 1 %% if not soma
A(i, parent(i)) = -1/ra_prox(i);
% A(i, i) = cm(i)/timeStep + 1/rm(i) + 1/ra_prox(i);
A(i, i) = cm(i)/timeStep + 1/rm(i) + 1/ra_prox(i) + synArray(indexOfTimeArray, i);
else
A(i, i) = cm(i)/timeStep + 1/rm(i) + ...
gNa * m_Na_inf(V(i))^3 * h_Na(i) + gKdr * n_Kdr(i)^4;
% A(i, i) = cm(i)/timeStep + 1/rm(i) + ...
% gNa * m_Na_inf(V(i))^3 * h_Na(i) + gKdr * n_Kdr(i)^4 + synArray(indexOfTimeArray, i);
end
daughter_array = cell2mat(childs_cellarray(i));
if ~isempty(daughter_array)
for j = 1:length(daughter_array)
daugther_index = daughter_array(j);
A(i, i) = A(i, i) + 1/ra_prox(daugther_index);
A(i, daugther_index) = -1/ra_prox(daugther_index);
end
end
if type(i) == 1 && parent(i) == -1
% iInj(i) = area(1) * 1e-8 * getSignal('trapezoid', 1.0, 90.0, 0, 1500, t, 10.0, 80.0); % uA
iInj(i) = 0;
% B(i) = (cm(i)/timeStep) * V(i) + EL/rm(i) + iInj(i) +...
% gNa * m_Na_inf(V(i))^3 * h_Na(i) * ENa + ....
% gKdr * n_Kdr(i)^4 * EK + synArray(indexOfTimeArray, i) * Esyn;
B(i) = (cm(i)/timeStep) * V(i) + EL/rm(i) + iInj(i) +...
gNa * m_Na_inf(V(i))^3 * h_Na(i) * ENa + ....
gKdr * n_Kdr(i)^4 * EK;
else
iInj(i) = 0;
B(i) = (cm(i)/timeStep) * V(i) + EL/rm(i) + iInj(i) + synArray(indexOfTimeArray, i) * Esyn;
% B(i) = (cm(i)/timeStep) * V(i) + EL/rm(i) + iInj(i);
end
end
K = inv(A);
V_last = V;
% V = (B\A)';
V = K*B;
% V-V_last
localError = max(abs(V-V_last));
% iSynArray(indexOfTimeArray, i) = g_syn_max * r(i) * (Esyn - V(i));
% synArray(indexOfTimeArray, i) = g_syn_max * r(i);
% synArray(indexOfTimeArray, i) = h_Na(i);
if localError <= tolerance
% disp('tolerance satisfied');
break;
end
if abs(V(i))>1000
disp('V is to large')
break;
end
end
for i = 1:numOfSegments
iSynArray(indexOfTimeArray, i) = synArray(indexOfTimeArray, i) * (Esyn - V(i));
% iSynArray(indexOfTimeArray, i) = r(i);
end
NAS.data.('results').('iSynArray') = iSynArray;
% if indexOfTimeArray == numOfSteps+1
% Esyn
% V
% end
% synArray
% r
timeArray(indexOfTimeArray) = t;
IArray(indexOfTimeArray) = iInj(1);
VsArray(indexOfTimeArray) = V(1);
VArray(indexOfTimeArray,:) = V(:);
if abs(V(i))>1000
disp('V is to large')
break;
end
if indexOfTimeArray == numOfSteps+1
NAS.guiData.isFinishedSim = 1;
NAS.data.('results').('timeArray') = timeArray;
NAS.data.('results').('VsArray') = VsArray;
NAS.data.('results').('VArray') = VArray;
NAS.data.('results').('IArray') = IArray;
% whos synArray
% whos timeArray
%
% figure
% plot(timeArray, synArray);
h1=plot(handles.axes4, timeArray, synArray);
% set(h1, 'LineStyle','none','Marker', 'o',...
% 'MarkerEdgeColor',[0.509, 0.309, 0.490],...
% 'MarkerFaceColor',[0.509, 0.309, 0.490],...
% 'MarkerSize',2);
set(h1, 'LineStyle','-','Marker', 'o',...
'MarkerSize',2);
xlabel(handles.axes4, 'Time (ms)');
ylabel(handles.axes4, 'Syn. conductance (mS)');
% ylabel(handles.axes4, 'Current (uA)');
h2=plot(handles.axes6, timeArray, VsArray);
set(h2, 'LineStyle','none','Marker', 'o',...
'MarkerEdgeColor',[0.509, 0.309, 0.490],...
'MarkerFaceColor',[0.509, 0.309, 0.490],...
'MarkerSize',2);
% set(handles.axes6,'xtick',[])
ylabel(handles.axes6, 'Membrane potential (mV)');
set(handles.popupmenu7, 'Visible', 'on');
set(handles.pushbutton28, 'Visible', 'on');
set(handles.pushbutton32, 'Visible', 'on');
set(handles.text41, 'Visible', 'on');
set(handles.axes6, 'Visible', 'on');
set(handles.axes4, 'Visible', 'on');
set(handles.text42, 'Visible', 'off');
set(handles.uipanel6, 'Visible', 'off');
set(handles.uipanel7, 'Visible', 'off');
set(handles.uipanel8, 'Visible', 'off');
set(handles.uipanel9, 'Visible', 'off');
drawnow;
guidata(hObject, handles);
end
if NAS.guiData.isSimPreview
h1=plot(handles.axes4, timeArray, synArray);
% set(h1, 'LineStyle','none','Marker', 'o',...
% 'MarkerEdgeColor',[0.509, 0.309, 0.490],...
% 'MarkerFaceColor',[0.509, 0.309, 0.490],...
% 'MarkerSize',2);
set(h1, 'LineStyle','-','Marker', 'o',...
'MarkerSize',2);
xlabel(handles.axes4, 'Time (ms)');
ylabel(handles.axes4, 'Syn. conductance (mS)');
% ylabel(handles.axes4, 'Current (uA)');
h2=plot(handles.axes6, timeArray, VsArray);
set(h2, 'LineStyle','none','Marker', 'o',...
'MarkerEdgeColor',[0.509, 0.309, 0.490],...
'MarkerFaceColor',[0.509, 0.309, 0.490],...
'MarkerSize',2);
% set(handles.axes6,'xtick',[])
ylabel(handles.axes6, 'Membrane potential (mV)');
drawnow;
end
end
% --- Executes on button press in pushbutton12.
function pushbutton12_Callback(hObject, eventdata, handles)
global NAS;
NAS.gui.single_analyze_configParams = nas_singleNeuron1_analyze_configParams;
% set(NAS.gui.single_analyze,'Visible','on');
set(NAS.gui.single_analyze_configParams,'Visible','on');
% --- Executes on button press in pushbutton13.
function pushbutton13_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton13 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.uipanel6,'Visible','on');
set(handles.uipanel7,'Visible','off');
set(handles.uipanel9,'Visible','off');
set(handles.uipanel8,'Visible','off');
set(handles.axes4,'Visible','off');
set(handles.axes6,'Visible','off');
cla(handles.axes4);
cla(handles.axes6);
% --- Executes on button press in pushbutton14.
function pushbutton14_Callback(hObject, eventdata, handles)
set(handles.uipanel8,'Visible','on');
set(handles.uipanel9,'Visible','off');
set(handles.uipanel6,'Visible','off');
set(handles.uipanel7,'Visible','off');
set(handles.axes4,'Visible','off');
set(handles.axes6,'Visible','off');
cla(handles.axes4);
cla(handles.axes6);
set(handles.pushbutton22,'Visible','on');
set(handles.pushbutton23,'Visible','on');
set(handles.listbox4,'Visible','on');
set(handles.text32,'Visible','on');