%% This code is written by Nooshin Abdollahi - Dec7 2021
% Matlab codes necessary for peripheral network modeling.
% The output of this code will be saved and loaded in Python script.
clc
clear all
close all
%% How many axons of each group?
Desired_Total_Number_Axons = 20 ; % Total number of axons (both motor and sensory) in a fascicle
Number_Motor_Axons = 0 * Desired_Total_Number_Axons ; % Based on literature 15% motor axons
Number_Sensory_Axons = Desired_Total_Number_Axons - Number_Motor_Axons ; % Based on literature 85% sensory axons
Number_C = 0 ;
Number_Adelta = 0 ;
Number_Abeta = 20 ;
Total_Number_Axons = Number_Motor_Axons + Number_C + Number_Adelta + Number_Abeta ;
%% Range of radius (diameter) in each group
% ** C fibers have the the smallest radius
% Motor axons
Motor_Axons_radius = 2.85:8 ; % diameter:[5.7 16] => radius:[2.85 8]
mean_Motor_Axons_radius = mean(Motor_Axons_radius) ;
% Sensory axons
Sensory_C_radius = 0.1:0.1:0.75 ; % C fiber diameters [0.2 1.5]
mean_Sensory_C_radius = mean(Sensory_C_radius) ;
Sensory_Adelta_radius = 0.5:0.1:3 ; % diameters [1 6]
mean_Sensory_Adelta_radius = mean(Sensory_Adelta_radius) ;
Sensory_Abeta_radius = 3:0.1:6 ; % diameters [6 12]
mean_Sensory_Abeta_radius = mean(Sensory_Abeta_radius) ;
Sensory_Aalpha_radius = 6:0.1:10 ; % I do not include this [12 20]
mean_Sensory_Aalpha_radius = mean(Sensory_Aalpha_radius) ;
%% Distribution of radius (diameters) of different types of axons
% Distributions are basaed on Gaussian
% Group1:motor Group2:C fiber Group3:Adelta Group4:Abeta
%%%%%%%%%%%%%%%%%%%%%%%%% Group1: Motor axons %%%%%%%%%%%%%%%%%%%%%%%%%%%%
Motor_Axons_dist = normrnd(mean_Motor_Axons_radius, 0.5 ,[1,Number_Motor_Axons]) ; % you can change sigma (=0.5). increasing sigma => wider distribution
for i=1:length(Motor_Axons_dist)
Motor_Axons_dist(1,i) = round(Motor_Axons_dist(1,i),1) ; % round it to 1 number after decimal
% if Motor_Axons_dist(1,i) < 2.85
% Motor_Axons_dist(1,i) = 2.85 ;
% end
%
% if Motor_Axons_dist(1,i) > 8
% Motor_Axons_dist(1,i) = 8 ;
% end
end
Motor_Axons_dist ;
% figure
% plot(1:Number_Motor_Axons, Motor_Axons_dist , 'ob')
% ylim([0 15])
% title('Motor axons')
%%%%%%%%%%%%%%%%%%%%%%%%%% Sensory axons %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Group2: C fibers
%Sensory_C_dist = abs (normrnd(mean_Sensory_C_Diam , 0.5 ,[1,Number_C]) ) ; % you can change sigma
% for i=1:length(Sensory_C_dist)
% Sensory_C_dist(1,i) = round(Sensory_C_dist(1,i),1) ;
% end
Sensory_C_dist = mean_Sensory_C_radius*ones(1,Number_C) ; % all C fibers have the same radius (no heterogenety within C fibers)
Sensory_C_dist ;
figure
plot(1:Number_C, Sensory_C_dist , 'ob')
ylim([0 15])
title('C fibers')
ylabel('radius')
xlabel('#axon')
% Group3: Adelta
Sensory_Adelta_dist = abs (normrnd(mean_Sensory_Adelta_radius , 0.5 ,[1,Number_Adelta]) ) ; % you can change sigma, *abs() becacuse radius values cannot be negative.
for i=1:length(Sensory_Adelta_dist)
Sensory_Adelta_dist(1,i) = round(Sensory_Adelta_dist(1,i),1) ; % round to have one number after decimal
% if Sensory_Adelta_dist(1,i) < 0.5
% Sensory_Adelta_dist(1,i) = 0.5 ;
% end
%
% if Sensory_Adelta_dist(1,i) > 3
% Sensory_Adelta_dist(1,i) = 3 ;
% end
end
Sensory_Adelta_dist ;
figure
plot(1:Number_Adelta, Sensory_Adelta_dist , 'ob')
ylim([0 15])
title('Adelta fibers')
ylabel('radius')
% Group4: Abeta
Sensory_Abeta_dist = abs (normrnd(mean_Sensory_Abeta_radius , 0.5 ,[1,Number_Abeta]) ) ; % you can change the sigma
for i=1:length(Sensory_Abeta_dist)
Sensory_Abeta_dist(1,i) = 6 ;
% if Sensory_Abeta_dist(1,i) < 3
% Sensory_Abeta_dist(1,i) = 3 ;
% end
%
% if Sensory_Abeta_dist(1,i) > 6
% Sensory_Abeta_dist(1,i) = 6 ;
% end
end
Sensory_Abeta_dist ;
figure
plot(1:Number_Abeta, Sensory_Abeta_dist , 'ob')
ylim([0 15])
title('Abeta fibers')
ylabel('radius')
%%%%%%%%%%%%%%%%%%% put all axons in a vector %%%%%%%%%%%%%%%%%%%%%%%%%%%%
All_Axons(1:Number_Motor_Axons,1) = Motor_Axons_dist ;
All_Axons(Number_Motor_Axons+1:Number_Motor_Axons+Number_C,1) = Sensory_C_dist ;
All_Axons(Number_Motor_Axons+Number_C+1:Number_Motor_Axons+Number_C+Number_Adelta,1) = Sensory_Adelta_dist ;
All_Axons(Number_Motor_Axons+Number_C+Number_Adelta+1:Number_Motor_Axons+Number_C+Number_Adelta+Number_Abeta,1) = Sensory_Abeta_dist ;
size(All_Axons);
% which axon belongs to which group?
GG(1:Number_Motor_Axons,1)=1 ;
GG(Number_Motor_Axons+1:Number_Motor_Axons+Number_C,1)=2 ;
GG(Number_Motor_Axons+Number_C+1:Number_Motor_Axons+Number_C+Number_Adelta,1)=3 ;
GG(Number_Motor_Axons+Number_C+Number_Adelta+1:Number_Motor_Axons+Number_C+Number_Adelta+Number_Abeta,1)=4 ;
%I do not save GG, I save G becasue G is the final grouping after shuffling spikes
%%%%%%%%% shuffling the axons in the "All_Axons" vector randomely %%%%%%%%%
% suffling is important for next part (i.e. pack_v)
shuffled_All_Axons = All_Axons(randperm(length(All_Axons)))' ;
for i=1:length(shuffled_All_Axons)
f = find(All_Axons == shuffled_All_Axons(1,i)) ;
G(i,1)=GG(f(1,1),1) ;
end
G ; % Grouping for shuffled_All_Axons (save this)
%% Fiber packing
[C,R] = pack_v(shuffled_All_Axons) ; % C:(x,y) (Coordinates) R includes radius
%% plotting fiber packing (i.e. location of each axon)
figure
for i=1:length(R)
if G(i,1)==1
viscircles(C(i,:),R(i,1), 'color', 'b','Linewidth',0.2) % motor axons are shown in blue
elseif G(i,1)==2
viscircles(C(i,:),R(i,1), 'color', 'r','Linewidth',0.2) % C fibers are shown in red
elseif G(i,1)==3
viscircles(C(i,:),R(i,1), 'color', 'g','Linewidth',0.2) % Adelta fibers are shown in green
elseif G(i,1)==4
viscircles(C(i,:),R(i,1), 'color', 'k','Linewidth',0.2) % Abeta fibers are shown in black
end
hold on
end
axis square
%% numbering the axons in the figure above
axis square
a = [0:length(C)-1]'; b = num2str(a); c = cellstr(b);
dx = 0.2; dy = 0.1; % displacement so the text does not overlay the data points
text(C(:,1)-dx, C(:,2), c , 'FontSize',10);
%%
for i=2:length(R)
R(i,1) = R(i-1,1) + 0.0000001 ;
end
%% Next part: In NEURON, I need to build axons with different diameters. In this section, I will find the morphological parameters (e.g. internodelength)dependent on FiberD
%I will save the parameters from this section and use it to build the axons in NEURON
unique_radius = unique(R) ; %unique_radius(1,1): C fiber radius
size(unique_radius) ;
%% the parameters (p1,p2,p3) are obtained from another Matlab Code ("MRGcurvefitting")
% figure
for i = 1:length(unique_radius) % it starts from 2 because C fiber's radius is the smallest one : unique_radius(1,:) and C fibers are unmyelinated
x= unique_radius(i,1)*2 ; % to calculate diameters
p1 = 0.01876 ;
p2 = 0.4787 ;
p3 = 0.1204 ;
axonD = p1*x^2 + p2*x + p3 ;
paraD2 = axonD ;
p1 = 0.006304 ;
p2 = 0.2071 ;
p3 = 0.5339 ;
nodeD = p1*x^2 + p2*x + p3 ;
paraD1 = nodeD ;
p1 = -8.215 ;
p2 = 272.4 ;
p3 = -780.2 ;
deltax = p1*x^2 + p2*x + p3 ;
p1 = -0.0199 ;
p2 = 3.016 ;
p3 = 17.44 ;
paralength2 = p1*x^2 + p2*x + p3 ; % FLUT length
paralength2_segments = 5 ;
p1 = -0.389 ;
p2 = 14.88 ;
p3 = 9.721 ;
nl = round ( p1*x^2 + p2*x + p3 ) ;
paralength1=3 ;
nodelength=1.0 ;
interlength=(deltax-nodelength-(2*paralength1)-(2*paralength2)) ;
internode_segments = 40 ; % number of segments in one internode
parameters(i,1) = axonD ;
parameters(i,2) = nodeD ;
parameters(i,3) = deltax ;
parameters(i,4) = paralength2/paralength2_segments ;
parameters(i,5) = nl ;
parameters(i,6) = interlength/internode_segments ; % length of each segment
% plot(1:6 , parameters(i-1,:) )
% hold on
% title('Parameters')
end
for ww = 2:20
parameters(ww,1) = parameters(1,1) ;
parameters(ww,2) = parameters(1,2) ;
parameters(ww,3) = parameters(1,3) ;
parameters(ww,4) = parameters(1,4) ;
parameters(ww,5) = parameters(1,5) ;
parameters(ww,6) = parameters(1,6) ;
end
%% Next part: Find the neighbouring axons (for connection)
N=zeros(length(C),length(C)) ;
dist=zeros(length(C),length(C)) ;
Distance_edge=zeros(length(C),length(C)) ;
Distance_Centers = pdist2(C, C) ; % distance between centers
%%%%%%% Distance between edges of circles not centers of circles %%%%%%%%%
for i=1:length(Distance_Centers)
for j=1:length(Distance_Centers)
if i ~= j
Distance_edge(i,j)= Distance_Centers(i,j) - (R(i,1)+R(j,1)) ;
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
m= 1000 ; % minimum distance (threshold)
for k=1:length(Distance_Centers)
for i=1:length(Distance_Centers)
if k ~= i
if Distance_Centers(k,i)< m
N(k,i)=1 ; % 1 means connection, 0 means no connection
dist(k,i) = Distance_Centers(k,i) ; % distance between the axons
end
end
end
end
for j=1:length(N)
for i=1:length(N)
if N(j,i)==1
N(i,j)=0 ;
dist(i,j) = 0 ;
end
end
end
%% Next part: Find the sections that will be connected to each other
Total_Length_Axon = 46500 ; % the total length of axons is "about" ... (microns)
%%%%%%%%%%%%%%%%%%%%% the other type of axons %%%%%%%%%%%%%%%%%%%%%%%%%%%%
for J=1: length(unique_radius)
z1=0 ;
z2=0 ;
z3=0 ;
z4=0 ;
j=1 ;
i=2 ;
which_radius = J ;
%
eval(['coordinates_sections_' num2str(J) '(1,1) = 0 ;']);
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+nodelength;']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = 0.5;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("node_%d",z1) ;']);
z1=z1+1 ;
i=i+1 ;
j=j+1 ;
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+paralength1;']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("MYSA_%d",z2) ;']);
z2=z2+1 ;
i=i+1 ;
j=j+1 ;
for rr=1:paralength2_segments
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+parameters(which_radius,4);']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("FLUT_%d",z3) ;']);
z3=z3+1 ;
i=i+1 ;
j=j+1 ;
end
for r=1:internode_segments
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+(parameters(which_radius,6));']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("STIN_%d",z4) ;']);
z4=z4+1 ;
i=i+1 ;
j=j+1 ;
end
for rr=1:paralength2_segments
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+parameters(which_radius,4);']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("FLUT_%d",z3) ;']);
z3=z3+1 ;
i=i+1 ;
j=j+1 ;
end
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+paralength1;']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("MYSA_%d",z2) ;']);
z2=z2+1 ;
i=i+1 ;
j=j+1 ;
Length_one_set(J,1) = eval(['coordinates_sections_' num2str(J) '(1,i-1)']) ; % Length of one set including (node+MYSA+5*FLUT+40*Internodes+5*FLUT+MYSA)
number_sets(J,1) = round (Total_Length_Axon/Length_one_set(J,1)) ; % how many sets we need for this specific radius in order to get a total length about 10000 microns
%
for ii=2:number_sets(J,1) % it starts from 2 becasue the first set is already above
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+nodelength;']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("node_%d",z1) ;']);
z1=z1+1 ;
i=i+1 ;
j=j+1 ;
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+paralength1;']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("MYSA_%d",z2) ;']);
z2=z2+1 ;
i=i+1 ;
j=j+1 ;
for rr=1:paralength2_segments
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+parameters(which_radius,4);']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("FLUT_%d",z3) ;']);
z3=z3+1 ;
i=i+1 ;
j=j+1 ;
end
for r=1:internode_segments
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+(parameters(which_radius,6));']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("STIN_%d",z4) ;']);
z4=z4+1 ;
i=i+1 ;
j=j+1 ;
end
for rr=1:paralength2_segments
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+parameters(which_radius,4);']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("FLUT_%d",z3) ;']);
z3=z3+1 ;
i=i+1 ;
j=j+1 ;
end
eval(['coordinates_sections_' num2str(J) '(1,i) = coordinates_sections_' num2str(J) '(1,i-1)+paralength1;']);
eval(['mid_coordinates_sections_' num2str(J) '(1,j) = (coordinates_sections_' num2str(J) '(1,i) + coordinates_sections_' num2str(J) '(1,i-1) )/2;']);
eval(['name_sections_' num2str(J) '(1,j) = sprintf("MYSA_%d",z2) ;']);
z2=z2+1 ;
i=i+1 ;
j=j+1 ;
end
Length_All_sets(J,1) = eval(['coordinates_sections_' num2str(J) '(1,i-1)']) ; % "around" 10000 microns
Number_of_nodes (J,1) = z1 ;
end
%%
% a = 0 ;
% b= 208 ;
%
% rrr = (b-a).*rand(20,1) + a ;
rrr = [67.4678402083890 ;
330.422368924505;
129.465457490639;
219.869784370584;
68.9098714719089;
250.424487623081;
109.396054368700;
272.096904966341;
286.713233306243;
311.231062614663;
187.425304977039;
34.8696932467240;
95.2544189861966;
379.948342384695;
63.3892558911968;
343.539862635652;
223.950453068184;
414.392042116784;
32.5210199613244;
184.154160226586] ;
%%
coordinates_sections_2 = coordinates_sections_2 - rrr(1,1) ;
mid_coordinates_sections_2 = mid_coordinates_sections_2 - rrr(1,1) ;
coordinates_sections_3 = coordinates_sections_3 - rrr(2,1) ;
mid_coordinates_sections_3 = mid_coordinates_sections_3 - rrr(2,1) ;
coordinates_sections_4 = coordinates_sections_4 - rrr(3,1) ;
mid_coordinates_sections_4 = mid_coordinates_sections_4 - rrr(3,1) ;
coordinates_sections_5 = coordinates_sections_5 - rrr(4,1) ;
mid_coordinates_sections_5 = mid_coordinates_sections_5 - rrr(4,1) ;
coordinates_sections_6 = coordinates_sections_6 - rrr(5,1) ;
mid_coordinates_sections_6 = mid_coordinates_sections_6 - rrr(5,1) ;
coordinates_sections_7 = coordinates_sections_7 - rrr(6,1) ;
mid_coordinates_sections_7 = mid_coordinates_sections_7 - rrr(6,1) ;
coordinates_sections_8 = coordinates_sections_8 - rrr(7,1) ;
mid_coordinates_sections_8 = mid_coordinates_sections_8 - rrr(7,1) ;
coordinates_sections_9 = coordinates_sections_9 - rrr(8,1) ;
mid_coordinates_sections_9 = mid_coordinates_sections_9 - rrr(8,1) ;
coordinates_sections_10 = coordinates_sections_10 - rrr(9,1) ;
mid_coordinates_sections_10 = mid_coordinates_sections_10 - rrr(9,1) ;
coordinates_sections_11 = coordinates_sections_11 - rrr(10,1) ;
mid_coordinates_sections_11 = mid_coordinates_sections_11 - rrr(10,1) ;
coordinates_sections_12 = coordinates_sections_12 - rrr(11,1) ;
mid_coordinates_sections_12 = mid_coordinates_sections_12 - rrr(11,1) ;
coordinates_sections_13 = coordinates_sections_13 - rrr(12,1) ;
mid_coordinates_sections_13 = mid_coordinates_sections_13 - rrr(12,1) ;
coordinates_sections_14 = coordinates_sections_14 - rrr(13,1) ;
mid_coordinates_sections_14 = mid_coordinates_sections_14 - rrr(13,1) ;
coordinates_sections_15 = coordinates_sections_15 - rrr(14,1) ;
mid_coordinates_sections_15 = mid_coordinates_sections_15 - rrr(14,1) ;
coordinates_sections_16 = coordinates_sections_16 - rrr(15,1) ;
mid_coordinates_sections_16 = mid_coordinates_sections_16 - rrr(15,1) ;
coordinates_sections_17 = coordinates_sections_17 - rrr(16,1) ;
mid_coordinates_sections_17 = mid_coordinates_sections_17 - rrr(16,1) ;
coordinates_sections_18 = coordinates_sections_18 - rrr(17,1) ;
mid_coordinates_sections_18 = mid_coordinates_sections_18 - rrr(17,1) ;
coordinates_sections_19 = coordinates_sections_19 - rrr(18,1) ;
mid_coordinates_sections_19 = mid_coordinates_sections_19 - rrr(18,1) ;
coordinates_sections_20 = coordinates_sections_20 - rrr(19,1) ;
mid_coordinates_sections_20 = mid_coordinates_sections_20 - rrr(19,1) ;
%%
%
figure
plot(coordinates_sections_3 ,1 , 'ob')
hold on
plot(coordinates_sections_6 ,1.05 , 'or')
ylim([1 1.5])
%xlim([0 10000])
%%
compartments_in_a_set = 3 + internode_segments + (2*paralength2_segments) ;
%%
%%%%%%%%%% Connect other axons (excluding C fibers) to axons %%%%%%%%%%%%%
for s=1 :length((unique_radius))
for t=1 :length((unique_radius))
if t> s
clear S_first
clear S_second
clear n nn f1 f2 fz m mm
L1=0 ;
L2=0 ;
p=1 ;
if eval(['coordinates_sections_' num2str(t) '(1,1)']) == eval(['coordinates_sections_' num2str(s) '(1,1)'])
S_first(p,1) = sprintf("node_%d",L1) ;
S_second(p,1) = sprintf("node_%d",L2) ;
p=p+1 ;
L1=L1+1 ;
L2=L2+1 ;
end
for n2=((L2*compartments_in_a_set)+1):compartments_in_a_set:compartments_in_a_set*Number_of_nodes(t,1)
f2 = eval(['mid_coordinates_sections_' num2str(t) '(1,n2)']) ;
for n1=((L1*compartments_in_a_set)+1):compartments_in_a_set:compartments_in_a_set*Number_of_nodes(s,1)
f1 = eval(['mid_coordinates_sections_' num2str(s) '(1,n1)']) ;
if f1 < f2
for m=1:length (eval(['coordinates_sections_' num2str(t)]))-1
if f1 >= eval(['coordinates_sections_' num2str(t) '(1,m)']) && f1 <= eval(['coordinates_sections_' num2str(t) '(1,m+1)'])
S_first(p,1) = sprintf("node_%d",L1) ;
S_second(p,1) = eval(['name_sections_' num2str(t) '(1,m)']) ;
p=p+1 ;
end
end
L1=L1+1 ;
end
end
for mm=1:length (eval(['coordinates_sections_' num2str(s)]))-1
if f2 >= eval(['coordinates_sections_' num2str(s) '(1,mm)']) && f2 <= eval(['coordinates_sections_' num2str(s) '(1,mm+1)'])
S_first(p,1) = eval(['name_sections_' num2str(s) '(1,mm)']) ;
S_second(p,1) = sprintf("node_%d",L2) ;
p=p+1 ;
end
end
L2=L2+1 ;
end
if Number_of_nodes(s,1) > Number_of_nodes(t,1)
for nn=((L1*compartments_in_a_set)+1):compartments_in_a_set: compartments_in_a_set*Number_of_nodes(s,1)
fz=eval(['mid_coordinates_sections_' num2str(s) '(1,nn)']) ;
for m=1:length (eval(['coordinates_sections_' num2str(t)]))-1
if fz >= eval(['coordinates_sections_' num2str(t) '(1,m)']) && fz <= eval(['coordinates_sections_' num2str(t) '(1,m+1)'])
S_first(p,1) = sprintf("node_%d",L1) ;
S_second(p,1) = eval(['name_sections_' num2str(t) '(1,m)']) ;
p=p+1 ;
end
end
L1=L1+1 ;
end
end
eval(['Connect_types_' num2str(s),num2str(t) '(1:length(S_first),1) = S_first ;']);
eval(['Connect_types_' num2str(s),num2str(t) '(length(S_first)+1:length(S_first)+length(S_second),1) = S_second ;']);
%%%%%%%%%% save data %%%%%%%%%%
clear SAVE
SAVE = char( eval(['Connect_types_' num2str(s),num2str(t)])) ;
filename=['Connect_types_' num2str(s),num2str(t) '.mat'];
save(filename , 'SAVE');
end
end
end
%%
%%%%%%%%%%%%%%%%%%%%% Connect each axon to itself %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for s=1 :length((unique_radius))
for t=1 :length((unique_radius))
if t== s
clear S_first
clear S_second
clear n nn f1 f2 fz m mm
L1=0 ;
L2=0 ;
p=1 ;
for n=0:Number_of_nodes(s,1)-1
S_first(p,1) = sprintf("node_%d",n) ;
S_second(p,1) = sprintf("node_%d",n) ;
p=p+1 ;
end
eval(['Connect_types_' num2str(s),num2str(t) '(1:length(S_first),1) = S_first ;']);
eval(['Connect_types_' num2str(s),num2str(t) '(length(S_first)+1:length(S_first)+length(S_second),1) = S_second ;']);
%%%%%%%%%% save data %%%%%%%%%%
clear SAVE
SAVE = char( eval(['Connect_types_' num2str(s),num2str(t)])) ;
filename=['Connect_types_' num2str(s),num2str(t) '.mat'];
save(filename , 'SAVE');
end
end
end
%% Output
% *** Connection_types_xx are saved above
save('R.txt' , 'R' , '-ascii') % save radius of all axons
save('C.txt' , 'C' , '-ascii') % save location of all axons (x,y)
save('G.txt' , 'G' , '-ascii') % save Groups of all axons. i.e., each radius in "R" belongs to which type (Group1,2,3 or 4)
% To be used in NEURON to build models
save('parameters.txt' , 'parameters' , '-ascii')
save('Number_of_nodes.txt' , 'Number_of_nodes' , '-ascii')
save('unique_radius.txt' , 'unique_radius' , '-ascii')
save('neighboringAxon.txt' , 'N' , '-ascii')
% % save('neighbouringAxon.mat','N')
save('dist.txt' , 'dist' , '-ascii')
%
save('Distance_edge.txt' , 'Distance_edge' , '-ascii')
%% Next: Boundary Condition
% Boundary_coordinates = zeros(20,2) ;
%
%
% %%
% x=1 ;
% for i=1:20
%
% Boundary_coordinates(i,2) = 40 ;
% Boundary_coordinates(i,1) = x ;
% x=x+1 ;
%
% end
%
%
% %%
% v = ones(1,20) ;
% Boundary_neighboring = diag(v) ;
%
% Boundary_dist = Boundary_neighboring ;
%
% Boundary_dist(0+1,0+1) = Boundary_neighboring(0+1,0+1)*(8+8+0.1+0.1) ;
%
%
% Boundary_dist(1+1,1+1) = Boundary_neighboring(1+1,1+1)*(8+0.1) ;
% Boundary_dist(2+1,2+1) = Boundary_neighboring(2+1,2+1)*(8+0.1) ;
% Boundary_dist(3+1,3+1) = Boundary_neighboring(3+1,3+1)*(8+0.1) ;
% Boundary_dist(9+1,9+1) = Boundary_neighboring(9+1,9+1)*(8+0.1) ;
% Boundary_dist(11+1,11+1) = Boundary_neighboring(11+1,11+1)*(8+0.1) ;
% Boundary_dist(12+1,12+1) = Boundary_neighboring(12+1,12+1)*(8+0.1) ;
%%
%
% % finding the neurons on the edge, for now I do it manually
%
% neurons_on_edge_up = [10 8 4 ] ;
% neurons_on_edge_right = [7 5 6 13 ] ;
% neurons_on_edge_down = [14 18 17 ] ;
% neurons_on_edge_left = [19 15 16 ] ;
%
% neurons_on_edge = [neurons_on_edge_up, neurons_on_edge_right, neurons_on_edge_down, neurons_on_edge_left] ;
%
% % finding the boundary cable coordinates
% distance_boundary_to_neuron = 7 ; % microns
%
% for i=1:length(neurons_on_edge_up)
% B_coordinates_up(i,1) = C(neurons_on_edge_up(i)+1,1) ;
% B_coordinates_up(i,2) = C(neurons_on_edge_up(i)+1,2) + distance_boundary_to_neuron ;
% end
%
% for i=1:length(neurons_on_edge_right)
% B_coordinates_right(i,1) = C(neurons_on_edge_right(i)+1,1) + distance_boundary_to_neuron ;
% B_coordinates_right(i,2) = C(neurons_on_edge_right(i)+1,2) ;
% end
%
% for i=1:length(neurons_on_edge_down)
% B_coordinates_down(i,1) = C(neurons_on_edge_down(i)+1,1) ;
% B_coordinates_down(i,2) = C(neurons_on_edge_down(i)+1,2) - distance_boundary_to_neuron ;
% end
%
% for i=1:length(neurons_on_edge_left)
% B_coordinates_left(i,1) = C(neurons_on_edge_left(i)+1,1) - distance_boundary_to_neuron ;
% B_coordinates_left(i,2) = C(neurons_on_edge_left(i)+1,2) ;
% end
%
% Boundary_coordinates(:,1) = [B_coordinates_up(:,1)', B_coordinates_right(:,1)', B_coordinates_down(:,1)', B_coordinates_left(:,1)'] ;
% Boundary_coordinates(:,2) = [B_coordinates_up(:,2)', B_coordinates_right(:,2)', B_coordinates_down(:,2)', B_coordinates_left(:,2)'] ;
% Boundary_coordinates ;
%
% plot(Boundary_coordinates(:,1),Boundary_coordinates(:,2),'o','Color',[0.9290 0.6940 0.1250])
%
% %% finding the neighboring neurons to which boundary cables are connected
%
% C_and_Boundary_coordinates(:,1) = [ C(:,1)' , Boundary_coordinates(:,1)' ] ;
% C_and_Boundary_coordinates(:,2) = [ C(:,2)' , Boundary_coordinates(:,2)' ] ;
%
% size_C = size(C) ;
% size_Boundary_coordinates = size(Boundary_coordinates) ;
%
% Boundary_N=zeros(length(C_and_Boundary_coordinates),length(C_and_Boundary_coordinates)) ;
% Boundary_dist=zeros(length(C_and_Boundary_coordinates),length(C_and_Boundary_coordinates)) ;
% Boundary_Distance = pdist2(C_and_Boundary_coordinates, C_and_Boundary_coordinates) ;
%
%
%
% m= 8 ; % minimum distance (threshold)
%
%
%
%
% for k=1:length(Boundary_Distance)
% for i=1:length(Boundary_Distance)
% if k ~= i
% if Boundary_Distance(k,i)< m
% Boundary_N(k,i)=1 ;
% Boundary_dist(k,i)=Boundary_Distance(k,i) ;
%
% end
% end
% end
%
% end
%
% Boundary_neighboring = Boundary_N(size_C+1:size(Boundary_N) ,1:size_C) ;
% Boundary_neighboring = Boundary_neighboring.*0 ;
%
% Boundary_neighboring(1,10+1) = 1 ;
% Boundary_neighboring(2,8+1) = 1 ;
% Boundary_neighboring(3,4+1) = 1 ;
% Boundary_neighboring(4,7+1) = 1 ;
% Boundary_neighboring(5,5+1) = 1 ;
% Boundary_neighboring(6,6+1) = 1 ;
% Boundary_neighboring(7,13+1) = 1 ;
% Boundary_neighboring(8,14+1) = 1 ;
% Boundary_neighboring(9,18+1) = 1 ;
% Boundary_neighboring(10,17+1) = 1 ;
% Boundary_neighboring(11,19+1) = 1 ;
% Boundary_neighboring(12,15+1) = 1 ;
% Boundary_neighboring(13,16+1) = 1 ;
%
%
% Boundary_dist = Boundary_dist(size_C+1:size(Boundary_N) ,1:size_C) ;
% Boundary_dist = Boundary_neighboring.*7 ;
%
%
%
%
%%
%%
% Boundary_neighboring(2,13)= 0 ;
% Boundary_dist(2,13) = 0 ;
%
% Boundary_neighboring(7,16)= 0 ;
% Boundary_dist(7,16) = 0 ;
%% find the sections for connections
% for Boundary Cable
z=0 ;
i=2 ;
j=1 ;
number_of_sections = 1000 ;
sectionlength= Total_Length_Axon/number_of_sections ;
for ii=0:number_of_sections-1
Boundary_coordinates_sections(1,1) = 0 ;
Boundary_coordinates_sections(1,i) = Boundary_coordinates_sections(1,i-1) + sectionlength ;
Boundary_mid_coordinates_section(1,j) = (Boundary_coordinates_sections(1,i) + Boundary_coordinates_sections(1,i-1) )/2 ;
Boundary_name_sections(1,j) = sprintf("section_%d",z) ;
i=i+1 ;
z=z+1 ;
j=j+1 ;
end
for t=1 :length((unique_radius))
clear S_first
clear S_second
clear n f1 m
L=0 ;
p=1 ;
for n=1:compartments_in_a_set:compartments_in_a_set*Number_of_nodes(t,1)
f1 = eval(['mid_coordinates_sections_' num2str(t) '(1,n)']) ;
for m=1:length (Boundary_coordinates_sections)-1
if f1 >= Boundary_coordinates_sections(1,m) && f1 <= Boundary_coordinates_sections(1,m+1)
S_first(p,1) = sprintf("node_%d",L) ;
S_second(p,1) = Boundary_name_sections(1,m) ;
p=p+1 ;
end
end
L=L+1 ;
end
eval(['Boundary_to_' num2str(t) '(1:length(S_first),1) = S_first ;']);
eval(['Boundary_to_' num2str(t) '(length(S_first)+1:length(S_first)+length(S_second),1) = S_second ;']);
%%%%%%%%%% save data %%%%%%%%%%
clear SAVE
SAVE = char( eval(['Boundary_to_' num2str(t)])) ;
filename=['Boundary_to_' num2str(t) '.mat'];
save(filename , 'SAVE');
end
%%% Boundary to Boundary are not connected
%% Output
% *** Boundary_to_x are saved above
save('Boundary_coordinates.txt' , 'Boundary_coordinates' , '-ascii') % (x,y)
save('Boundary_neighboring.txt' , 'Boundary_neighboring' , '-ascii')
save('Boundary_dist.txt' , 'Boundary_dist' , '-ascii')
%% Next step: Transverse resistances (g) unit: S/cm2
% Calculating c in PLOS computational biology paper, Equation 9
for i=1 :length((unique_radius))
for j=1 :length((unique_radius))
if j>i
clear Rg
z=2 ;
L = length(eval(['Connect_types_' num2str(i),num2str(j)]))/2 ;
Rg=zeros(L,1) ;
for Z=2:L-1
CO1 = find( eval(['name_sections_' num2str(i)]) == eval(['Connect_types_' num2str(i),num2str(j) '(Z-1,1)']) );
CO2 = find( eval(['name_sections_' num2str(i)]) == eval(['Connect_types_' num2str(i),num2str(j) '(Z+1,1)']));
g1 = eval(['mid_coordinates_sections_' num2str(i) '(1,CO1)']) ;
g2 = eval(['mid_coordinates_sections_' num2str(i) '(1,CO2)']) ;
Rg(z,1) = (g2-g1)/2 ;
z=z+1 ;
if g2 < g1
msg = 'Error occurred. g2<g1';
error(msg)
end
end
CO1 = find( eval(['name_sections_' num2str(i)]) == eval(['Connect_types_' num2str(i),num2str(j) '(1,1)']) );
CO2 = find( eval(['name_sections_' num2str(i)]) == eval(['Connect_types_' num2str(i),num2str(j) '(2,1)']));
g1 = eval(['mid_coordinates_sections_' num2str(i) '(1,CO1)']) ;
g2 = eval(['mid_coordinates_sections_' num2str(i) '(1,CO2)']) ;
Rg(1,1) = (g2-g1)/2 ;
CO1 = find( eval(['name_sections_' num2str(i)]) == eval(['Connect_types_' num2str(i),num2str(j) '(L-1,1)']) );
CO2 = find( eval(['name_sections_' num2str(i)]) == eval(['Connect_types_' num2str(i),num2str(j) '(L,1)']));
g1 = eval(['mid_coordinates_sections_' num2str(i) '(1,CO1)']) ;
g2 = eval(['mid_coordinates_sections_' num2str(i) '(1,CO2)']) ;
Rg(L,1) = (g2-g1)/2 ;
%%%%%%%%%% save data %%%%%%%%%%
filename=['Rg_' num2str(i),num2str(j) '.txt'];
save(filename , 'Rg' , '-ascii')
end
end
end
%%
%%% Areas of the nodes
for i=1 :length((unique_radius))
for j=2 :length((unique_radius))
if j>i
clear Areas
k=0 ;
L = length(eval(['Connect_types_' num2str(i),num2str(j)]))/2 ;
Areas =zeros(L,1) ;
for Z=1:L
if (eval(['Connect_types_' num2str(i),num2str(j) '(Z,1)']) == sprintf("node_%d",k) )
Areas(Z,1) = pi*parameters(i,2) ; % surface area = pi*d*L (L=1), parameters(i,2): nodeD
k=k+1 ;
else
Areas(Z,1) = pi*parameters(j,2) ; % surface area = pi*d*L (L=1), parameters(i,2): nodeD
end
end
%%%%%%%%%% save data %%%%%%%%%%
filename=['Areas_' num2str(i),num2str(j) '.txt'];
save(filename , 'Areas' , '-ascii')
end
end
end
%% between boundary cables and the axons (C fibers and other axons) %%%%%
for j=1:length((unique_radius))
clear Rg
z=2 ;
L = length(eval(['Boundary_to_' num2str(j)]))/2 ;
Rg=zeros(L,1) ;
for Z=2:L-1
CO1 = find( eval(['name_sections_' num2str(j)]) == eval(['Boundary_to_' num2str(j) '(Z-1,1)']) );
CO2 = find( eval(['name_sections_' num2str(j)]) == eval(['Boundary_to_' num2str(j) '(Z+1,1)']) );
g1 = eval(['mid_coordinates_sections_' num2str(j) '(1,CO1)']) ;
g2 = eval(['mid_coordinates_sections_' num2str(j) '(1,CO2)']) ;
Rg(z,1) = (g2-g1)/2 ;
z=z+1 ;
end
CO1 = find( eval(['name_sections_' num2str(j)]) == eval(['Boundary_to_' num2str(j) '(1,1)']) );
CO2 = find( eval(['name_sections_' num2str(j)]) == eval(['Boundary_to_' num2str(j) '(2,1)']));
g1 = eval(['mid_coordinates_sections_' num2str(j) '(1,CO1)']) ;
g2 = eval(['mid_coordinates_sections_' num2str(j) '(1,CO2)']) ;
Rg(1,1) = (g2-g1)/2 ;
CO1 = find( eval(['name_sections_' num2str(j)]) == eval(['Boundary_to_' num2str(j) '(L-1,1)']) );
CO2 = find( eval(['name_sections_' num2str(j)]) == eval(['Boundary_to_' num2str(j) '(L,1)']));
g1 = eval(['mid_coordinates_sections_' num2str(j) '(1,CO1)']) ;
g2 = eval(['mid_coordinates_sections_' num2str(j) '(1,CO2)']) ;
Rg(L,1) = (g2-g1)/2 ;
%%%%%%%%%% save data %%%%%%%%%%
filename=['Boundary_Rg_' num2str(j) '.txt'];
save(filename , 'Rg' , '-ascii')
end
%%
%%
%
% figure
%
% for i=1:length(C)
%
% plot(C(i,1),C(i,2),'ob')
% hold on
%
%
% end
%
% for i=1:length(Boundary_coordinates)
%
% plot(Boundary_coordinates(i,1),Boundary_coordinates(i,2),'or')
% hold on
%
%
% end