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main_currentspread_6_11_2022.m
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main_currentspread_6_11_2022.m
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% code for Yucel et al
%
% to replicate results for initial submission select:
% flag.res = highres;
% flag.dip = perceptual;
% flag.rd = 'threshold'
%
% but better parameters are probably
% flag.res = 'lowres';
% flag.dip = 'perceptual';
% flag.rd = 'scale'
%% plot amplitude thresholds
figure(1); clf
prctile_out = cs.plot_amp_thresholds([15 50 85]); % get percentile range for amplitude thresholds
% fitting params
fitParams.nreps = 12; % shouldn't be below 10
fitParams.tol = 0.1; fitParams.lo = 0; fitParams.hi = 5000; fitParams.thr = 1;
%% begin simulations, basic parameters
safety_lim = 660;
flag.res = 'lowres'; % running the code with coarse or fine parameter sampling. Warning! highres is very slow!
flag.dip = 'perceptual'; % default is assuming that electric current below threshold has no perceptual effect, alternative is 'electrical'
flag.rd = 'scale'; % 'threshold', RD just changes the threshold, or scales all current linearly
savestr = ['6_12_2022', '_', flag.res];
%% plot, current spreads for 2 electrodes at different heights
fitParams.nreps = 20;
clear ret; ret = cs.setdefaultparams(flag);
ret.ss = 25;
ret.a = 1.5; ret.k = 15;
ret.t_lift = 0;
ret.x = [-700 700];
ret.y = [0 0];
ret_saved = ret;
plt.pos = [ 42 1088 939 231];
z_list = [-150 -750];
for zz = 1:2 % simulating pairs of electrodes, at two different heights
ret.z = z_list(zz);
figure(zz+1);clf
ret = cs.calc_dist_from_electrode(ret);
if isfield(ret, "loc")
ret = rmfield(ret, "loc");
end
ret = cs.fit_currentspreadfast(ret, fitParams);
% now double it and calculate what the current spread looks like
ret.eI = ret.eI * 2;
ret = cs.create_currentspread(ret);
ret = cs.calculate_dip(ret, flag);
cs.create_currentspreadfig(ret); set(gcf, 'Position', plt.pos);
set(gcf, 'Name', ['height = ', num2str(ret.z)]); drawnow
end
%% calculate thresholds as a function of height, for all the possible retinal damages, single electrode
ret = cs.setdefaultparams(flag);
ret.x = 0; ret.y = 0; % location of the electrode
ct = 1;
for z = 1:length(ret.z_range)
disp(['fitting z = ', num2str(z), ' out of ', num2str(length(ret.z_range))]);
ret.z = -ret.z_range(z);
ret = cs.calc_dist_from_electrode(ret);
for r = 1:length(ret.rd_range)
ret.t_ret = ret.rd_range(r) * ret.t_ret_min;
for a = 1:length(ret.a_range)
ret.a = ret.a_range(a);
for ks = 1:length(ret.k_range)
ret.k = ret.k_range(ks);
% find the threshold for the z
ret = cs.fit_currentspreadfast(ret, fitParams);
if ret.eI <= safety_lim % if this threshold below the safety limit
ret_single.eI(ct) = ret.eI;
ret_single.a(ct) = ret.a_range(a); ret_single.k(ct) =ret.k_range(ks);
ret_single.z(ct) = ret.z_range(z);
ret_single.Th_RD(ct) =ret.rd_range(r); % how much retinal damage raises threshold
ret_single.t_ret(ct) = ret.t_ret;
if z > 1
ind = find(ret_single.a ==ret.a_range(a) & ret_single.k==ret.k_range(ks) & ...
ret_single.Th_RD==ret.rd_range(1) & ret_single.z==ret.z_range(1));
ret_single.Th_Z(ct) = ret_single.eI(ct)./(ret_single.eI(ind) * ret_single.Th_RD(ct)); % calculate how much lift is raising threshold
end
ct = ct + 1;
end; end; end; end; end
disp([num2str(ct-1), ' parameterizations have thresholds below the safety limit']);
%% plot thresholds as a function of height, for a variety of retinal damage, single electrode
figure(1); clf
for a = 1:length(ret.a_range)
for ks = 1:length(ret.k_range)
for r = 1:length(ret.rd_range)
ind = find(ret_single.a ==ret.a_range(a) & ret_single.k==ret.k_range(ks) & ...
ret_single.Th_RD==ret.rd_range(1));
subplot(1,2,1)
plot(ret_single.z(ind), ret_single.eI(ind), 'k'); hold on
xlabel('z'); ylabel('threshold')
set(gca, 'XLim', [0 1000]); set(gca, 'XTick', [0:200:1000])
set(gca, 'YLim', [0 700]);set(gca, 'YTick',[0:100:700])
subplot(1,2,2)
plot(log(ret_single.z(ind)), log(ret_single.eI(ind)) , 'k'); hold on
xlabel('z'); ylabel('threshold non damaged retina')
set(gca, 'XTick', log([ 100 1000])); set(gca, 'XLim', log([50 2000])); set(gca, 'XTickLabel',[100 1000]); logx2raw
set(gca, 'YTick', log([10 100 1000])); set(gca, 'YLim', log([9 2000])); set(gca, 'YTickLabel',[10 100 1000]); logy2raw
end
end
end
save([savestr, '_single'], '-v7.3');
%% simulate a range of parameter values
clear ret; ret = cs.setdefaultparams(flag);
ret.y = [0 0];
ct = 1;
for d = 1:length(ret.d_range)
disp(['fitting d = ', num2str(d), ' out of ', num2str(length(ret.d_range))]);
ret.x = [-round(ret.d_range(d)/2) round(ret.d_range(d)/2)];
for s = 1:length(ret_single.eI) % for each parameterization below safety limits
ret.z = -ret_single.z(s);
ret.t_ret = ret_single.t_ret(s);
ret.k = ret_single.k(s);
ret.a = ret_single.a(s);
ret.eI = ret_single.eI(s) * 2;
if ret.eI>safety_lim
ret.eI = safety_lim;
end
ret = cs.calc_dist_from_electrode(ret);
ret = cs.create_currentspread(ret);
ret = cs.calculate_dip(ret, flag);
ret_pair.eI(ct) = ret_single.eI(s);
ret_pair.a(ct) = ret_single.a(s);
ret_pair.k(ct) = ret_single.k(s);
ret_pair.z(ct) = ret_single.z(s);
ret_pair.Th_RD(ct) = ret_single.Th_RD(s);
ret_pair.Th_Z(ct) = ret_single.Th_Z(s);
ret_pair.ret.t_ret(ct) = ret_single.t_ret(s);
ret_pair.dist(ct) = ret.d_range(d);
ret_pair.I_max(ct)= ret.I_max;
ret_pair.I_mid(ct) = ret.I_mid;
ret_pair.dip(ct) = ret.dip;
ct = ct+1;
end
end
save([savestr, '_pair'], '-v7.3');
return
%% create isodipcurve
% find data close to the predicted regression line
p = 1.0e+02 * [0.003063041388051 -3.849023842682739];
amp_pred = polyval(p, ret.d_range);
amp_max = 499; %280; 177; %;
amp_min = 177; %177;%177; %
% find the distances corresponding to each possible dip criterion, and the
% expected amplitude based on the regression model, for that distance
clear keepSim;
err_Thr = 25;
ct = 1;
for ks = 1:length(ret.k_range)
disp(['ks ', num2str(ks), ' out of ', num2str(length(ret.k_range))]);
for a = 1:length(ret.a_range)
disp(['a ', num2str(a), ' out of ', num2str(length(ret.a_range))]);
for r = 1:length(ret.rd_range)
clear M;
ind = find(ret_pair.eI>amp_min & ret_pair.eI<amp_max & ret_pair.a == ret.a_range(a) & ret_pair.k == ret.k_range(ks) & ret_pair.Th_RD == ret.rd_range(r));
dip_min = max([ceil(min(ret_pair.dip(ind))), 10]);
dip_max = min([floor(max(ret_pair.dip(ind))), 90]);
% for a given dip'
Th_Z_unique = unique(ret_pair.Th_Z(ind));
dist_unique = unique(ret_pair.dist(ind));
dip_vals = ret_pair.dip(ind);
Th_Z_vals = ret_pair.Th_Z(ind);
z_vals = ret_pair.z(ind);
Th_RD_vals = ret_pair.Th_RD(ind);
eI_vals = ret_pair.eI(ind);
dist_vals = ret_pair.dist(ind);
% calculate the matrix of dip as a function of inter-electrode
% distance and Z. This matrix doesn't vary with RD
if length(Th_Z_unique)>2 & length(dist_unique)>2
for i = 1:length(Th_Z_unique)
for j = 1:length(dist_unique)
gval = find(Th_Z_vals == Th_Z_unique(i) & dist_vals == dist_unique(j) & Th_RD_vals == ret.rd_range(r));
M(j, i) = nanmean(dip_vals(gval));
end; end
[c,h] =contour(Th_Z_unique,dist_unique, M, [dip_min:dip_max]);
[~, ia] = unique(Th_Z_vals(1:length(Th_Z_unique)));
tmp_Z = Th_Z_vals(1:length(Th_Z_unique));
tmp_eI = eI_vals(1:length(Th_Z_unique)); tmp_z = z_vals(1:length(Th_Z_unique));
for dd = dip_min:dip_max
[Th_Z_c, dist_c] = cs.unwrap_contour(c, dd); % the Z values as a function of separation for a single iso curve
eI_c = interp1(tmp_Z(ia), tmp_eI(ia), Th_Z_c);
z_c = interp1(tmp_Z(ia), tmp_z(ia), Th_Z_c);
g_eI = find(eI_c>amp_min & eI_c<amp_max);
pred_eI_c = polyval(p, dist_c(g_eI));
err = sqrt(mean((eI_c(g_eI)-pred_eI_c).^2));
if err<err_Thr
keepSim(ct).eI = eI_c(g_eI);
keepSim(ct).dist = dist_c(g_eI);
keepSim(ct).Th_Z = Th_Z_c(g_eI);
keepSim(ct).z = z_c(g_eI);
keepSim(ct).Th_RD = ones(size(Th_Z_c(g_eI))).*ret.rd_range(r);
keepSim(ct).err = err;
keepSim(ct).a = ret.a_range(a);
keepSim(ct).k = ret.k_range(ks);
keepSim(ct).dip = dd;
[~, si] = sort(keepSim(ct).dist); % probably unnecessary
% patchline( keepSim(ct).dist(si), keepSim(ct).eI(si), ...
% 'EdgeColor',[.3 .3 .3], 'LineWidth',2,'EdgeAlpha',0.2); drawnow
ct = ct+1;
end; end; end; end; end; end
save([savestr, '_keepSim'], '-v7.3');
%% now plot it all
figure(4); clf; hold on
plot(ret.d_range(2:end),amp_pred(2:end) , 'g-', 'LineWidth', 2); hold on
for ct = 1:length(keepSim)
disp(['ct ', num2str(ct), ' out of ', num2str(length(keepSim))]);
s = scatter(keepSim(ct).dist,keepSim(ct).eI, ...
'MarkerFaceColor',[.3 .3 .3], 'MarkerFaceAlpha',0.2, 'MarkerEdgeAlpha', 0); drawnow
drawnow
end
set(gca, 'XLim', [1500 4000])
xlabel('Physical Distance ');
ylabel('Amplitude');
%% Z vs. RD for successful simulations
figure(5); clf
for ks = 1:length(keepSim)
p = scatter([keepSim(ks).Th_Z], [keepSim(ks).Th_RD]+.03*randn(size(keepSim(ks).Th_RD)), ...
'ko', 'MarkerFaceColor', 'k', 'MarkerEdgeColor', 'none', 'MarkerFaceAlpha', .5); hold on
drawnow;
end
xlabel('Lift values');
ylabel('RD values');
axis equal
set(gca, 'XLim', [.9 9])
set(gca, 'YLim', [.9 5.5])
%% now calculate what would have happened without lift, RD or either
%5 Figure 10 Panel D
clear ret; ret = cs.setdefaultparams(flag);
for ks = 1:length(keepSim)
ret.a = keepSim(ks).a;
ret.k = keepSim(ks).k;
ret.y = [0 0];
for ps = 1:length(keepSim(ks).eI)
ret.eI = keepSim(ks).eI(ps) * 2;
if ret.eI>safety_lim
ret.eI = safety_lim;
end
% no RD
ret.z = -keepSim(ks).z(ps);
ret.t_ret = ret.t_ret_min;
lo = 1; hi = 3000;
for i = 1:10
dist = (hi+lo)/2;
ret.x = [-round(dist/2) round(dist/2)];
ret = cs.calc_dist_from_electrode(ret);
ret = cs.create_currentspread(ret);
ret = cs.calculate_dip(ret, flag);
if ret.dip > keepSim(ks).dip
hi = dist;
else
lo = dist;
end
end
keepSim(ks).no_RD_dist(ps) = (hi+lo)/2;
keepSim(ks).no_RD_dip(ps) = ret.dip;
% no Z
ret.z = 0;
ret.t_ret = ret.t_ret_min * keepSim(ks).Th_RD(ps);
lo = 1; hi = 3000;
for i = 1:10
dist = (hi+lo)/2;
ret.x = [-round(dist/2) round(dist/2)];
ret = cs.calc_dist_from_electrode(ret);
ret = cs.create_currentspread(ret);
ret = cs.calculate_dip(ret, flag);
if ret.dip > keepSim(ks).dip
hi = dist;
else
lo = dist;
end
end
keepSim(ks).no_Z_dist(ps) = (hi+lo)/2;
keepSim(ks).no_Z_dip(ps) = ret.dip;
% no RD or Z
ret.z = 0 ;
ret.t_ret = ret.t_ret_min;
lo = 1; hi = 3000;
for i = 1:10
dist = (hi+lo)/2;
ret.x = [-round(dist/2) round(dist/2)];
ret = cs.calc_dist_from_electrode(ret);
ret = cs.create_currentspread(ret);
ret = cs.calculate_dip(ret, flag);
if ret.dip > keepSim(ks).dip
hi = dist;
else
lo = dist;
end
end
keepSim(ks).no_Z_RD_dist(ps) = (hi+lo)/2;
keepSim(ks).no_Z_RD_dip(ps) = ret.dip;
end
end
save([savestr, '_keepSim2'], '-v7.3');
%% scatter plots
figure(4); clf; hold on
plot(ret.d_range(2:end),amp_pred(2:end) , 'g-', 'LineWidth', 2); hold on
for ct = 1:10:length(keepSim)
disp(['ct ', num2str(ct), ' out of ', num2str(length(keepSim))]);
scatter( keepSim(ct).dist, keepSim(ct).eI,'ko', 'MarkerFaceColor', 'k', 'MarkerEdgeColor', 'none', 'MarkerFaceAlpha', .5); hold on
end
set(gca, 'XLim', [1500 3500])
set(gca, 'YLim', [0 600])
xlabel('Physical Distance ');
ylabel('Amplitude');
%% Z vs. RD for successful simulations
figure(5); clf
for ks = 1:10:length(keepSim)
disp(['ct ', num2str(ct), ' out of ', num2str(length(keepSim))]);
p = scatter([keepSim(ks).Th_Z], [keepSim(ks).Th_RD]+.03*randn(size(keepSim(ks).Th_RD)), ...
'ko', 'MarkerFaceColor', 'k', 'MarkerEdgeColor', 'none', 'MarkerFaceAlpha', .5); hold on
end
xlabel('Lift values');
ylabel('RD values');
axis equal
set(gca, 'XLim', [.9 9])
set(gca, 'YLim', [.9 5.5])
%% Histogram of dip values for successful simulations
figure(6); clf
hist([keepSim(:).dip])
set(gca, 'XLim', [0 100])
set(gca, 'XTick', 0:10:100)
xlabel('Dip required for 75% discrimination')
set(gcf, 'Position', [1000 1107 1121 231])
%% histogram of distances, when Z, RD or both is removed
figure(7); clf
orig = []; no_Z = []; no_RD = []; no_Z_RD = [];
for ks = 1:length(keepSim)
orig = cat(1, orig, keepSim(ks).dist(:));
no_Z = cat(1, no_Z, keepSim(ks).no_Z_dist(:));
no_RD = cat(1, no_RD, keepSim(ks).no_RD_dist(:));
no_Z_RD = cat(1, no_Z_RD, keepSim(ks).no_Z_RD_dist(:));
end
ss = 50;
h = histogram(orig,100:ss:3000, 'FaceColor',[1 1 1], 'FaceAlpha', .3, 'EdgeAlpha', 1); hold on
h_no_Z = histogram(no_Z,100:ss:3000, 'FaceColor',[1 0 0], 'FaceAlpha', .3, 'EdgeAlpha',1); hold on
h_no_RD = histogram(no_RD,100:ss:3000, 'FaceColor',[0 1 0], 'FaceAlpha', .3, 'EdgeAlpha',1); hold on
h_no_Z_RD = histogram(no_Z_RD,100:ss:3000, 'FaceColor',[0 0 1], 'FaceAlpha', .3, 'EdgeAlpha', 1); hold on
figure(8);clf
plot(h.BinEdges(2:end)-h.BinWidth/2, h.Values./sum(h.Values), 'k', 'LineWidth', 2); hold on
plot(h_no_Z.BinEdges(2:end)-h_no_Z.BinWidth/2, h_no_Z.Values./sum(h_no_Z.Values), 'r','LineWidth', 2); hold on
plot(h_no_Z_RD.BinEdges(2:end)-h_no_Z_RD.BinWidth/2, h_no_Z_RD.Values./sum(h_no_Z_RD.Values), 'Color', [.5 0 1], 'LineWidth', 2); hold on
plot(h_no_RD.BinEdges(2:end)-h_no_RD.BinWidth/2, h_no_RD.Values./sum(h_no_RD.Values), 'b','LineWidth', 2); hold on
line([1557 1557], [ 0 .35],'Color', [.5 .5 .5]); hold on
line([2291 2291], [ 0 .35], 'Color', [.5 .5 .5])
line([1324 1324], [ 0 .35], 'Color', [.5 .5 .5])
%set(gca, 'YLim', [ 0 45])
line([1557 1557], [ 0 .35],'Color', [.5 .5 .5]); hold on
line([2291 2291], [ 0 .35], 'Color', [.5 .5 .5])
line([1324 1324], [ 0 .35], 'Color', [.5 .5 .5])
set(gca, 'XLim', [0 2700])
set(gca, 'YLim', [0 .35])
xlabel('Min Distance required for 60% discrimination, pctiles')
disp(['distances all :', num2str(round(prctile(orig, [25 50 70])))]); hold on
disp(['no z :', num2str(round(prctile(no_Z, [25 50 70])))]); hold on
disp(['no rd :', num2str(round(prctile(no_RD, [25 50 70])))]); hold on
disp(['no z or rd :', num2str(round(prctile(no_Z_RD, [25 50 70])))]); hold on