finished solution for population vector

This commit is contained in:
Jan Benda 2017-01-24 12:14:13 +01:00
parent 0501b89444
commit 2fa3bdd38d
3 changed files with 94 additions and 1 deletions

View File

@ -55,6 +55,8 @@
\texttt{spikes} variables of the \texttt{population*.mat} files.
The \texttt{angle} variable holds the angle of the presented bar.
NOTE: the orientation is angle plu 90 degree!!!!!!
\begin{parts}
\part Illustrate the spiking activity of the V1 cells in response
to different orientation angles of the bars by means of spike
@ -97,6 +99,9 @@
\end{parts}
\end{questions}
NOTE: change data generation such that the phase variable is indeed
the maximum response and not the minumum!
\end{document}
gains and angles of the 6 neurons:

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@ -16,6 +16,7 @@ end
close all
cosine = @(p,xdata)0.5*p(1).*(1.0-cos(2.0*pi*(xdata/180.0-p(2))));
files = dir('../unit*.mat');
phases = zeros(length(files), 1);
figure()
for j = 1:length(files)
file = files(j);
@ -31,7 +32,11 @@ for j = 1:length(files)
p0 = [mr, angles(maxi)/180.0-0.5];
%p = p0;
p = lsqcurvefit(cosine, p0, angles, rates');
phase = p(2)*180.0
phase = p(2)*180.0 + 90.0;
if phase > 180.0
phase = phase - 180.0;
end
phases(j) = phase;
subplot(2, 3, j);
plot(angles, rates, 'b');
hold on;
@ -41,3 +46,73 @@ for j = 1:length(files)
ylim([0.0 50.0])
title(sprintf('unit %d', j))
end
%% read out:
a = load('../population04.mat');
spikes = a.spikes;
angle = a.angle;
unitphases = a.phases*180.0 + 90.0;
unitphases(unitphases>180.0) = unitphases(unitphases>180.0) - 180.0;
figure();
subplot(1, 3, 1);
angleestimates1 = zeros(size(spikes, 2), 1);
angleestimates2 = zeros(size(spikes, 2), 1);
for j = 1:size(spikes, 2)
rates = zeros(size(spikes, 1), 1);
for k = 1:size(spikes, 1)
r = firingrate(spikes(k, j), 0.0, 0.2);
rates(k) = r;
end
[x, inx] = sort(phases);
plot(phases(inx), rates(inx), '-o');
hold on;
angleestimates1(j) = popvecangle(phases, rates);
[m, i] = max(rates);
angleestimates2(j) = phases(i);
end
hold off;
subplot(1, 3, 2);
hist(angleestimates1);
subplot(1, 3, 3);
hist(angleestimates2);
angle
mean(angleestimates1)
mean(angleestimates2)
%% read out robustness:
files = dir('../population*.mat');
angles = zeros(length(files), 1);
e1m = zeros(length(files), 1);
e1s = zeros(length(files), 1);
e2m = zeros(length(files), 1);
e2s = zeros(length(files), 1);
for i = 1:length(files)
file = files(i);
a = load(strcat('../', file.name));
spikes = a.spikes;
angle = a.angle;
angleestimates1 = zeros(size(spikes, 2), 1);
angleestimates2 = zeros(size(spikes, 2), 1);
for j = 1:size(spikes, 2)
rates = zeros(size(spikes, 1), 1);
for k = 1:size(spikes, 1)
r = firingrate(spikes(k, j), 0.0, 0.2);
rates(k) = r;
end
angleestimates1(j) = popvecangle(phases, rates);
[m, inx] = max(rates);
angleestimates2(j) = phases(inx);
end
angles(i) = angle;
e1m(i) = mean(angleestimates1);
e1s(i) = std(angleestimates1);
e2m(i) = mean(angleestimates2);
e2s(i) = std(angleestimates2);
end
figure();
subplot(1, 2, 1);
scatter(angles, e1m);
subplot(1, 2, 2);
scatter(angles, e2m);

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@ -0,0 +1,13 @@
function angle = popvecangle(phases, rates)
% estimate population vector
vecs = zeros(2, length(phases));
norm = sum(rates);
vecs(1, :) = rates.*cos(2*pi*phases/180.0)/norm;
vecs(2, :) = rates.*sin(2*pi*phases/180.0)/norm;
mvec = mean(vecs, 2);
angle = atan2(mvec(2), mvec(1));
angle = angle/2/pi*180.0;
if angle < 0
angle = angle + 180.0;
end
end