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Visualisation.m
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Visualisation.m
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clc
clear all
load monkeydata_training.mat
%% Raster plot same trial different unit
[row, col] = find(trial(1,1).spikes(:,:) > 0);
scatter(col, row, 2, 'filled')
%spikes(:,:) = find(trial(1,1).spikes(:,:) > 0);
%scatter(spikes, ones(size(spikes))*[1:size(spikes,2)])
%plotSpikeRaster(trial(1,1).spikes(1,:))
%% Raster plot same unit different trials
row = [];
col = [];
for i = 1:size(trial, 1)
[~, col_temp] = find(trial(i,1).spikes(1,:) > 0);
row = [row ones(1,length(col_temp))*i];
col = [col col_temp];
end
%row = ones(size(pos))*[1:size(pos, 1)]
scatter(col, row, 2, 'filled')
%% PSTH
trial_n = 100; %trial you want to observe
angle_n = 1; %reaching angle you want ot observe
bin_n = length(trial(trial_n,angle_n).spikes(1,:)); %number of bins equal
%to the # of ms
[~, col] = find(trial(trial_n,angle_n).spikes(:,:) > 0);
histogram(col, length(trial(trial_n,angle_n).spikes(1,:)))
normalization_factor = length(col); %factor to calculate the frequency from the probability
histogram(col, bin_n)
hold on
[f,xi] = ksdensity(col); %calculates probability distribution
plot(xi,f*normalization_factor, 'LineWidth', 2)
%% Raster per neuron across angles
min_length = 1*10^5;
for angle_n = 1:size(trial, 2)
for i = 1:size(trial, 1)
temp_length = size(trial(i,angle_n).spikes(1,:), 2);
if temp_length < min_length
min_length = temp_length;
end
end
end
% neurons = 1:14:size(trial(1, 1).spikes, 1);
neurons = 1:1:5;
overall_angles = zeros(length(neurons), min_length, size(trial, 2));
for angle_n=1:size(trial, 2)
for neuron_n = 1:length(neurons)
single_neuron = zeros(size(trial, 1), min_length);
for trial_n=1:size(trial, 1)
signal = trial(trial_n, angle_n).spikes(neurons(neuron_n), :);
single_neuron(trial_n, :) = signal(1:min_length);
end
overall_angles(neuron_n, :, angle_n) = mean(single_neuron);
end
end
max_ = -Inf;
for neuron_n=1:size(overall_angles,1)
for angle_n=1:size(overall_angles,3)
for time_point=1:size(overall_angles,2)
if overall_angles(neuron_n, time_point, angle_n)> max_
max_ = overall_angles(neuron_n, time_point, angle_n);
end
end
end
end
c = 1;
for i=1:length(neurons)
for j=1:size(trial, 2)
figure(100)
hold on;
subplot(length(neurons),size(trial, 2), c)
plot(1:min_length, smoothdata(smoothdata(overall_angles(i, :, j))));
ylim([0 max_]);
xline(300, '--r')
c = c + 1;
end
end
%% Linear fitting of labels
figure()
params_ = zeros(size(trial,2), 3);
for i=1:size(trial,2)
temp = [];
for j=1:size(trial,1)
temp = [temp trial(j,i).handPos];
end
p = polyfit(temp(1, :), temp(2, :), 1);
params_(i, :) = p;
subplot(2,4,i)
hold on;
x = linspace(-20,100,1000);
y = polyval(p, x);
plot(x,y)
scatter(temp(1, :), temp(2, :))
end
%% Hand Trajectory
angles = trial(1, :).handPos;
figure()
hold on
for i=1:size(trial,2)
plot3(trial(1, i).handPos(1, :), trial(1, i).handPos(2, :), trial(1, i).handPos(3, :))
end
view(3)
xlabel('X')
ylabel('Y')
zlabel('Z')
grid on
figure()
hold on
for i=1:size(trial,2)
plot(trial(1, i).handPos(1, :), trial(1, i).handPos(2, :))
end
xlabel('X')
ylabel('Y')
zlabel('Z')
grid on
%% Tuning curve
hold off;
pref_directions = zeros(1, size(trial(1,1).spikes, 1));
for unit_n=1:size(trial, 2)
average_freq = zeros(1, size(trial, 2));
std = zeros(1, size(trial, 2));
angles = [30 70 110 150 190 230 310 350].*(pi/180); %angles corresponding to the 8
for angle_n = 1:size(trial, 2)
for i = 1:size(trial, 1)
temp = average_freq(1, angle_n);
x_new = mean(trial(i,angle_n).spikes(unit_n,:));
average_freq(1, angle_n) = average_freq(1, angle_n) + x_new;
% std(1, angle_n) = 1/i*((i-1)*std(1, angle_n) + (i-1)*(i-2)*(average_freq(1, angle_n)-temp)^2); %running sample variance
std(1, angle_n) = std(1, angle_n) + (x_new-temp)*(x_new-average_freq(1, angle_n)); %running sample intrmediate variance
end
end
average_freq = average_freq/i; %averaging
% std = sqrt(std); %standard deviation from variance
std = sqrt(std/i); %standard deviation from intermediate variance
pref_directions(unit_n) = find(average_freq == max(average_freq));
end
%vertical bars representing the firing frequency at the different angles
figure()
hold on
bar(average_freq)
errorbar(1:8, average_freq, std/2, std/2)
%firing frequency in polar coordinates
figure()
polarplot([angles;angles], [zeros(size(average_freq));average_freq], 'LineWidth', 3);
%% averaged signal for each angle
min_length = 1*10^5;
for angle_n = 1:size(trial, 2)
for i = 1:size(trial, 1)
temp_length = size(trial(i,angle_n).spikes(1,:), 2);
if temp_length < min_length
min_length = temp_length;
end
end
end
average_spike_train = zeros(size(trial, 2), min_length);
for angle_n = 1:size(trial, 2)
for i = 1:size(trial, 1)
average_spike_train(angle_n, :) = average_spike_train(angle_n, :) + mean(trial(i,angle_n).spikes(:,1:min_length), 1);
end
end
average_spike_train = average_spike_train/i;
for i = 1:size(trial, 2)
figure(10)
subplot(4,2,i)
plot(average_spike_train(i,:));
figure(11)
subplot(4,2,i)
%plot(smooth(smooth(smooth(average_spike_train(i,:)))));
end
%% Hand position delta
min_length = 1*10^5;
for angle_n = 1:size(trial, 2)
for i = 1:size(trial, 1)
temp_length = size(trial(i,angle_n).spikes(1,:), 2);
if temp_length < min_length
min_length = temp_length;
end
end
end
average_hand_pos = zeros(2, min_length);
average_deltaHand = zeros(size(trial, 2), min_length-1);
for angle_n = 1:size(trial, 2)
for i = 1:size(trial, 1)
average_hand_pos(1, :) = average_hand_pos(1, :) + trial(i,angle_n).handPos(1,1:min_length)/size(trial, 1);
average_hand_pos(2, :) = average_hand_pos(2, :) + trial(i,angle_n).handPos(2,1:min_length)/size(trial, 1);
end
% average_deltaHand(angle_n, :) = pdist([average_hand_pos(angle_n, 2:end,:); average_hand_pos(angle_n, 1:end-1,:)], 'euclidean');
for i = 1:min_length-1
average_deltaHand(angle_n, i) = norm(average_hand_pos(:, i+1) - average_hand_pos(:, i));
end
average_hand_pos = zeros(2, min_length);
end
figure()
for i = 1:size(trial, 2)
subplot(4,2,i)
plot(average_deltaHand(i,:));
end
%% PSTH across angles
dummy = 10000;
for n_unit_i = 1:98
for angle_i = 1:size(trial, 2)
for trial_i = 1:size(trial, 1)
n_bins = length(trial(trial_i, angle_i).spikes(n_unit_i, :));
if n_bins < dummy
dummy = n_bins;
end
end
end
end
%%%%%%%%%%%%%%%%%%SUM ACROSS ANGLES%%%%%%%%%%%%%%%%
figure
count = 0;
n_neurons = 98;
step = 25;
useful_neurons = 1:98;
for n_unit_i = 17:32 %Choose neuron units to plot
t = [];
for angle_i = 1:size(trial, 2)
for trial_i = 1:size(trial, 1)
[~, t_i] = find(trial(trial_i, angle_i).spikes(n_unit_i, 1:dummy) > 0);
t = [t t_i];
end
end
subplot(4, 4, count+1)
hold on
histogram(t, dummy)
xline(300, 'color', 'b');
xlim([0 dummy])
ylim([0 100])
[F, xi] = ksdensity(t);
plot(xi, F*length(t), 'LineWidth', 2)
smooth = F*length(t);
if (max(smooth) < 15)
useful_neurons(1, n_unit_i) = 0;
end
count = count + 1;
end
useless_neurons = find(useful_neurons == 0);
%% Highest response per angle
pre_motor_window = 320;
average_spike_trains = zeros(size(trial(1,1).spikes, 1), size(trial, 2));
for angle_n = 1:size(trial, 2)
for i = 1:size(trial, 1)
average_spike_trains(:,angle_n) = average_spike_trains(:,angle_n) + mean(trial(i, angle_n).spikes(:, 1:pre_motor_window), 2);
end
end
%active neurons is a matrix, each column represents one angle and
%the neurons are ordered from the highest to lowest
[~, active_neurons] = sort(average_spike_trains, 'descend');