CODEWITHSUNDEEP
matlabgaussian
chinkare_16
chinkare_16

Reputation: 135

How to make smooth curve nerve signals from neuron spike data

I have my data stored in structures regarding recording from neurons. Neuronal spikes are stored in a logical array where a spike is 1 and no spike is 0. 

spike = <1x50 logical> 
spike = [1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 1 1 0 0 ...]

What I have to do is to convert these spikes into smooth curve signal using Gaussian filter.

I have the following function for smoothing:

function z = spikes(x, winWidth)
% places a Gaussian centered on every spike
% if x is matrix, then perform on the columns

winWidth = round(winWidth);

if winWidth == 0
    y = [0 1 0];
    w = 1;
else
    w = winWidth * 5;
    t = -w : w;
    y = normpdf(t,0,winWidth);
end

if isvector(x)
    z = conv(x,y);
    z = z(w+1 : end);
    z = z(1 : length(x));
else
    z = zeros(size(x));
    for i = 1 : size(x,2)
        z1 = conv(x(:,i),y);
        z1 = z1(w+1 : end);
        z1 = z1(1 : length(x));
        z(:,i) = z1;
    end
end

end

I was just wondering how can I make nerve signals from spikes that are like the above logical array?

PS: I am very lost and my answers are not understandable to be posted here.

Upvotes: 1

Views: 126

Answers (1)

ver228
ver228

Reputation: 109

If I understood correctly you just have to increase the sampling frequency and convolve. Since your original array corresponds to a signal with sampling frequency of one spike, if you want to increase the resolution of your spikes, you need to artificially introduce more data points between the spikes.

spike = [1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 1 1 0 0];

![n_samples = numel(spike);
resampling_f = 50;
new_signal = zeros(n_samples*resampling_f,1);

spikes_ind = find(spike);
new_signal((spikes_ind-1)*50+round(resampling_f/2)) = 1;

%here you can use the spikes function you defined
winWidth = 10;
w = winWidth * 5;
t = -w : w;
kernel = normpdf(t,0,winWidth);
spikes_sample = conv(x,kernel);

figure, hold on
subplot(1,2,1), hold on
plot(new_signal)

subplot(1,2,2), hold on
plot(spikes_sample)][1]

enter image description here

Upvotes: 2

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