Plotting a gaussian sample against theoretical gaussian curve in Matlab

I am running some basic examples about gaussian processes.

I want to manually generate a vector with samples of a gaussian process and then plot them.

First I create a Gaussian distribution and a sample vector s:

% CLEAR ALL
clear all
close all

% CREATE GAUSSIAN PROCESS
mu = 2
sigma = 1
d = makedist('Normal','mu',mu,'sigma',sigma);

% CREATE SAMPLE OF ABOVE GAUSSIAN PROCESS
s = random(d, [1000, 1]);

Then I plot the samples in a histogram:

% DISPLAY HOW DISTRIBUTION LOOKS LIKE
figure('Name', 'PDF')
title('GAUSSIAN DISTRIBUTION \mu=2 and \sigma=1')
histogram(s,100); hold on;

% DISPLAY HOW DISTRIBUTION SHALL LIKE
gauss_x = -5:0.01:5
gauss_y = ...
    (1/(sigma*sqrt(2*pi))) ...
    *exp( ...
    (-1/2)*((gauss_x-mu)/sigma).^2 ...
    )
plot(gauss_x, gauss_y); hold off;

Because the area is not normalised to 1, the theoretical gauss plot for mu=2 and sigma=1 appears at a much lower scale.

I try to normalise the area using Normalization parameter:

% NORMALIZE SAMPLE
figure('Name', 'PDF NORMALISED')
title('GAUSSIAN DISTRIBUTION \mu=2 and \sigma=1 NORM.')
histogram(s,100, 'Normalization','probability'); hold on;
plot(gauss_x, gauss_y); hold off;

But I still do not get the expected result, which would be the histogram being scaled with the theoretical curve.

What am I missing? Is it related to not understanding the Normalization parameter correctly or am I missing something related to the mathematical gaussian probability itself?