Matlab Neural Networks: input size vs. number of inputs
This question is better fit in stackoverflow or cross validated site, yet I am trying to illustrate a simple example. 

Suppose you have three measurements $a$, $b$, and $c$, with each measurement only $1$ time reading, the hidden layer node and output are scalar. Compose an input vector is shown below:

        w1 
(a,b,c) =&gt; (H1)-------(O1) =&gt; Result


The value at output layer is $ \alpha ((a+b+c)w_1)$, where $\alpha$ is the activation function.

When you treat the measurements separately at the input layer, the neural network is:

  w1 
a =&gt; 
     \
  w2                
b =&gt; (H1)----------(O1) =&gt; Result
     /                
  w3                
c =&gt; 


and the output is $ \alpha (aw_1 + bw_2 + cw_3)$. Now you are dealing with three independent neurons, which indicates that updating one of the weights won't change the 'learned' weights on other input nodes. 

As far as I know, a single neuron usually take in scalars rather than vector values (for the case of one time reading), or take in vectors rather than matrix arrays (for the case of a series of time readings).