feature vector of NN

net = results.net
inputs = results.inputs

% Say 'net' is the network. Assume you want the output of layer 'i'.
net.outputConnect(i) = 1 % Modifies the architecture to set layer 'i' as an additional output.
out = sim(net, inputs) % the activations of i-th layer are now included in the output vector.

X = inputs; % Initialize 'X' as inputs. Each layer's values will be computer into 'X'. 

% Layer 1
X = net.IW{1,1} * X + net.b{1}; % Compute the values of layer 1. Use 'net.IW' (input weights)
X = tansig(X) % apply whichever is the activation function of layer 1.

% Layer 2
X = net.LW{2,1} * X + net.b{2}; % compute the values of layer 2. Use 'net.LW' (layer weights)
X = logsig(X) % apply whichever is the activation function of layer 2.

% layer 3
X = net.LW{3,2} * X + net.b{3} % same of for layer 2
X = softmax(X) % apply whichever is the activation function of layer 3.

% 'X' now contains the activations of the third layer in the network.