We can generalize this expression to an MLP with \( l \) hidden layers. The complete functional form is, $$ \begin{align} &y^{l+1}_1\! = \!f_{l+1}\!\left[\!\sum_{j=1}^{N_l}\! w_{1j}^3 f_l\!\left(\!\sum_{k=1}^{N_{l-1}}\! w_{jk}^2 f_{l-1}\!\left(\! \dots \!f_1\!\left(\!\sum_{n=1}^{N_0} \!w_{mn}^1 x_n\! + \!b_m^1\!\right) \!\dots \!\right) \!+ \!b_k^2\!\right) \!+ \!b_1^3\!\right] && \tag{10} \end{align} $$ which illustrates a basic property of MLPs: The only independent variables are the input values \( x_n \).