  We present a framework to derive upper bounds on the number of regions that
feed-forward neural networks with ReLU activation functions are affine linear
on. It is based on an inductive analysis that keeps track of the number of such
regions per dimensionality of their images within the layers. More precisely,
the information about the number regions per dimensionality is pushed through
the layers starting with one region of the input dimension of the neural
network and using a recursion based on an analysis of how many regions per
output dimensionality a subsequent layer with a certain width can induce on an
input region with a given dimensionality. The final bound on the number of
regions depends on the number and widths of the layers of the neural network
and on some additional parameters that were used for the recursion. It is
stated in terms of the $L1$-norm of the last column of a product of matrices
and provides a unifying treatment of several previously known bounds: Depending
on the choice of the recursion parameters that determine these matrices, it is
possible to obtain the bounds from Mont\'{u}far (2014), (2017) and Serra et.
al. (2017) as special cases. For the latter, which is the strongest of these
bounds, the formulation in terms of matrices provides new insight. In
particular, by using explicit formulas for a Jordan-like decomposition of the
involved matrices, we achieve new tighter results for the asymptotic setting,
where the number of layers of the same fixed width tends to infinity.
