Title: On the effect of the activation function on the distribution of hidden nodes in a deep network. Abstract: We analyze the joint probability distribution on the lengths of the
vectors of hidden variables in different layers of a fully connected
deep network, when the weights and biases are chosen randomly according to
Gaussian distributions, and the input is binary-valued.  We show
that, if the activation function satisfies a minimal set of
assumptions, satisfied by all activation functions that we know that
are used in practice, then, as the width of the network gets large,
the ``length process'' converges in probability to a length map
that is determined as a simple function of the variances of the
random weights and biases, and the activation function.

We also show that this convergence may fail for activation functions 
that violate our assumptions.