  We consider inverse problems where the conditional distribution of the
observation ${\bf y}$ given the latent variable of interest ${\bf x}$ (also
known as the forward model) is known, and we have access to a data set in which
multiple instances of ${\bf x}$ and ${\bf y}$ are both observed. In this
context, algorithm unrolling has become a very popular approach for designing
state-of-the-art deep neural network architectures that effectively exploit the
forward model. We analyze the statistical complexity of the gradient descent
network (GDN), an algorithm unrolling architecture driven by proximal gradient
descent. We show that the unrolling depth needed for the optimal statistical
performance of GDNs is of order $\log(n)/\log(\varrho_n^{-1})$, where $n$ is
the sample size, and $\varrho_n$ is the convergence rate of the corresponding
gradient descent algorithm. We also show that when the negative log-density of
the latent variable ${\bf x}$ has a simple proximal operator, then a GDN
unrolled at depth $D'$ can solve the inverse problem at the parametric rate
$O(D'/\sqrt{n})$. Our results thus also suggest that algorithm unrolling models
are prone to overfitting as the unrolling depth $D'$ increases. We provide
several examples to illustrate these results.
