  Understanding of the evolved biological function of sleep has advanced
considerably in the past decade. However, no equivalent understanding of dreams
has emerged. Contemporary neuroscientific theories generally view dreams as
epiphenomena, and the few proposals for their biological function are
contradicted by the phenomenology of dreams themselves. Now, the recent advent
of deep neural networks (DNNs) has finally provided the novel conceptual
framework within which to understand the evolved function of dreams. Notably,
all DNNs face the issue of overfitting as they learn, which is when performance
on one data set increases but the network's performance fails to generalize
(often measured by the divergence of performance on training vs. testing data
sets). This ubiquitous problem in DNNs is often solved by modelers via "noise
injections" in the form of noisy or corrupted inputs. The goal of this paper is
to argue that the brain faces a similar challenge of overfitting, and that
nightly dreams evolved to combat the brain's overfitting during its daily
learning. That is, dreams are a biological mechanism for increasing
generalizability via the creation of corrupted sensory inputs from stochastic
activity across the hierarchy of neural structures. Sleep loss, specifically
dream loss, leads to an overfitted brain that can still memorize and learn but
fails to generalize appropriately. Herein this "overfitted brain hypothesis" is
explicitly developed and then compared and contrasted with existing
contemporary neuroscientific theories of dreams. Existing evidence for the
hypothesis is surveyed within both neuroscience and deep learning, and a set of
testable predictions are put forward that can be pursued both in vivo and in
silico.
