The Overfitted Brain: Dreams evolved to assist generalization

TL;DR

This paper proposes that dreams evolved as a biological mechanism to prevent overfitting in the brain, enhancing generalization during learning, similar to techniques used in deep neural networks.

Contribution

It introduces the overfitted brain hypothesis, linking dreams to overfitting mitigation, and provides a framework connecting neuroscience and deep learning.

Findings

Dreams help create corrupted sensory inputs to improve generalization.

Sleep loss and dream deprivation lead to overfitting and poor generalization.

The hypothesis is supported by evidence from neuroscience and deep learning studies.

Abstract

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.