Learning cortical representations through perturbed and adversarial dreaming

Abstract
Data availability
Article and author information
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Abstract

Humans and other animals learn to extract general concepts from sensory experience without extensive teaching. This ability is thought to be facilitated by offline states like sleep where previous experiences are systemically replayed. However, the characteristic creative nature of dreams suggests that learning semantic representations may go beyond merely replaying previous experiences. We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs). Learning in our model is organized across three different global brain states mimicking wakefulness, NREM and REM sleep, optimizing different, but complementary objective functions. We train the model on standard datasets of natural images and evaluate the quality of the learned representations. Our results suggest that generating new, virtual sensory inputs via adversarial dreaming during REM sleep is essential for extracting semantic concepts, while replaying episodic memories via perturbed dreaming during NREM sleep improves the robustness of latent representations. The model provides a new computational perspective on sleep states, memory replay and dreams and suggests a cortical implementation of GANs.

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript. Deep learning benchmark datasets (CIFAR-10 and SVHN) were used for the simulations.We published all code necessary to repeat our experiments in the following repository: https://github.com/NicoZenith/PAD

The following data sets were generated

1. Deperrois N
(2022) Learning cortical representations through perturbed and adversarial dreaming
GitHub.

https://github.com/NicoZenith/PAD

The following previously published data sets were used

1. Alex Krizhevsky
(2009) CIFAR-10
CIFAR-10.

https://www.cs.toronto.edu/~kriz/cifar.html
1. Yuval Netzer
(2011) SVHN
SVHN.

http://ufldl.stanford.edu/housenumbers/

Article and author information

Author details

Nicolas Deperrois

Department of Physiology, University of Bern, Bern, Switzerland

For correspondence
nicolas.deperrois@unibe.ch

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-7178-1818
Mihai A Petrovici

Department of Physiology, University of Bern, Bern, Switzerland

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-2632-0427
Walter Senn

Department of Physiology, University of Bern, Bern, Switzerland

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-3622-0497
Jakob Jordan

Department of Physiology, University of Bern, Bern, Switzerland

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-3438-5001

Funding

European Commission (604102)

Mihai A Petrovici
Walter Senn
Jakob Jordan

European Commission (720270)

Mihai A Petrovici
Walter Senn
Jakob Jordan

European Commission (785907)

Mihai A Petrovici
Walter Senn
Jakob Jordan

European Commission (945539)

Mihai A Petrovici
Walter Senn
Jakob Jordan

Universität Bern (Interfaculty Research Cooperation 'Decoding Sleep')

Nicolas Deperrois
Walter Senn

Universität Heidelberg (Manfred Stärk Foundation)

Mihai A Petrovici

Swiss National Science Foundation (Sinergia Grant CRSII5-180316)

Walter Senn

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.