Don't worry, you probably aren't swallowing spiders. Breakthroughs, discoveries, and DIY tips sent every weekday. All of us lay down every night and proceed to be completely unaware of what's happening in the waking world around us as we snooze. It shouldn't be surprising, then, that there are all kinds of myths about sleep. We have inaccurate ideas about what prevents us from sleeping, what helps us sleep, and what happens while we're sleeping.

Do you need more sleep in fall and winter? Less sunlight, colder weather, and diet changes make us sleepier--and that's OK. Winter mornings make staying under the covers feel impossible to resist. Breakthroughs, discoveries, and DIY tips sent every weekday. It's a crisp, fall day in mid-November, and though your calendar is filled with evening get-togethers and morning runs, you're feeling sluggish.

Dream2Image is the world's first dataset combining EEG signals, dream transcriptions, and AI-generated images. Based on 38 participants and more than 31 hours of dream EEG recordings, it contains 129 samples offering: the final seconds of brain activity preceding awakening (T-15, T-30, T-60, T-120), raw reports of dream experiences, and an approximate visual reconstruction of the dream. This dataset provides a novel resource for dream research, a unique resource to study the neural correlates of dreaming, to develop models for decoding dreams from brain activity, and to explore new approaches in neuroscience, psychology, and artificial intelligence. Available in open access on Hugging Face and GitHub, Dream2Image provides a multimodal resource designed to support research at the interface of artificial intelligence and neuroscience. It was designed to inspire researchers and extend the current approaches to brain activity decoding. Limitations include the relatively small sample size and the variability of dream recall, which may affect generalizability.

Scientists have brought science fiction one step closer to reality by achieving the first two-way communication between individuals during lucid dreaming. In an experiment that sounds like a scene out of the movie'Inception,' REMspace - a California-based startup that designs technology to enhance sleep and lucid dreaming - reportedly exchanged a message between two people who were asleep. The company used'specially designed equipment' which included a'server,' an'apparatus,' 'Wifi' and'sensors,' but did not specify the exact technology they used. The study participants were asleep in separate homes when REMspace researchers beamed a word created through a unique language between them. REMspace CEO and founder Michael Raduga said: 'Yesterday, communicating in dreams seemed like science fiction.

The idea of controlling your dreams might sound like the plot of the latest science fiction blockbuster. But this mysterious gift is a reality for around 20 per cent of people, who are able to go on exciting trips in impossible worlds. Depicted in films such as'Inception', lucid dreaming could provide a useful link between the real world and the dream world. Scientists are trying to tap into the potential of lucid dreaming, helping people complete tasks like turning on lights or even driving virtual cars while asleep. Here are three tricks that could allow you to try it for yourself.

In this work, we leverage machine learning techniques to identify potential biomarkers of oxygen desaturation during sleep exclusively from electroencephalogram (EEG) signals in pediatric patients with sleep apnea. Development of a machine learning technique which can successfully identify EEG signals from patients with sleep apnea as well as identify latent EEG signals which come from subjects who experience oxygen desaturations but do not themselves occur during oxygen desaturation events would provide a strong step towards developing a brain-based biomarker for sleep apnea in order to aid with easier diagnosis of this disease. We leverage a large corpus of data, and show that machine learning enables us to classify EEG signals as occurring during oxygen desaturations or not occurring during oxygen desaturations with an average 66.8% balanced accuracy. We furthermore investigate the ability of machine learning models to identify subjects who experience oxygen desaturations from EEG data that does not occur during oxygen desaturations. We conclude that there is a potential biomarker for oxygen desaturation in EEG data.

Semantic representations in higher sensory cortices form the basis for robust, yet flexible behavior. These representations are acquired over the course of development in an unsupervised fashion and continuously maintained over an organism's lifespan. Predictive learning theories propose that these representations emerge from predicting or reconstructing sensory inputs. However, brains are known to generate virtual experiences, such as during imagination and dreaming, that go beyond previously experienced inputs. Here, we suggest that virtual experiences may be just as relevant as actual sensory inputs in shaping cortical representations. In particular, we discuss two complementary learning principles that organize representations through the generation of virtual experiences. First, "adversarial dreaming" proposes that creative dreams support a cortical implementation of adversarial learning in which feedback and feedforward pathways engage in a productive game of trying to fool each other. Second, "contrastive dreaming" proposes that the invariance of neuronal representations to irrelevant factors of variation is acquired by trying to map similar virtual experiences together via a contrastive learning process. These principles are compatible with known cortical structure and dynamics and the phenomenology of sleep thus providing promising directions to explain cortical learning beyond the classical predictive learning paradigm.

A trio of researchers from the University of California, working with a colleague from the Institute of Computer Science of the Czech Academy of Sciences, has found that it is possible to prevent catastrophic forgetting in AI systems by having such systems mimic human REM sleep. In their paper published in PLOS Computational Biology, Ryan Golden, Jean Erik Delanois, Maxim Bazhenov and Pavel Sanda describe teaching artificial intelligence systems to remember what was learned from a beginning task when working on a second task. Prior research has shown that people experience something called consolidation of memory during REM sleep. It is a process whereby things that were experienced recently are moved to long term memory to make room for new experiences. Without such a process, the brain undergoes catastrophic forgetting, where memories of recent things are not retained.

Neuroscience Beyond down-regulating cortical activity, sleep also promotes long-term memory formation and the strengthening of synaptic connections. It is possible that both core sleep stages, slow-wave sleep (SWS) and rapid eye movement (REM) sleep, contribute to these processes. Niethard et al. used in vivo two-photon calcium imaging in mice to assess the activity of large populations of cortical layer 2/3 cells during SWS and REM sleep. Most pyramidal neurons substantially decreased their activity during SWS and REM sleep episodes. The decrease during SWS sleep, but not during REM sleep, was accompanied by increased inhibitory interneuron activity. However, a subpopulation of pyramidal cells exhibited upregulated activity during SWS. These neurons are possibly involved in memory formation, and also underwent profound down-regulation during subsequent REM sleep. J. Neurosci. , 41 , 4212 (2021).

Why we dream is one of science's most perplexing mysteries, but a neuroscientist in the US thinks he finally has the answer. Erik Hoel, a research assistant professor of neuroscience at Tufts University in Massachusetts, has taken inspiration from artificial intelligence (AI) for his theory. In a new report, he argues that the often hallucinogenic, nonsensical quality of dreaming is like throwing in new, unexpected data to a neural network. Professor Hoel calls this the'overfitted brain hypothesis' – and argues that it keeps human minds from'fitting too well to their daily distribution of stimuli'. This illustration represents the overfitted brain hypothesis of dreaming, which claims that the sparse and hallucinatory quality of dreams helps prevent the brain from'overfitting' to its biased daily sources of learning Neural networks are a subset of machine learning and are at the heart of deep learning algorithms.