Merriam-Webster’s defines a time warp as a “discontinuity, suspension or anomaly” in the otherwise normal passage of time; this year all three terms could apply. It seems like March happened 10 years ago; everyday may as well be Wednesday, and still, somehow, here come the holidays — fast, just like every year.

Some bard or novelist may yet come forth to help explain the paradoxes of pandemic time, both its Groundhog Days and the blurs of stress and fear for those on the front lines, or who had infectious people in their household. But brain science also has something to say about the relationship between perceived time and the Greenwich Mean variety, and why the two may slip out of sync.

In a new study, a research team based in Dallas reported the first strong evidence to date of so-called “time cells” in the human brain. The finding, posted by the journal PNAS, was not unexpected: In recent years, several research groups have isolated neurons in rodents that track time intervals. It’s where the scientists look for these cells, and how they identified them, that provide some insight into the subjective experiences of time.

In their paper published in Frontiers of Physics, Franco Vazza (astrophysicist at the University of Bologna) and Alberto Feletti (neurosurgeon at the University of Verona) investigated the similarities between two of the most challenging and complex systems in nature: the cosmic network of galaxies and the network of neuronal cells in the human brain.

Despite the substantial difference in scale between the two networks (more than 27 orders of magnitude), their quantitative analysis, which sits at the crossroads of cosmology and neurosurgery, suggests that diverse physical processes can build structures characterized by similar levels of complexity and self-organization.

The human brain functions thanks to its wide neuronal network that is deemed to contain approximately 69 billion neurons. On the other hand, the observable universe can count upon a cosmic web of at least 100 billion galaxies. Within both systems, only 30% of their masses are composed of galaxies and neurons. Within both systems, galaxies and neurons arrange themselves in long filaments or nodes between the filaments. Finally, within both systems, 70% of the distribution of mass or energy is composed of components playing an apparently passive role: water in the brain and dark energy in the observable Universe.

Circa 2013

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A group of scientists from Kyoto has managed to successfully analyze and “record” the basic elements of what people see when they dream. The idea of recording dreams has been a mainstay in science fiction, but also a frequent goal for researchers. As Smithsonian Magazine writes, this group designed its study based on the premise that brains react to “seeing” objects with repeatable patterns that can be measured with MRI. If a machine can recognize the patterns well enough, it can reverse-engineer them, giving us a window into what’s going on inside people’s heads while they dream.

Three participants were selected for a study and asked to sleep for several three-hour blocks in an MRI scanner. Once they fell asleep, scientists woke them up and asked them to describe what they’d seen in the dream, grouping them into loose categories and sub-categories like “car,” “male,” “female,” or “dwelling.” The group then picked representations of those categories from an online image search and showed them to the participants, once again measuring their brain activity to figure out what patterns might be unique to that concept. Finally, the participants were asked to sleep again, but this time, a machine wouldn’t simply record how their brain responded to dreaming — it would attempt to match it to one of the categories with a series of images, as seen in the video below.

When matching the contents of the video to the categories the sleeper actually recounted when asked about a dream, the machine turned out to be right roughly 60 percent of the time, or better than it could have done by random chance. The system was unsurprisingly better at detecting general meta-categories, like whether someone was looking at a person or a scene, than it was at sensing more specific objects.