Summary: As the body moves between REM sleep and slow-wave sleep cycles, the hippocampus and neocortex interact to facilitate memory formation.
Source: University of Pennsylvania
What role do sleep stages play in the formation of memories?
“We’ve known for a long time that useful learning occurs during sleep,” says University of Pennsylvania neuroscientist Anna Schapiro. “You encode new experiences while you’re awake, you go to sleep, and when you wake up, your memory has been transformed in some way.”
However, precisely how new experiences are processed during sleep has largely remained a mystery. Using a neural network computational model they built, Schapiro, Penn Ph.D. student Dhairyya Singh and Kenneth Norman of Princeton University now have new insight into the process.
In research published in the Proceedings of the National Academy of Sciences, they show that as the brain cycles through slow-wave, rapid-eye-movement (REM) sleep, which happens about five times a night, the hippocampus teaches the neocortex what it learned. , transforming new and fleeting information into lasting memory.
“This isn’t just a model of learning in local brain circuits. It’s how one brain region can teach another brain region during sleep, a time when there’s no guidance from the outside world,” says Schapiro, assistant professor in the Department of Psychology at Penn. “It’s also a proposal for how we learn gracefully over time as our environment changes.”
Broadly speaking, Schapiro studies learning and memory in humans, specifically how people acquire and consolidate new information. She has long thought that sleep played a role here, something she and her team have been testing in a lab, recording what happens in the brain while participants sleep.
His team also builds neural network models to simulate learning and memory functions. For this work specifically, Schapiro and his colleagues built a model of a neural network composed of a hippocampus, the brain’s center for new memories, responsible for learning the day-to-day episodic information of the world, and the neocortex , responsible for facets such as language, superior. – level of cognition and more permanent memory storage.
During simulated sleep, researchers can observe and record which simulated neurons fire when they are in these two areas, and then analyze these patterns of activity.
The team ran several sleep simulations using a brain-inspired learning algorithm they created. The simulations revealed that during slow-wave sleep, the brain mostly reviews recent incidents and data, guided by the hippocampus, and during REM sleep, it mainly repeats what happened previously, guided by storage of memory in neocortical regions.
During simulated sleep, researchers can observe and record which simulated neurons fire when they are in these two areas, and then analyze these patterns of activity. The image is in the public domain
“As the two brain regions connect during non-REM sleep, that’s when the hippocampus really teaches the neocortex,” says Singh, a second-year doctoral student in Schapiro’s lab. “Then, during the REM phase, the neocortex is reactivated and can reproduce what it already knows,” consolidating control of the data in long-term memory.
Alternating between the two stages of sleep is also important, he says. “When the neocortex doesn’t get a chance to reproduce its own information, we see that the information that is there gets overwritten. We think you need to have alternating REM and non-REM sleep for strong memory formation to occur.”
The findings are consistent with what is known in the field, although aspects of the model are still theoretical.
“We have yet to test this,” Schapiro says. “One of our next steps will be to conduct experiments to understand whether REM sleep actually generates old memories and what implications this might have for integrating new information into your existing knowledge.”
Because the current simulations were based on a typical adult getting a healthy night’s sleep, they don’t necessarily transfer to other types of adults or less-than-stellar sleep habits.
Nor do they provide information about children, who require different amounts and types of closure than adults. Schapiro says he sees great potential for his model to answer some of these outstanding questions.
“Having a tool like this allows you to go in many directions, especially because the architecture of sleep changes throughout life and in various disorders, and we can simulate those changes in the model,” he says.
In the long run, a better understanding of the role of sleep stages in memory could help inform treatments for psychiatric and neurological disorders for which sleep deficits are a symptom. Singh says there could also be implications for deep learning and artificial intelligence.
“Our biologically inspired algorithm could provide new directions for more powerful offline memory processing in AI systems,” he says.
This proof-of-concept work connecting sleep and memory formation brings the field one step closer to those goals.
Funding: Funding for this research came from the National Institutes of Health (Grant R01 MH069456) and the Charles E. Kaufman Foundation (Grant KA2020-114800).
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About this sleep and memory research news
Author: Michele BergerSource: University of PennsylvaniaContact: Michele Berger – University of PennsylvaniaImage: Image is in the public domain
Original research: Access closed. “A model of autonomic interactions between the hippocampus and neocortex driving sleep-dependent memory consolidation” by Anna Schapiro et al. PNAS
Summary
A model of autonomic interactions between hippocampus and neocortex driving sleep-dependent memory consolidation
How do we expand our knowledge of the world over time?
Many theories of memory formation and consolidation have postulated that the hippocampus stores new information and then “teaches” that information to the neocortex over time, especially during sleep. But it is not clear, mechanistically, how this actually works: how can these systems interact during periods with virtually no environmental input to achieve useful learning and changes in representation?
We provide a framework for thinking about this question, with simulations of neural network models as a demonstration.
The model is composed of hippocampus and neocortical areas, which reproduce memories and interact with each other completely autonomously during simulated sleep. Oscillations are harnessed to support error-based learning that leads to useful changes in memory representation and behavior.
The model has a non-rapid eye movement (NREM) sleep stage, where the dynamics between the hippocampus and neocortex are tightly coupled, with the hippocampus helping the neocortex to restore high-fidelity versions of new attractors, and a phase of REM sleep, where the neocortex is located. able to more freely explore existing attractions.
We find that alternating between NREM and REM sleep stages, which alternately focuses model playback on recent and remote information, facilitates elegant continuous learning.
We thus provide an account of how the hippocampus and neocortex can interact without any external input during sleep to drive useful new cortical learning and protect old knowledge as new information is integrated.