Summary: Researchers have discovered that some neurons not only replay past experiences, but also anticipate future events during sleep.
By studying the hippocampal activity of rats, they discovered that the neurons stabilize spatial representations and prepare for future tasks. This groundbreaking study reveals the role of sleep in neuroplasticity and memory consolidation.
Highlights:
- Hippocampal neurons anticipate future experiences during sleep.
- Sharp ripples in the brain facilitate memory consolidation and spatial representation.
- The study uses advanced machine learning to track neural activity and predict behavior.
Source: Rice University
Some dreams can actually predict the future: new research has shown that during sleep, certain neurons not only replay the recent past, but also anticipate future experience.
This finding is part of a series of insights provided by a study on sleep and learning published in Nature by a team of researchers from Rice University and the University of Michigan.
The research provides unprecedented insight into how individual neurons in the rats’ hippocampus stabilize and adjust spatial representations during rest periods after the animals’ first run through a maze.
“Certain neurons fire in response to specific stimuli,” said Kamran Diba, associate professor of anesthesiology at Michigan and corresponding author of the study. “Neurons in the visual cortex activate when presented with the appropriate visual stimulus. The neurons we study show location preferences.
Working with collaborators in the Neural Circuits and Memory Lab led by Diba, Rice neuroscientist Caleb Kemere studied the process by which these specialized neurons produce a representation of the world after a new experience.
Specifically, the researchers tracked sharp ripples, a pattern of neural activation known to play a role in the consolidation of new memories and, more recently, has also been shown to mark which parts of a new experience should be stored as memories.
“For the first time in this paper, we observed how these individual neurons stabilize spatial representations during rest periods,” said Kemere, associate professor of electrical and computer engineering and bioengineering at Rice.
Sleep is essential for memory and learning. Science has quantified this age-old intuition by measuring performance on memory tests after a nap rather than after a period of wakefulness or even sleep deprivation.
About 20 years ago, scientists also discovered that neurons in the brains of sleeping animals that had been allowed to explore a new environment just before resting fired to replay the animals’ trajectories during sleep. ‘exploration.
This finding is consistent with the knowledge that sleep helps new experiences crystallize into stable memories, suggesting that the spatial representations of many of these specialized neurons in the hippocampus are stable during sleep. However, researchers wanted to see if there was more to the story.
“We imagined that some neurons might change their representations, reflecting the experience we all have of waking up with a new understanding of a problem,” Kemere said. “To show this, however, it was necessary to track how individual neurons achieve spatial tuning, that is, the process by which the brain learns to navigate a new route or environment.”
The researchers trained rats to run back and forth on an elevated track with a liquid reward at each end and observed how individual neurons in the animals’ hippocampus “grew” in the process. By calculating an average spike rate over several rounds, the researchers were able to estimate the neurons’ localization field – or the area of the environment that a given neuron “cared about” the most.
“The critical point here is that locum fields are estimated using the behavior of the animal,” Kemere said, highlighting the challenge of assessing what happens to locum fields during rest periods when the animal is not does not physically move through the maze.
“I’ve been thinking for a long time about how we can assess neuron preferences outside of the maze, such as during sleep,” Diba said. “We addressed this challenge by relating the activity of each neuron to the activity of all other neurons.”
This is the key innovation of the study: the researchers developed a statistical machine learning approach that used the other neurons studied to estimate where the animal dreamed of being. They then used these dreamed positions to estimate the spatial tuning process of each neuron in their datasets.
“The ability to track neuron preferences even without a stimulus was an important advance for us,” Diba said.
Diba and Kemere praised Kourosh Maboudi, a postdoctoral researcher at Michigan and lead author of the study, for his role in developing the learned tuning approach.
The method confirmed that the spatial representations that form during the experience of a new environment are, for most neurons, stable during several hours of post-experience sleep. But as the researchers predicted, the story doesn’t end there.
“What I loved most about this research and the reason I was so excited was discovering that it’s not necessarily the case that during sleep the only thing these neurons do is to stabilize a memory of the experience,” Kemere said. . “It turns out that some neurons end up doing other things.
“We can observe these other changes happening during sleep, and when we put the animals back into the environment a second time, we can validate that these changes actually reflect something that was learned while the animals were sleeping.” It is as if the second exposure to space occurs while the animal is sleeping.
This is important because it provides a direct observation of neuroplasticity as it occurs during sleep. Kemere pointed out that almost all research on plasticity – which examines the mechanisms that allow neurons to rewire themselves and form new representations – examines what happens during periods of wakefulness when stimuli are presented rather than during the sleep when relevant stimuli are absent.
“It seems that plasticity or rewiring of the brain requires very rapid time scales,” Diba said, pointing out the fascinating relationship between the duration of actual experience, “which can take the space of seconds, minutes but also hours or days”, and real memories, “which are super compressed”.
“If you remember something, the memory is instantaneous,” Diba said, referring to a famous literary passage by French modernist writer Marcel Proust in which a childhood memory resurrects an entire lost world of past experiences in an instant.
The study is an example of the advances in neuroscience enabled in recent decades by technological advances in the design of stable, high-resolution neural probes as well as computational power based on machine learning.
In light of this progress, Kemere said that brain science is poised to make significant advances in the future, while at the same time expressing concern about the impact of recent budget cuts on the continuation of the research.
“It’s quite possible that if we had started this work today, we wouldn’t have been able to do these experiments and get these results,” Kemere said. “We’re really grateful that the opportunity presented itself.”
Funding: The research was supported by the National Institutes of Health (R01NS115233, R01MH117964). The contents of this press release are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
About this sleep and neuroscience news
Author: Silvia Cernea Clark
Source: Rice University
Contact: Silvia Cernea Clark – Rice University
Picture: Image is credited to Neuroscience News
Original research: Closed access.
“Readjustment of hippocampal representations during sleep” by Kamran Diba et al. Nature
Abstract
Readjustment of hippocampal representations during sleep
The hippocampal representations underlying spatial memory undergo continuous refinement after their formation.
Here, to track spatial tuning of neurons dynamically during offline states, we used a novel Bayesian learning approach based on average decoded position triggered by spikes in ensemble recordings of freely moving rats.
By measuring these settings, we found spatial representations within the sharp ripples of the hippocampus that were stable for hours during sleep and strongly aligned with the place fields initially observed during maze exploration.
These representations were explained by a combination of factors including a preconfigured structure before exposure to the maze and representations that emerged during θ oscillations and sharp wave ripples awakened in the maze, revealing the contribution of these events to the formation of ensembles.
Strikingly, ripple representations during sleep predicted future location fields of neurons upon re-exposure to the maze, even when these fields deviated from previous location preferences.
In contrast, we observed settings with misalignment to the maze fields during sleep and rest before maze exposure and in the later stages of sleep.
In summary, the new decoding approach allowed us to infer and characterize the stability and readjustment of place fields during offline periods, revealing the rapid emergence of representations following further exploration and the role of sleep in dynamic representation of the hippocampus.