A group at the Institute of Science Tokyo simultaneously measured the activity of thousands of neurons in two mousebrain regions and found that the areas sometimes shared timing information but maintained more independent representations at other times. The researchers, led by Associate Professor Riichiro Hira, maintained that this flexibility enables the brain to remain stable as it adapts to changing requirements.
A Task Built to Catch the Brain Switching Modes
Hira’s team created a task to highlight this timing mystery. They trained mice to expect rewards at intervals that switched between 6 and 12 seconds, without any break to mark the end of one trial and the start of the next. Since there was no pause, the mice had to keep updating their internal sense of time throughout the experiment, instead of resetting their mental stopwatch for each new trial.
The mice eventually figured out the pattern. At first they expected a reward every six seconds even if the time they had to wait was longer than this, but by the end of the training session, their behavior indicated that they had learned to anticipate both reward intervals. This showed that the mice were capable of keeping track of both durations and of predicting which one would come next.
While the mice performed the task, the researchers recorded activity in their secondary motor cortex and the posterior parietal cortex using wide-field two-photon calcium imaging. Both regions play key roles in planning, prediction, and working memory.
Two Regions, Two Kinds of Mistakes
The neurons fired in sequence in each brain area, with different cells becoming active at different times. This sequence showed that each region was monitoring the passage of time.
The researchers also identified two kinds of errors in the neural activity. In some cases, both areas made the same mistake when assessing the passage of time. These coherent errors suggested that both regions sometimes shared the same timing drift. In other instances, only one region was inaccurate while the other remained correct. These independent errors suggested that one region could temporarily maintain a different timing representation from the other. Independent errors were more frequent than coherent ones, and this ratio remained the same in all the mice examined.
If the brain regions were just randomly out of sync, you’d expect the ratio to change. However, it didn’t, which suggests that the brain has a built-in way to balance working together and working independently.
Modeling the Switch Between Coherence and Independence
To identify this mechanism, the team built a computational model with two recurrent neural networks linked by sparse, long-range connections and exposed to the same background noise. The model produced the same 2-to-1 ratio of independent to coherent errors seen in the mice.
The model also discovered that the limited connections between the networks tended to cause them to become synchronized, while the common noise caused them to move in separate directions. Hira provided an explanation of the interaction saying, “We found sparse inter-regional connectivity promotes synchronization between the two regions, whereas widespread fluctuations prevent complete synchronization, allowing each region to preserve its own temporal representation.” Neither force takes precedence; instead, a compromise is reached which allows communication between the regions.
The findings challenge the idea that brain regions rely on a single synchronized timing system. It indicates that the brain can adjust how different parts are coordinated, rather than just turning that coordination on or off.
What a Flexible Clock Could Mean for the Brain
Hira’s team believes that there are implications beyond simple timing circuits. “In the future, our results may contribute to a better understanding of cognition, neurological disorders involving disrupted inter-regional coordination, and the development of brain-inspired artificial intelligence and robotic control systems that combine stability with flexibility,” said Hira.
This last idea may prove most important in the years ahead. Engineers building AI and robotic systems face a similar challenge: how to let separate parts work on their own when needed, yet still come back into sync. The mouse brain shows that sparse connections and shared background fluctuations can achieve this balance. This could point to a design principle that even applies to systems with no biological components.
Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.
“Our findings show that the brain is far more active and capable during unconsciousness than previously thought,” said Dr. Sameer Sheth, professor and Cullen Foundation Endowed Chair of neurosurgery and a McNair Scholar at Baylor. “Even when patients are fully anesthetized, their brains continue to analyze the world around them.”
a Rare Look Inside the Anesthetized Hippocampus
Sheth’s team recorded from hundreds of individual neurons in the hippocampus, a region of the brain essential for memory formation. Researchers collected data during epilepsy surgeries, specifically anterior temporal lobectomies, when patients were under general anesthesia and the hippocampus was temporarily accessible. This rare opportunity allowed researchers to directly record hippocampal activity, something scalp EEG cannot.
The researchers used Neuropixels probes, high-density recording devices that can monitor hundreds of neurons simultaneously. This study represents the first use of this technology in the human hippocampus during anesthesia. Seven patients participated, all under propofol anesthesia with consistent sedation levels maintained throughout the recordings.
The Unconscious Brain Still Learns
In the initial test, researchers played patients a repeating tone with occasional unexpected pitches. Hippocampal neurons consistently detected these outlier tones, and their sensitivity increased over approximately ten minutes. Researchers usually observe this kind of adaptive neural response during wakefulness.
To dig deeper, the researchers built a computer model of a recurrent neural network, trained to perform the same tone discrimination task. The model showed the same pattern as the human brain recordings, suggesting that this increased sensitivity to outlier tones can arise from local brain circuits alone, without the need for higher-level conscious processing.
Predicting Words Before They’re Spoken
The researchers also played short stories to anesthetized patients while recording from the same neurons. Hippocampal activity tracked language in real time, distinguishing between nouns, verbs, and adjectives. Neural activity reflected both the current word and prediction of the upcoming word in the sequence. “The brain appears to anticipate what comes next in a story, even without conscious awareness,” Sheth said.
“This kind of predictive coding is something we associate with being awake and attentive, yet it’s happening here in an unconscious state,” added Baylor neurosurgery professor Benjamin Hayden.
What This Means for the Science of Consciousness
The results show that the hippocampus can support neural computations associated with complex cognition even during unconsciousness. This suggests that at least some components of language processing and prediction may not require conscious awareness. Sheth’s team proposes that consciousness does not arise from a single brain region but rather emerges from interactions among multiple brain regions. The hippocampus may process information that later becomes part of awareness.
The researchers noted a parallel with artificial intelligence. Large language models generate text by predicting the next word in a sequence; the anesthetized hippocampus showed a similar pattern of prediction. This similarity may provide neuroscientists and AI researchers with a shared framework for studying language prediction based on biological circuitry.
“Can we use these signals to deploy and run a speech prosthetic for some of the parts of the brain that are damaged by stroke or injury? These are questions that we can now consider in relation to this part of the brain,” said study author and Baylor neurosurgery resident Kalman Katlowitz.
While these results are promising, the team acknowledged the study’s limitations. The research focused only on propofol-based general anesthesia, so the results may not apply to other unconscious states such as natural sleep or coma. Additionally, the researchers examined only the hippocampus, so they cannot yet determine how broadly this type of unconscious processing occurs in other brain regions.
“This work pushes us to rethink what it means to be conscious,” Sheth concluded. “The brain is doing much more behind the scenes than we fully understand.”
Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.
When these neurons were deactivated in mice, the animals became highly distractible, mirroring one of the hallmark features of ADHD. Restoring activity in these neurons allowed the mice to regain normal focus.
An Old Problem With No Modern Answer
Selective spatial attention is the brain’s ability to focus on relevant information while ignoring distractions. This process allows a person to follow a conversation in a noisy environment or recognize a familiar face in a crowd. Conditions such as ADHD and autism also often involve disruptions in selective attention. For many years, scientists believed the prefrontal cortex was primarily responsible for this function.
However, this view does not fully explain how attention works across different species. Primates have a well-developed prefrontal cortex, while other vertebrates have a less pronounced version, yet birds, fish, and turtles still effectively focus their attention.
“If we really go back in evolution, for hundreds of millions of years, birds have had this ability, fish have had this ability. And they do not typically have a highly developed prefrontal cortex, so how does the brain solve this problem?” said lead author Ninad Kothari, a postdoctoral fellow in Johns Hopkins’ Department of Psychological and Brain Sciences. “We were able to identify an evolutionarily old region in the brainstem which affords this ability.”
A Filter Hiding in Plain Sight
The researchers traced this behavior to a network of inhibitory neurons located deep within the brainstem, a region found in all vertebrates. Mysore’s laboratory had previously studied this area in birds, frogs, and turtles before examining it in mice.
The team created an attention task, similar to those used in human studies, to test the role of these neurons. Mice viewed visual cues on a screen and earned rewards for responding to a target in front of them while ignoring a distracting signal off to the side. This test measured how well the mice could pick out important information and ignore distractions. The mice successfully performed the task until researchers deactivated the brainstem neurons, at which point they became easily distracted.
Ruling Out the Obvious Explanations
Before concluding that these neurons controlled attention, the researchers tested for other possible explanations, such as vision or motor problems, that might account for the mice’s behavior. However, the researchers ruled out these alternative explanations.
“The only thing impaired was their ability to take the competing pieces of information, compare them, and pay attention to the location with the most important information,” said senior author Shreesh Mysore, a neuroscientist who studies neural circuits tied to behavior. “This part of the brain is like an attentional selection engine. It helps solve the question: ‘What is most important information I should pay attention to right now?'”
The attention deficit was not permanent. When the neurons were reactivated, the same mice that had previously been unable to ignore faint distractions were once again able to ignore even strong distractors.
“A hallmark of ADHD is that even faint distractors draw attention away — and that’s exactly what we see here when these neurons are silenced,” Mysore said. “But the very next day, when the neurons are turned back on, the same animal can ignore distractors again, even very strong ones.”
What This Could Mean for Human Brains
“All the evidence to date suggests that these neurons exist in humans too,” Mysore said. “But are they responsible for selective spatial attention in humans? An exciting hypothesis is that they play a crucial role.”
If researchers find that these neurons function differently in people with attention disorders, the circuit could become a target for future therapies.
Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.
Researchers at NYU Langone Health recorded the activity of hundreds of neurons simultaneously across several brain regions in mice. By tracking how signals move from the hippocampus to the neocortex, where long-term memories are kept, they found that a subset of hippocampal neurons appears to participate in routing information between regions in distinct patterns. These cells participate in both incoming and outgoing signaling, but do so using distinct patterns of activity.
Separate Channels Through the Same Cells
The study focused on a series of interconnected brain regions: CA3, which processes rapidly changing information; CA1, which serves as a central hub; and the retrosplenial cortex, which is important for navigation and spatial memory. About one in four CA1 neurons receives most of the signals from CA3. When these same neurons send signals to the retrosplenial cortex, they fire in a different pattern, creating a separate outgoing channel using the same cells.
Rather than using new neurons, the brain separates these channels by changing how the same neurons fire together. The researchers suggest this arrangement may help reduce interference between new learning and more stable cortical memories.
“Our findings help explain how memory can be both moldable and enduring,” said co-lead author Joaquín Gonzalez, PhD, a postdoctoral fellow in the Department of Psychiatry at NYU Grossman School of Medicine. “By changing how the same cells fire together instead of turning on new cells, the brain can keep information organized and protect older memories.”
What Happens During Sleep
The CA1 neurons involved in learning during the day also stay active at night. The researchers found that these cells remain busy during sleep events called sharp-wave ripples, which are bursts of activity the hippocampus uses to replay and strengthen memories from the day.
Since overlapping neurons are recruited during both waking activity and sleep replay, the same circuit elements appear to participate in both learning and consolidation. This theoretically allows the brain to use a single system for both learning and storing memories, without the two processes interfering with each other.
“Our study shows how learning and memory consolidation can coexist in the same network,” said co-lead author Mihály Vöröslakos, MD, PhD. “Our discovery was made possible because for the first time, we were able to record hundreds of individual neurons across all the key regions simultaneously in animals that were moving around naturally.”
Implications for Disease and AI
The system identified in this research may help explain how memory disorders begin. In Alzheimer’s disease, for example, the hippocampal circuits examined here are some of the earliest to deteriorate. These networks are essential for sorting and organizing new experiences.
“Our discovery of a ‘memory switchboard’ deep in the hippocampus may provide clues as to how memory circuits fail in Alzheimer’s disease,” said co-senior author Zhe S. Chen, PhD, a professor in the Departments of Psychiatry and Neuroscience at NYU Grossman School of Medicine.
Artificial intelligence systems have a hard time holding onto what they’ve already learned when faced with new tasks, a problem researchers call catastrophic forgetting. The human brain appears able to update memories without catastrophic forgetting by altering the use of existing neural circuits. By studying how our brains pull this off, scientists hope to design AI that can keep learning new things without forgetting the old.
“By showing how the mammalian brain can safeguard memories during learning, our research may offer a biological blueprint for designing next-generation AI technology that can update itself continuously without overwriting what it has already acquired,” said co-senior author György Buzsáki, MD, PhD, the Biggs Professor of Neuroscience at NYU Grossman School of Medicine.
The researchers plan to investigate whether similar switchboard-like channels exist in other memory circuits. The authors caution that their findings in mice under controlled conditions do not yet allow conclusions about the human brain.
Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.
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