When the brain tracks time, it must decide whether its regions should rely on a shared timing signal or tack time separately. A recent study in Nature Communications provides evidence that the brain uses both approaches, switching between them depending on the situation.
A group at the Institute of Science Tokyo simultaneously measured the activity of thousands of neurons in two mouse brain 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.