Scientists have discovered a way to distort the perception of time in the brain

Scientists from the Learning Lab at the Champalimo Research Center have discovered a method to manipulate the perception of time in the brain by controlling the neural activity of rats. The new development could be applied to treat diseases such as Parkinson’s and Huntington’s, and could also impact the fields of robotics and learning algorithms.

In a new study published in the journal Nature Neuroscience, scientists artificially slowed down or sped up patterns of neural activity in rats, distorting their judgment of the length of time. This is the most compelling evidence for the influence of the brain’s “internal clock” on behavior.

clock hypothesis

The brain maintains a decentralized and flexible perception of time, shaped by the dynamics of neural networks distributed throughout the brain. Joe Paton, senior author of the study, likens it to a rock thrown into a pond. “Each time a rock falls, it creates waves that radiate outward from the surface in a repeatable pattern. Based on these patterns, it can be inferred when and where the rock was thrown into the water.”

Manipulating time with temperature

To establish a causal relationship, the team turned to an old technique in the neuroscientist’s arsenal: temperature. Researchers have developed a thermoelectric device to focus heating or cooling the striatum of rats while simultaneously recording neural activity.

Under the influence of changes in temperature, the rats began to perceive the same time interval either shorter or longer. For example, heating the striatum accelerated the dynamics of the striatum population, much like speeding up the hands of a clock, causing rats to judge a given time interval as being longer than it actually was.

The results of the study indicate that the striatum plays a key role in determining “what” and “when” to do, while other brain structures solve the second task – “how” to control the current movement.

Implications and future directions

The results of the study may help develop new therapeutic targets for severe diseases such as Parkinson’s and Huntington’s disease. In addition, the results may affect the algorithms used in robotics and education.