overview: Spending time in a new environment develops neural representation in amazing ways.
sauce: Salk Institute
Young children may believe that the moon is following them or that they can reach out and touch it. It appears much closer than proportional to the actual distance. When we move through our daily lives, we tend to think of ourselves as moving through space in a straight line.
But Salk scientists have found that time spent exploring the environment causes neural representation to grow in surprising ways.
Findings published in Nature Neuroscience On December 29, 2022, neurons in the hippocampus, essential for spatial navigation, memory, and planning, represent space in a way that conforms to nonlinear hyperbolic geometry (exponentially outward-growing 3-dimensional spread) indicates that (In other words, it looks like the inside of an hourglass inflating.)
Researchers also found that the size of the space increases with time spent in the place. The size increases logarithmically, which corresponds to the maximum increase in information processed by the brain.
This finding provides a valuable method for analyzing data on neurocognitive disorders involving learning and memory such as Alzheimer’s disease.
“Our research shows that the brain does not always work in a straight line. Instead, neural networks work along an expanding curve, analyzed using hyperbolic geometry and information theory. and can be understood,” said Professor Tatyana Sharpee Salk, Edwin K. Hunter Chair, who led the study.
“It’s exciting to see that the neural responses in this area of the brain formed a map that expanded with experience based on time spent in a particular location.” The effect was maintained even for slight time shifts when
Sharpee’s lab uses advanced computational techniques to better understand how the brain works. They recently pioneered the use of hyperbolic geometry to better understand biological signals such as olfactory molecules and olfactory perception.
In the current study, scientists have found that hyperbolic geometry also guides neural responses. Hyperbolic maps of sensory molecules and events are perceived with hyperbolic neural maps.
Spatial representation expanded dynamically in correlation with the time rats spent exploring each environment. And as the rats slowly moved through the environment, they were able to acquire more information about the space, which further developed their neural representations.
“The findings provide a new perspective on how neural representations change with experience,” says Huanqiu Zhang, a graduate student in Sharpie’s lab.
“The geometric principles identified in our study could also guide future efforts to understand neural activity in various brain systems.”
“You might think hyperbolic geometry is only applicable on a cosmic scale, but that’s not true,” says Sharpie.
“Our brains work much slower than the speed of light, which is why we observe hyperbolic effects in comprehensible rather than astronomical space. Secondly, these dynamic hyperbolic effects in the brain We want to learn more about how representations grow, interact and communicate with each other.”
Other authors include P. Dylan Rich of Princeton University and Albert K. Lee of the Howard Hughes Medical Institute’s Janelia Research Campus.
See also
About this Spatial Perception Research News
author: press office
sauce: Salk Institute
contact: Press Office – Salk Institute
image: Images credited to the Salk Institute
Original research: open access.
“Spatial representation of hippocampus exhibits hyperbolic geometry that expands with experienceby Huanqiu Zhang et al. Nature Neuroscience
overview
Spatial representation of hippocampus exhibits hyperbolic geometry that expands with experience
Everyday experience suggests that we perceive nearby distances linearly. However, the actual geometry of the spatial representation in the brain is unknown.
Here we report that neurons in the CA1 region of the rat hippocampus, which mediate spatial perception, represent space according to a nonlinear hyperbolic geometry. This geometry uses an exponential scale and produces more position information than a linear scale.
We found that the size of the representation matched the best prediction of the number of CA1 neurons. Representations expanded dynamically with the logarithm of the time the animal spent exploring the environment to accommodate the maximum mutual information it could receive. Dynamic changes followed even small changes due to changes in the animal’s running speed.
These results demonstrate how neural circuits use dynamic hyperbolic geometry to achieve efficient representations.
