The brain’s ability to perceive space as the universe expands

summary: Time spent in a new environment increases neural representations in surprising ways.

Source: Salik Institute

Young children sometimes think that the moon follows them or that they can reach out and touch it. It seems much closer than proportional to the actual distance. As we move about in our daily lives, we tend to think of ourselves as navigating through space in a linear fashion.

But the Salk scientists found that time spent exploring the environment causes a sudden growth in neural representations.

The results are published in Natural neuroscience On December 29, 2022, he showed that hippocampal neurons essential for spatial navigation, memory, and planning represent space in a way consistent with nonlinear hyperbolic geometry—a three-dimensional extension that grows exponentially outward. (In other words, its shape resembles the inside of an expanding hourglass.)

The researchers also found that the size of this space increased with time spent on the site. The volume increases logarithmically in proportion to the maximum possible increase in the information processed by the brain.

This discovery offers interesting avenues for analyzing data on neurocognitive disorders involving learning and memory, such as Alzheimer’s disease.

Our study shows that the brain does not always function in a linear fashion. Instead, neural networks operate along an expanding curve, which can be analyzed and understood using hyperbolic geometry and information theory,” says Salk Professor Tatiana Charpy, the Edwin K. Hunter Professor, who led the study.

“It was exciting to see the neural responses in this brain region formed a map that experimentally expanded with time spent in a particular location. The effect persisted even with slight deviations in time as the animal ran slower or faster in the environment.”

Sharpee’s lab uses advanced computational approaches to better understand how the brain works. They have recently pioneered the use of hyperbolic geometry to better understand biological signals such as odor molecules, in addition to odor perception.

In the current study, the scientists found that the hyperbolic geometry also directs neural responses. Hyperbolic maps of sensory molecules and events are visualized through maps of hyperbolic neurons.

Spatial representations developed dynamically in association with the time the rat spent exploring each environment. As the rat moved more slowly through the environment, it gained more information about space, which increased its neural representations.

“The results provide a new perspective on how neural representations can change through experience,” says Huanqiu Zhang, a graduate student in Sharpee’s lab.

“The engineering principles identified in our study could also guide future efforts in understanding neural activity in different brain systems.”

“You might think that hyperbolic geometry only applies on a cosmic scale, but that’s not true,” says Sharpey.

Our brains operate much slower than the speed of light, which may explain why deterministic effects are observed on sensible rather than cosmic spaces. Next, we’d like to learn more about how these dynamic hyperbolic representations evolved in the brain, interacting and communicating with each other.

Other authors include B. Dylan Rich of Princeton University and Albert K. Lee is from the Janelia Research Campus at the Howard Hughes Medical Institute.

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Source: Salik Institute
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Spatial representations of the hippocampus show hyperbolic geometry that changes with experience by Huanqiu Zhang et al. Natural neuroscience

summary

Spatial representations of the hippocampus show a hyperbolic geometry that changes with experience

Everyday experience suggests that we perceive distances linearly close to us. However, the actual geometry of spatial representation in the brain is unknown.

Here, we report that neurons in the CA1 region of the rat hippocampus that mediate spatial perception represent space in a nonlinear hyperbolic geometry. This geometry uses an exponential scale and produces more positional information than a linear scale.

We found that the representation size corresponded to optimal expectations of the number of CA1 neurons. The representations also grew dynamically in proportion to the logarithm of the time the animal spent exploring the environment, corresponding to the maximum amount of mutual information that could be received. Dynamic changes followed even small differences due to changes in the animal’s running speed.

These results demonstrate how neural circuits achieve efficient representations using dynamic hyperbolic geometry.