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Study suggests that physical processes can have hidden neural network-like abilities

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We tend to separate the brain and the muscle—the brain does the thinking; the muscle does the doing. The brain takes in complex information about the world and makes decisions, and the muscle merely executes. This has also shaped how we think about a single cell; some molecules within cells are seen as ‘thinkers’ that take in information about the chemical environment and decide what the cell needs to do for survival; separately, other molecules are seen as the ‘muscle,’ building structures needed for survival.

But a new study shows how the molecules that build structures, i.e., the muscle, can themselves do both the thinking and the doing. The study, by scientists with the University of Chicago, California Institute of Technology, and Maynooth University, was published in Nature and may suggest avenues for new ways to think about computation using the principles of physics.

“We show that a natural molecular process—nucleation—that has been studied as a ‘muscle’ for a long time can do complex calculations that rival a simple neural network,” said UChicago Assoc. Prof. Arvind Murugan, one of the two senior co-authors of the paper. “It’s an ability hidden in plain sight—the ‘doing’ molecules can also do the ‘thinking.’ Evolution can exploit this fact in cells to get more done with fewer parts, with less energy and greater robustness.”

Thinking using physics

To survive, cells need to recognize the environment they are in and respond accordingly. For example, some combinations of molecules might indicate a time of stress that requires hunkering down, while other combinations of molecules might indicate a time of plenty. However, the difference between these molecular signals can be subtle—different environments might involve the same molecules but in different proportions.

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Conceptual framework for pattern recognition by nucleation. When one set of molecules can potentially assemble multiple distinct structures, the nucleation process that selects between outcomes is responsive to high-dimensional concentration patterns. Assembly pathways can be depicted on an energy landscape (schematic shown) as paths from a basin for unassembled components that proceed through critical nucleation seeds (barriers) to a basin for each possible final structure. Seeds that colocalize high-concentration components will lower the nucleation barrier for corresponding assembly pathways. The resulting selectivity of nucleation in high-dimensional self-assembly is sufficiently expressive to perform complex pattern recognition in a manner analogous to neural computation. Credit: Nature (2024). DOI: 10.1038/s41586-023-06890-z © Provided by Phys.org

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Click the link below for the article:

https://www.msn.com/en-us/news/technology/study-suggests-that-physical-processes-can-have-hidden-neural-network-like-abilities/ar-AA1nbYts?cvid=9d22edbbd31541b9933236e086d5ff5e&ocid=winp2fptaskbarhover&ei=6&sc=shoreline

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