Life from the Thermodynamic Perspective: Unraveling Temporal Asymmetry
Life is perceived as a system perpetually defying equilibrium in thermodynamics, steadfastly opposing the relentless march toward chaos. This defiance embodies the essence of irreversibility, establishing an indelible connection between life and the ‘arrow of time,’ a concept introduced by the English physicist Arthur Eddington in 1927.
Unlocking Temporal Asymmetry
A groundbreaking effort by an international team, including researchers from Kyoto University, Hokkaido University, and the Basque Center for Applied Mathematics, has culminated in a significant breakthrough regarding temporal asymmetry.
This newfound knowledge promises to enhance our comprehension of the intricate behaviors within biological systems, machine learning, and AI tools.
The Solution to Entropy Production
Co-author Miguel Aguilera from the Basque Center for Applied Mathematics explains, “The study offers, for the first time, an exact mathematical solution of the temporal asymmetry—also known as entropy production—of nonequilibrium disordered Ising networks.”
The Intricacies of Ising Networks
The researchers focused on the Ising model, a prototype of large-scale complex networks. This model is a valuable tool for investigating recurrently connected neurons.
When connections between neurons are symmetric in equilibrium, the Ising model manifests complex disordered states known as spin glasses. It’s worth noting that the mathematical solution of this equilibrium state earned Giorgio Parisi the prestigious 2021 Nobel Prize in Physics.
Diving into Time Irreversibility
Unlike the dynamic equilibrium in living systems, the Ising model’s spin crystals follow reversible dynamics. The researchers began understanding the time-irreversible Ising dynamics triggered by asymmetric connections between neurons.
A Leap Forward for Artificial Neural Networks
The exact solutions from this research now serve as benchmarks for developing approximate methods to learn artificial neural networks. This advancement can potentially revolutionize the field of machine learning and its various phases.
Hideaki Shimazaki, from Kyoto University’s Graduate School of Informatics, emphasizes the significance of their findings: “The Ising model underpins recent advances in deep learning and generative artificial neural networks. So, understanding its behavior offers critical insights into biological and artificial intelligence.”
A Collaborative Leap Forward
Miguel Aguilera comments on their collaborative endeavor: “Our findings are the result of an exciting collaboration involving insights from physics, neuroscience, and mathematical modeling. The multidisciplinary approach has opened the door to novel ways to understand the organization of large-scale complex networks and perhaps decipher the thermodynamic arrow of time.”
Read the original article on PHYS.
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