Regensburg 2025 – wissenschaftliches Programm
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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 7: Focus Session: Self-Regulating and Learning Systems: from Neural to Social Networks
SOE 7.3: Vortrag
Mittwoch, 19. März 2025, 10:15–10:30, H45
Dynamical theory for adaptive biological systems — •Tuan Pham1 and Kunihiko Kaneko2 — 1Institute of Physics, University of Amsterdam, Science Park 904, Amsterdam, The Netherlands — 2Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, Copenhagen, 2100-DK, Denmark
Biological, social and neural networks are adaptive - their connections slowly change in response to the state of their constituting elements--the nodes, such as genes, individuals or neurons so as to make the collective states functionally robust under environmental stochasticity. To explain this kind of robust behavior, we develop an exact analytical theory for non-equilibrium phase transitions in multi-timescale and multi-agent dynamical systems, where there exists a correspondence between global adaptation and local learning. As an illustration of our general theory, we apply it to biological evolution, where phenotypes are shaped by gene-expression fast dynamics that are subjected to an external noise while genotypes are encoded by the configurations of a network of gene regulations. This network slowly evolves under natural selection with a mutation rate, depending on how adapted the shaped phenotypes are. Here we show how, high reciprocity of network interactions results in a trade-off between genotype and phenotype that, in turn, gives rise to a robust phase within an intermediate level of external noise. Reference: Tuan Minh Pham and Kunihiko Kaneko J. Stat. Mech. (2024) 113501.
Keywords: Local Learning; Adaptation; Path Integral; Genotype-Phenotype Map; Phenotypic Robustness