Speaker
Description
Collective behavior of animals is a fascinating example of self-organization in biology. This phenomenon is believed to provide several advantages to individuals, such as facilitating exchange of social information, promoting accurate collective decisions, or affording protection from predators. It has been theorized that animal collectives should operate in a special parameter region close to a critical point [1], where various aspects of collective computations become optimal [2]. Here, we discuss the "criticality hypothesis" in the context of collective animal behavior by combining experimental data and individual-based modeling. First, we will analyze self-organized coordination of movement in Trichoplax adhaerens, one of the simplest multicellular animals devoid of a central nervous system [3]. Subsequently, we shift our focus to the collective response of fish to predators. Using a spatially-explicit schooling model, we will investigate the optimal collective response at the transition between order and disorder, and individual-level evolutionary adaptation as a mechanisms for self-organization towards criticality [4]. Finally, by combining experimental data from laboratory setting and field experiments with computational models, we will explore the criticality hypothesis in relation to so-called "startle cascades", which represent rapid escape responses propagating through fish schools, akin to the activity avalanches observed in neuronal systems [5,6].
[1] T. Mora, W. Biale, J Stat Phys 144, 268-302 (2011);
[2] P. Romanczuk, B.C. Daniels. "Phase transitions and criticality in the collective behavior of animals-self-organization and biological function." In Order, Disorder and Criticality: Advanced Problems of Phase Transition Theory, pp. 179-208. 2023.
[3] Davidescu et al, Proc Natl Acad Sci 120 (2023);
[4] P.P. Klamser, P. Romanczuk, PLoS Comp Biol 17, e1008832 (2021);
[5] W. Poel et al., Sci Adv 8, eabm6385 (2022);
[6] L. Gómez Nava et al., Nature Phys 19 (2023);
*This work was supported by the Deutsche Forschungsgemeinschaft (German Research Foundation): RO 4766/2-1 and under Germany's Excellence Strategy - EXC 2002/1 'Science of Intelligence' project- no. 390523135.
