In this project the PI will study swarms experimentally and theoretically from the perspective of non-equilibrium statistical physics. The project will implement a fully controlled laboratory setup for interactive swarming experiments with fish and also use previously collected data on the collective motion of locusts. The project will contrast both systems using detailed, and idealized, swarming models to study the specificities and universality of their dynamics. Statistical physics concepts will be applied to search for generic characteristics of swarms that stem from common survival constraints. Detailed digital tracking of all individuals in a swarm will be used to investigate biologically meaningful swarming states. Comparisons will be made with equilibrium and non-equilibrium physical systems such as liquid crystals, spin-systems and granular materials. The project will achieve this by: 1. Building controlled laboratory experiments where hundreds, or thousands, of fish will be tracked concurrently and their response to stimuli recorded. 2. Developing detailed, and idealized, models to capture specific and universal aspects of swarms, respectively, and to develop new (physics-inspired) analytical tools for swarms such as correlation lengths, clustering, information flow, defect dynamics etc. 3. Investigate collective effects that are typical of non equilibrium dynamical systems such as scaling-laws, pattern formation, or transitions. 4. Testing if some swarming states follow a generic behavior due to the system adaptations and, if so, exploiting the consequences of this for theoretical analysis. The research will improve our understanding of swarming systems in particular and self-organization in general by combining experimental and theoretical approaches from biological and physical perspectives. It will set up new experiments and statistical measurements that can be used beyond the proposed context. It will explore the interactions between mechanical effects and behavioral adaptation across scales, furthering our understanding of non-equilibrium systems. In addition we will pursue a universal description of certain swarming states by hypothesizing that evolved critical-like behavior may exist. The interdisciplinary and collaborative nature of this project will be emphasized through the development of a strong outreach program for the public understanding of science. Swarming demonstrations capture the imagination and allow meaningful and creative interactions allowing the fundamental principles of collective behavior to be explored from a range of complimentary perspectives.
Collective behavior is a fundamental feature of biological systems, from the structure and functioning of tissues, to that of schooling fish, flocking birds and even our own human society. Despite this ubiquity we still have only a relatively rudimentary understanding of why collective behavior is so prevalent in nature and how it is achieved. These key questions were addressed in our project "Experimental and Theoretical Analysis of Collective Dynamics in Swarming Systems". Collective properties often result from an interplay between dynamic processes that occur across multiple scales of organization. This gives rise to very rich behavior, but the underlying rules become hard to determine. Animal groups, such as bird flocks, fish schools and insect swarms frequently exhibit complex coordinated collective motion that results from social interactions. Since we can readily observe and manipulate these systems, they provide an excellent opportunity to study collective biological dynamics from the perspective of statistical physics and reveal fundamental principles that underlie collective behavior. This is of widespread importance in a range of biological problems, from describing collective migration and managing fish populations to better understanding how and why tumor cells invade the body. In order to study the dynamics of animal groups, we developed a range of new technologies including computer vision software that allowed us to accurately track the motion of several hundred schooling fish in large laboratory arenas. To understand how individuals translate sensory information into motion, we employed raycasting to reconstruct the visual fields of all individuals. We investigated how groups form and function in a range of relevant ecological contexts, including predator evasion and while tracking complex environmental gradients. In particular we wanted to understand if groups can, through social interactions, form the substratum for higher-order collective intelligence. In addition we explored how social interactions evolved in response to risk of attack, for which we created a novel setup where real predators can attack, and exert selection pressure on, a virtual prey population. We made several important discoveries in this project, which resulted in a number of publications in leading scientific journals, including Science, Proceedings of the National Academy of Sciences (PNAS) and Current Biology. Our work was also featured extensively in the public domain, including The Economist, TIME, Wired, National Geographic, the New York Times, CNN, NPR and the Wall Street Journal. We demonstrated how uninformed individuals, or those without strong preferences regarding the outcome of decisions, play a crucial role in maintaining democratic consensus in groups (Science, 2011). Furthermore, such individuals increase the speed and sensitivity of collective decision-making (PNAS, 2012). We revealed how leaders evolve in cellular and animal groups, and the large impact they have on collective migration (PNAS, 2010; Journal of Statistical Physics, 2013) as well as how the visual sensing network in groups allows leaders to transfer information to others in a highly effective way (PNAS, 2013; Current Biology, 2013). We developed new computational models that liken swarms to standard physical systems such as the Ising model of ferromagnetism; a new Adaptive Networks approach that focuses on the network of interactions between agents (Science, 2011); an active elastic sheet model that evidenced a novel mechanism for self-organization and collective motion, which does not require explicit aligning interactions between agents and is instead based on standard elasticity dynamics; a simple variable-speed model (Physical Review E, 2012) and experiments (PNAS, 2010; PLoS Computational Biology, 2013) that show how swarming states change when individuals can change their self-propulsion speed. We also showed that coordinated collective motion evolves to inhibit predator’s targeting of prey (Science, 2012) and that animal groups can sense and respond to complex environmental gradients (e.g. light, temperature, salinity) even though no individual within the group is aware of the gradient (PNAS, 2009; Science, 2013). Our studies greatly improved our understanding of collective motion in animal groups, in particular, and of self-organization, in general, by combining experimental and theoretical approaches using biological and physical perspectives. The project also produced results that go beyond the specific fields targeted in the proposal. For example, it helped develop our understanding of human locomotion and collective decision-making. It also provided bio-inspired decentralized control algorithms implemented by collaborators in swarm robotic systems. The PIs presented this work at scientific meetings and in public lectures, such as at the Museum of Natural History, Harvard University, The Wellcome Collection in London, The Wisconsin Institute for Discovery, and National Geographic’s Live event "Locust swarms and ozone holes" with the PI and Nobel Laureate Mario Molina. It was also featured on the international BBC World Service and several television programs including PBS NOVA and the BBC shows "Dara O’Briain’s Science Club" and "The Code".
