scholarly journals Ontogeny of collective behavior reveals a simple attraction rule

2017 ◽  
Vol 114 (9) ◽  
pp. 2295-2300 ◽  
Author(s):  
Robert C. Hinz ◽  
Gonzalo G. de Polavieja

The striking patterns of collective animal behavior, including ant trails, bird flocks, and fish schools, can result from local interactions among animals without centralized control. Several of these rules of interaction have been proposed, but it has proven difficult to discriminate which ones are implemented in nature. As a method to better discriminate among interaction rules, we propose to follow the slow birth of a rule of interaction during animal development. Specifically, we followed the development of zebrafish, Danio rerio, and found that larvae turn toward each other from 7 days postfertilization and increase the intensity of interactions until 3 weeks. This developmental dataset allows testing the parameter-free predictions of a simple rule in which animals attract each other part of the time, with attraction defined as turning toward another animal chosen at random. This rule makes each individual likely move to a high density of conspecifics, and moving groups naturally emerge. Development of attraction strength corresponds to an increase in the time spent in attraction behavior. Adults were found to follow the same attraction rule, suggesting a potential significance for adults of other species.

2015 ◽  
Vol 137 (4) ◽  
pp. 2361-2361
Author(s):  
Simón E. Alfaro ◽  
Jorge Cellio ◽  
Maria P. Raveau ◽  
Christopher Feuillade

2015 ◽  
Author(s):  
Simon E. Alfaro ◽  
Jorge Cellio ◽  
Maria P. Raveau ◽  
Christopher Feuillade

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Erik Cuevas ◽  
Mauricio González ◽  
Daniel Zaldivar ◽  
Marco Pérez-Cisneros ◽  
Guillermo García

A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.


2012 ◽  
Vol 58 (2) ◽  
pp. 298-306 ◽  
Author(s):  
R. Alexander Bentley ◽  
Michael J. O’Brien

Abstract There is a long and rich tradition in the social sciences of using models of collective behavior in animals as jumping-off points for the study of human behavior, including collective human behavior. Here, we come at the problem in a slightly different fashion. We ask whether models of collective human behavior have anything to offer those who study animal behavior. Our brief example of tipping points, a model first developed in the physical sciences and later used in the social sciences, suggests that the analysis of human collective behavior does indeed have considerable to offer [Current Zoology 58 (2): 298–306, 2012].


PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193049 ◽  
Author(s):  
Katarína Bod’ová ◽  
Gabriel J. Mitchell ◽  
Roy Harpaz ◽  
Elad Schneidman ◽  
Gašper Tkačik

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