DOCSLIB.ORG

Simulation of Collective Motion of Self Propelled Particles in Homogeneous and Heterogeneous Medium

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Simulation of Collective Motion of Self Propelled Particles in Homogeneous and Heterogeneous Medium IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.11, November 2018 109 Simulation of Collective Motion of Self Propelled Particles in Homogeneous and Heterogeneous Medium Israr Ahmed†, Inayatullah Soomro††, Syed Baqer Shah†††, Hisamuddin Shaikh†††† SALU Khairpur Pakistan Abstract Large groups of animals moves very long distances and The concept of self-propelled particles is used to study the they cross rivers and forest [5]. Inspite of these facts, there collective motion of different organisms such as flocking of birds, has been limited research done on the effect of swimming of schools of fish or migrating of bacteria. The heterogeneous media on the collective behaviour of self- collective motion of self-propelled particles is investigated in the propelled particles [6]. Avoidance behaviour of the presence of obstacles and without obstacles. A comparison of the effects of interaction radius, speed and noise on the collective particle from the obstacle was simulated by the croft et al motion of self-propelled particles is conducted. It is found that in [7]. In this work measurement of effect was carried out the presence of obstacles, mean square displacement of the when a single particle collided with the static obstacles. It particles shows large fluctuation, whereas without obstacles was found that there are higher chances of collision of fluctuation is less. It is also shown that in the presence of the social interactions with the obstacles. This is due to the obstacles, an optimal noise, which maximizes the collective huge supposition and the occurrence of large parameter motion of the particles, exists values. Moreover, the creation of motion due to the social Key words: interaction produces key effects on the metrics used to Self-propelled particles, Collective motion, Obstacles. inform management and policy decisions.Mecholsky et al [8] worked on a continuous model in which flocks of birds were considered. Study was done on the linearized 1. Introduction interaction of the flocks with an obstacle. Behaviour of the Movements in dynamic and complex environments are an flocks was shown by the density disturbance when there integral part of the daily life activities; this includes was a interaction with the obstacles. The disturbance was walking in crowded spaces, playing sports, etc. Many of similar to the Mach cones where order was demonstrated these tasks that humans perform in such environments by the anistropic spread of waves of flocking. Chepizkho involve interactions with stationary or moving obstacles. In et al [9] focused on the dynamics of the self-propelled this context, it is important to note that there is always a particles in the heterogeneous medium. In their model need to coordinate the information with other individuals obstacles were randomly distributed in two dimensional to deal with the obstacles [1]. There are other examples spaces. In this model, particle showed avoidance from the obstacle, and the particle’s avoiding behaviour was also exist where living things deal with the obstacles such γ as flocks of birds facing obstacles while moving expressed by the turning speed . The mean square collectively. Understanding the detection and avoidance displacement of the SPPs produced two regimes as a from the obstacles when there is noise in the system is of function of obstacle density ρo and the particle’s turning extreme importance. Many studies have addressed the speed γ . It was found that, in first regime small value of interaction of individuals with obstacles from different γ produces diffusive movement of particle and perspectives. For example, in computer science field several studies have been carried out where robots interact determined by diffusive coefficient which demonstrated a with obstacles [2-4]. There are limited studies done to least at an intermediate obstacle density ρo . In second investigate the dynamics of natural systems which deal regime, it was found that for large obstacle densities and with the obstacles. Dynamics of the particles in the for higher values of γ , spontaneous trapping of the SPPs presence of the obstacles is often observed. Complex occurred. In their model it was also shown that the collective behaviour is exhibited by the bacteria in the presence of fixed and moving obstacles can change the presence of the obstacles. For example, collective dynamics of the collective behaviour of the particles. behaviour of bacteria was found in the heterogeneous Moreover, optimal noise amplitude took place which medium such as highly complex tissues in gastrointestinal maximised the collective motion. This kind of optimality tract or soil. does not appear in the homogeneous medium. Due to the smaller obstacle densities, order parameter showed a single Manuscript received November 5, 2018 Manuscript revised November 20, 2018 110 IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.11, November 2018 critical point, under this point the model showed long and direction of the particle is given by the following range order akin to the homogeneous medium. In the case equation: of large obstacle densities two critical points appeared and θ (t + ∆t) = θ (t) + h(x ) +η∆θ (2) made the motion disordered at both smaller and larger i r i noise amplitudes. Furthermore, there was the existence of In equation 1, x represents the position of ith particle, i quasi long range order in between these critical points [5]. In this research work, computer simulation is done for the vi (t) is the velocity of the particle with absolute self-propelled particles in homogeneous media and in velocity v . ∆t is the time interval that particles take to heterogeneous media. Here the term heterogeneous applies o when the collective motion of the particles is investigated move from one point to another. in the presence of the obstacles whereas the term In equation 2, θ represents the direction of the particle i homogeneous is consideredWhen collective behaviour of and ∆θ is the random fluctuation in the system, which is the particles is studied without obstacles. We compare the effects of the interaction radius, speed and noise on the created by noiseand has value in the range of [−π ,π ].η collective motion of self-propelled particles with and is the noise amplitude. θ (t) represents the average without obstacles. It is found that, in the existence of the r obstacles, mean square displacement of the particles direction of the particles which is within the interaction r r exhibit huge fluctuations in the system, whereas without radius , where is the radius of interaction between the obstacle density this displacement of the SPPs shows very self-propelled particles. θ (t) is given in the following r less fluctuating behaviour. It is also observed that in the equation: existence of the obstacles, optimal noise exists which θ (t) = arctan{ sin(θ ) cos(θ ) } (3) maximises the collective motion of the particles. Optimal r r r noise that increases the collective motion is helpful in Equation 3 has been taken from [10].The function h(x ) developing and understanding migration and navigation i strategies in moveable or non-moveable heterogeneous in equation 2 defines the interaction of particle with media, which help to understand evolution and adaptation of stochastic components in natural systems which show obstacles. Through this function particles avoid the collective motion. obstacles that are located in its neighborhood: γ 0 ∑ sin(αθki, −> i) ,if no (x i ) 0 n ()x −< 2. Methodology oixyikR o h()xi = A two dimensional continuum time model is introduced for 00, if noi()x = . N self-propelled Particles (SPPs). The model b demonstrates the effects of various parameters on the (4) collective behaviour of the SPPs. Motion of the particles is The equation 4 is taken from [5]. Through this equation restricted to the two dimensional box of size L having periodic boundary conditions. Particles interact with each obstacle is introduced. Here xi is the position of ith particle, other according to the same rule as in Vicsek et al [10] and y k is the position of kth obstacle. Ro is known as the where particle follows the average direction of the interaction radius between particle and obstacle. neighbours within the radius r .Spatial heterogeneity is no (xi ) represents the number of the obstacles located at given by the presence of the No fixed obstacles. The new element in the equation of motion of self-propelled the distance less than Ro from xi . In the above equation particles is introduced by the obstacle avoidance two conditions are given. Firstly, if is greater than interaction as it is given in Ref. [5]. Obstacles are no (xi ) randomly distributed in the system. Noise parameter is also zero, h(xi ) will show interaction with obstacles, secondly, introduced in the system, which is randomly given and has values between [−π ,π ] . At the initial time-step each if no (xi ) is equal to zero, h(xi ) will be zero. In equation particle has a random position and a random direction. 4, the term | xi − y k |< Ro , means that if the distance Particles update their positions as follows: between the obstacle and particle is less than the xi (t + ∆t) = xi (t) + vi (t)∆t, (1) interaction radius Ro , then the summation of sine IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.11, November 2018 111 3. Results and Discussion (∑sin(α k,i −θi )) will take place. Number of sine values that will be summed, depends on the number of Simulation was performed in a square box of length L. We obstacles n ( ) that are located in the interaction radius η = o xi first considered the case in which noise was, 0.01.At initial time steps particles moved randomly with constant range of the particle xi .
Load more

Recommended publications
  • Individual Versus Collective Cognition in Social Insects
    Individual versus collective cognition in social insects Ofer Feinermanᴥ, Amos Kormanˠ ᴥ Department of Physics of Complex Systems, Weizmann Institute of Science, 7610001, Rehovot, Israel. Email: [email protected] ˠ Institut de Recherche en Informatique Fondamentale (IRIF), CNRS and University Paris Diderot, 75013, Paris, France. Email: [email protected] Abstract The concerted responses of eusocial insects to environmental stimuli are often referred to as collective cognition on the level of the colony.To achieve collective cognitiona group can draw on two different sources: individual cognitionand the connectivity between individuals.Computation in neural-networks, for example,is attributedmore tosophisticated communication schemes than to the complexity of individual neurons. The case of social insects, however, can be expected to differ. This is since individual insects are cognitively capable units that are often able to process information that is directly relevant at the level of the colony.Furthermore, involved communication patterns seem difficult to implement in a group of insects since these lack clear network structure.This review discusses links between the cognition of an individual insect and that of the colony. We provide examples for collective cognition whose sources span the full spectrum between amplification of individual insect cognition and emergent group-level processes. Introduction The individuals that make up a social insect colony are so tightly knit that they are often regarded as a single super-organism(Wilson and Hölldobler, 2009). This point of view seems to go far beyond a simple metaphor(Gillooly et al., 2010)and encompasses aspects of the colony that are analogous to cell differentiation(Emerson, 1939), metabolic rates(Hou et al., 2010; Waters et al., 2010), nutrient regulation(Behmer, 2009),thermoregulation(Jones, 2004; Starks et al., 2000), gas exchange(King et al., 2015), and more.
    [Show full text]
  • Behavioural Plasticity and the Transition to Order in Jackdaw Flocks
    ARTICLE https://doi.org/10.1038/s41467-019-13281-4 OPEN Behavioural plasticity and the transition to order in jackdaw flocks Hangjian Ling 1,2, Guillam E. Mclvor 3, Joseph Westley3, Kasper van der Vaart1, Richard T. Vaughan4, Alex Thornton 3* & Nicholas T. Ouellette 1* Collective behaviour is typically thought to arise from individuals following fixed interaction rules. The possibility that interaction rules may change under different circumstances has 1234567890():,; thus only rarely been investigated. Here we show that local interactions in flocks of wild jackdaws (Corvus monedula) vary drastically in different contexts, leading to distinct group- level properties. Jackdaws interact with a fixed number of neighbours (topological interac- tions) when traveling to roosts, but coordinate with neighbours based on spatial distance (metric interactions) during collective anti-predator mobbing events. Consequently, mobbing flocks exhibit a dramatic transition from disordered aggregations to ordered motion as group density increases, unlike transit flocks where order is independent of density. The relationship between group density and group order during this transition agrees well with a generic self- propelled particle model. Our results demonstrate plasticity in local interaction rules and have implications for both natural and artificial collective systems. 1 Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA. 2 Department of Mechanical Engineering, University of Massachusetts Dartmouth, North Dartmouth,
    [Show full text]
  • Collective Motion from Local Attraction Daniel Strömbom
    Collective motion from local attraction Daniel Strömbom To cite this version: Daniel Strömbom. Collective motion from local attraction. Journal of Theoretical Biology, Elsevier, 2011, 283 (1), pp.145. 10.1016/j.jtbi.2011.05.019. hal-00719499 HAL Id: hal-00719499 https://hal.archives-ouvertes.fr/hal-00719499 Submitted on 20 Jul 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Author’s Accepted Manuscript Collective motion from local attraction Daniel Strömbom PII: S0022-5193(11)00261-X DOI: doi:10.1016/j.jtbi.2011.05.019 Reference: YJTBI6483 To appear in: Journal of Theoretical Biology www.elsevier.com/locate/yjtbi Received date: 15 September 2010 Revised date: 4 May 2011 Accepted date: 9 May 2011 Cite this article as: Daniel Strömbom, Collective motion from local attraction, Journal of Theoretical Biology, doi:10.1016/j.jtbi.2011.05.019 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form.
    [Show full text]
  • Inferring the Structure and Dynamics of Interactions in Schooling Fish
    Inferring the structure and dynamics of interactions in schooling fish Yael Katza, Kolbjørn Tunstrøma, Christos C. Ioannoua, Cristián Huepeb, and Iain D. Couzina,1 aDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544; and b614 North Paulina Street, Chicago, IL 60622 Edited* by Simon A. Levin, Princeton University, Princeton, NJ, and approved June 28, 2011 (received for review May 12, 2011) Determining individual-level interactions that govern highly coor- Recent empirical studies (19–26) have collected large datasets dinated motion in animal groups or cellular aggregates has been a of freely interacting individuals in order to infer the rules under- long-standing challenge, central to understanding the mechanisms lying their emergent collective motion. Ballerini et al. (22) and and evolution of collective behavior. Numerous models have been Cavagna et al. (26) have used the spatial structure of starling flocks proposed, many of which display realistic-looking dynamics, but to infer that starlings use topological rather than metric interac- nonetheless rely on untested assumptions about how individuals tions and that information is transferred over large distances within integrate information to guide movement. Here we infer behavior- flocks in a scale-free manner. High-temporal-resolution data from al rules directly from experimental data. We begin by analyzing tra- several species have been analyzed by employing model-based jectories of golden shiners (Notemigonus crysoleucas) swimming approaches. Lukeman et al. (25) fit data on the spatial conurations in two-fish and three-fish shoals to map the mean effective forces of surf scoters to a zonal model and identified best-fit parameter as a function of fish positions and velocities.
    [Show full text]
  • Collective Turns in Jackdaw Flocks: Kinematics and Information Transfer
    ORE Open Research Exeter TITLE Collective turns in jackdaw flocks: kinematics and information transfer AUTHORS Ling, H; Mclvor, GE; Westley, J; et al. JOURNAL Journal of The Royal Society Interface DEPOSITED IN ORE 01 November 2019 This version available at http://hdl.handle.net/10871/39459 COPYRIGHT AND REUSE Open Research Exeter makes this work available in accordance with publisher policies. A NOTE ON VERSIONS The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication Collective turns in jackdaw flocks Collective turns in jackdaw flocks: kinematics and information transfer Hangjian Ling1,4, Guillam E. Mclvor2, Joseph Westley2, Kasper van der Vaart1, Jennifer Yin1, Richard T. Vaughan3, Alex Thornton2*, Nicholas T. Ouellette1* 1Department of Civil and Environmental Engineering, Stanford University, Stanford, CA USA; 2Center for Ecology and Conservation, University of Exeter, Penryn, UK; 3School of Computing Science, Simon Fraser University, Burnaby, Canada 4Department of Mechanical Engineering, University of Massachusetts Dartmouth, North Dartmouth, MA USA; Correspondence: Nicholas T. Ouellette, Email: [email protected] Alex Thornton, Email: [email protected] Abstract: The rapid, cohesive turns of bird flocks are one of the most vivid examples of collective behaviour in nature, and have attracted much research. 3D imaging techniques now allow us to characterise the kinematics of turning and their group-level consequences in precise detail. We measured the kinematics of flocks of wild jackdaws executing collective turns in two contexts: during transit to roosts and anti-predator mobbing. All flocks reduced their speed during turns, likely due to constraints on individual flight capability.
    [Show full text]
  • Algorithmic Requirements for Swarm Intelligence in Differently Coupled Collective Systems
    Chaos, Solitons & Fractals 50 (2013) 100–114 Contents lists available at SciVerse ScienceDirect Chaos, Solitons & Fractals Nonlinear Science, and Nonequilibrium and Complex Phenomena journal homepage: www.elsevier.com/locate/chaos Algorithmic requirements for swarm intelligence in differently coupled collective systems Jürgen Stradner a, Ronald Thenius a, Payam Zahadat a, Heiko Hamann a,b, Karl Crailsheim a, ⇑ Thomas Schmickl a, a Artificial Life Laboratory at the Department of Zoology, Karl-Franzens University Graz, Universitätsplatz 2, A-8010 Graz, Austria b Department of Computer Science, University of Paderborn, Zukunftsmeile 1, 33102 Paderborn, Germany article info abstract Article history: Swarm systems are based on intermediate connectivity between individuals and dynamic Available online 7 March 2013 neighborhoods. In natural swarms self-organizing principles bring their agents to that favorable level of connectivity. They serve as interesting sources of inspiration for control algorithms in swarm robotics on the one hand, and in modular robotics on the other hand. In this paper we demonstrate and compare a set of bio-inspired algorithms that are used to control the collective behavior of swarms and modular systems: BEECLUST, AHHS (hormone controllers), FGRN (fractal genetic regulatory networks), and VE (virtual embryo- genesis). We demonstrate how such bio-inspired control paradigms bring their host sys- tems to a level of intermediate connectivity, what delivers sufficient robustness to these systems for collective decentralized control. In parallel, these algorithms allow sufficient volatility of shared information within these systems to help preventing local optima and deadlock situations, this way keeping those systems flexible and adaptive in dynamic non-deterministic environments. Ó 2013 Elsevier Ltd.
    [Show full text]
  • Intermittent Collective Dynamics Emerge from Conflicting Imperatives in Sheep Herds
    Intermittent collective dynamics emerge from conflicting imperatives in sheep herds Francesco Ginellia,1,2, Fernando Peruanib,2, Marie-Helène Pillotc,d, Hugues Chatée,f, Guy Theraulazc,d, and Richard Bonc,d aPhysics Department and Institute for Complex Systems and Mathematical Biology, King’s College, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom; bLaboratoire J.A. Dieudonné, Université de Nice Sophia Antipolis, UMR CNRS 6621, 06108 Nice Cedex 02, France; cCentre de Recherches sur la Cognition Animale, UMR-CNRS 5169, Universit Paul Sabatier, 31062 Toulouse Cedex 9, France; dCNRS, Centre de Recherches sur la Cognition Animale, F-31062 Toulouse, France; eService de Physique de l’État Condensé, CNRS UMR 3680, Commissariat à l’énergie atomique et aux énergies alternatives (CEA)–Saclay, 91191 Gif-sur-Yvette, France; and fBeijing Computational Science Research Center, Beijing 100094, China Edited by Giorgio Parisi, University of Rome, Rome, Italy, and approved August 18, 2015 (received for review February 25, 2015) Among the many fascinating examples of collective behavior few individuals that propagates to the whole group. In many exhibited by animal groups, some species are known to alternate cases, the behavioral shift occurs without any perceived threat in slow group dispersion in space with rapid aggregation phe- the neighborhood, resulting in a spontaneous transition at the nomena induced by a sudden behavioral shift at the individual collective level that can be interpreted as a consequence of ran- level. We study this phenomenon quantitatively in large groups of dom individual decisions. Such alternating behavioral phases at grazing Merino sheep under controlled experimental conditions. the collective level have also been reported in refs.
    [Show full text]
  • Using Activity and Sociability to Characterize Collective Motion Rstb.Royalsocietypublishing.Org David J
    Downloaded from http://rstb.royalsocietypublishing.org/ on August 9, 2018 Using activity and sociability to characterize collective motion rstb.royalsocietypublishing.org David J. T. Sumpter1,†, Alex Szorkovszky1,†, Alexander Kotrschal2, Niclas Kolm2 and James E. Herbert-Read2 1Mathematics Department, Uppsala University, Uppsala, Sweden Review 2Zoology Department, Stockholm University, Stockholm, Sweden DJTS, 0000-0002-1436-9103; AS, 0000-0001-9331-3214; AK, 0000-0003-3473-1402; Cite this article: Sumpter DJT, Szorkovszky A, JEH-R, 0000-0003-0243-4518 Kotrschal A, Kolm N, Herbert-Read JE. 2018 Using activity and sociability to characterize A wide range of measurements can be made on the collective motion of collective motion. Phil. Trans. R. Soc. B 373: groups, and the movement of individuals within them. These include, but are not limited to: group size, polarization, speed, turning speed, 20170015. speed or directional correlations, and distances to near neighbours. From http://dx.doi.org/10.1098/rstb.2017.0015 an ecological and evolutionary perspective, we would like to know which of these measurements capture biologically meaningful aspects of an Accepted: 11 December 2017 animal’s behaviour and contribute to its survival chances. Previous simu- lation studies have emphasized two main factors shaping individuals’ behaviour in groups; attraction and alignment. Alignment responses One contribution of 16 to a theme issue appear to be important in transferring information between group members ‘Collective movement ecology’. and providing synergistic benefits to group members. Likewise, attraction to conspecifics is thought to provide benefits through, for example, selfish Subject Areas: herding. Here, we use a factor analysis on a wide range of simple measure- ments to identify two main axes of collective motion in guppies (Poecilia behaviour, ecology, evolution reticulata): (i) sociability, which corresponds to attraction (and to a lesser degree alignment) to neighbours, and (ii) activity, which combines Keywords: alignment with directed movement.
    [Show full text]
  • Collective Motion in Biological Systems Andrea Deutsch, Peter Friedl, Luigi Preziosi, Guy Theraulaz
    Collective motion in biological systems Andrea Deutsch, Peter Friedl, Luigi Preziosi, Guy Theraulaz To cite this version: Andrea Deutsch, Peter Friedl, Luigi Preziosi, Guy Theraulaz. Collective motion in biological systems. Interface Focus, Royal Society publishing, 2012, 2 (6), pp.689-692. 10.1098/rsfs.2012.0048. hal- 02325148 HAL Id: hal-02325148 https://hal.archives-ouvertes.fr/hal-02325148 Submitted on 5 Nov 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Multi-scale analysis and modelling of collective migration in biological systems Andrea Deutsch1, Peter Friedl2,3,4, Luigi Preziosi5 & Guy Theraulaz6,7,8 1 Dept. Innovative Methods of Computing, Center for Information Services & High Performance Computing, Technische Universität Dresden, Dresden, Germany 2 Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, Netherlands, 3 Cancer Genomics Center, Utrecht, Netherlands 4 Department of Genitourinary Medicine, University of Texas MD Anderson Cancer Center, Houston, United States
    [Show full text]
  • Collective Cognition in Animal Groups
    Review Collective cognition in animal groups Iain D. Couzin Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA The remarkable collective action of organisms such as with mechanisms of decision-making within the brain swarming ants, schooling fish and flocking birds has [1,6]. Although many details differ, there is good reason long captivated the attention of artists, naturalists, phi- for increased communication between researchers inter- losophers and scientists. Despite a long history of scien- ested in collective animal behavior and those in cognitive tific investigation, only now are we beginning to science. decipher the relationship between individuals and group-level properties. This interdisciplinary effort is Collective motion beginning to reveal the underlying principles of collec- It is usually not possible to scale reliably from individual to tive decision-making in animal groups, demonstrating group behavior through verbal argument alone. Con- how social interactions, individual state, environmental sequently, considerable progress in revealing the prin- modification and processes of informational amplifica- ciples of collective behavior has been made using tion and decay can all play a part in tuning adaptive mathematical modeling techniques, such as computer response. It is proposed that important commonalities simulation (Box 1). Some of the earliest theoretical exist with the understanding of neuronal processes and approaches were inspired by particle physics [2,7]. These that much could be learned by considering collective introduced the influential concept of using equations to animal behavior in the framework of cognitive science. characterize individual movements and interactions (as ‘social forces’), the aim being to explore whether (and if so, Introduction how) individual behaviors can scale to the coherent collec- It is little wonder that the behavior of animal groups, tive motion exhibited by fish schools or bird flocks.
    [Show full text]
  • Analyzing Collective Motion with Machine Learning and Topology
    Analyzing collective motion with machine learning and topology Cite as: Chaos 29, 123125 (2019); https://doi.org/10.1063/1.5125493 Submitted: 23 August 2019 . Accepted: 22 November 2019 . Published Online: 18 December 2019 Dhananjay Bhaskar , Angelika Manhart , Jesse Milzman , John T. Nardini , Kathleen M. Storey , Chad M. Topaz , and Lori Ziegelmeier Chaos 29, 123125 (2019); https://doi.org/10.1063/1.5125493 29, 123125 © 2019 Author(s). Chaos ARTICLE scitation.org/journal/cha Analyzing collective motion with machine learning and topology Cite as: Chaos 29, 123125 (2019); doi: 10.1063/1.5125493 Submitted: 23 August 2019 · Accepted: 22 November 2019 · Published Online: 18 December 2019 View Online Export Citation CrossMark Dhananjay Bhaskar,1 Angelika Manhart,2 Jesse Milzman,3 John T. Nardini,4 Kathleen M. Storey,5 Chad M. Topaz,6 and Lori Ziegelmeier7,a) AFFILIATIONS 1Center for Biomedical Engineering, Brown University, Providence, Rhode Island 02912, USA 2Department of Mathematics, University College London, London WC1E 6BT, United Kingdom 3Department of Mathematics, University of Maryland, College Park, Maryland 20742, USA 4The Statistical and Applied Mathematical Sciences Institute (SAMSI), Durham, North Carolina 27709, USA 5Department of Mathematics, University of Michigan, Ann Arbor, Michigan 48105, USA 6Department of Mathematics and Statistics, Williams College, Williamstown, Massachusetts 01267, USA 7Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, Minnesota 55105, USA a)Electronic mail: [email protected] ABSTRACT We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D’Orsogna et al., Phys. Rev. Lett. 96, 104302 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as ocking and milling.
    [Show full text]
  • Collective Behavior in Animal Groups: Theoretical Models and Empirical Studies
    HFSP Journal PERSPECTIVE Collective behavior in animal groups: theoretical models and empirical studies Irene Giardina1 1Centre for Statistical Mechanics and Complexity ͑SMC͒, CNR-INFM, Department of Physics, University of Rome La Sapienza, P.le A. Moro 2, 00185 Rome, Italy and Institute for Complex Systems ͑ISC͒, CNR, Via dei Taurini 19, 00185 Rome, Italy ͑Received 27 June 2008; accepted 30 June 2008; published online 1 August 2008) Collective phenomena in animal groups have attracted much attention in the last years, becoming one of the hottest topics in ethology. There are various reasons for this. On the one hand, animal grouping provides a paradigmatic example of self-organization, where collective behavior emerges in absence of centralized control. The mechanism of group formation, where local rules for the individuals lead to a coherent global state, is very general and transcends the detailed nature of its components. In this respect, collective animal behavior is a subject of great interdisciplinary interest. On the other hand, there are several important issues related to the biological function of grouping and its evolutionary success. Research in this field boasts a number of theoretical models, but much less empirical results to compare with. For this reason, even if the general mechanisms through which self-organization is achieved are qualitatively well understood, a quantitative test of the models assumptions is still lacking. New analysis on large groups, which require sophisticated technological procedures, can provide the necessary empirical data. [DOI: 10.2976/1.2961038] CORRESPONDENCE Collective behavior in animal In all previous examples collec- Irene Giardina: [email protected] groups is a widespread phenomenon in tive behavior emerges in absence of biological systems, at very different centralized control: individual mem- scales and levels of complexity.
    [Show full text]