The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Inherent noise can facilitate coherence in collective swarm motion
Open accessAbstract
Among the most striking aspects of the movement of many animal groups are their sudden coherent changes in direction. Recent observations of locusts and starlings have shown that this directional switching is an intrinsic property of their motion. Similar direction switches are seen in self-propelled particle and other models of group motion. Comprehending the factors that determine such switches is key to understanding the movement of these groups. Here, we adopt a coarse-grained approach to the study of directional switching in a self-propelled particle model assuming an underlying one-dimensional Fokker–Planck equation for the mean velocity of the particles. We continue with this assumption in analyzing experimental data on locusts and use a similar systematic Fokker–Planck equation coefficient estimation approach to extract the relevant information for the assumed Fokker–Planck equation underlying that experimental data. In the experiment itself the motion of groups of 5 to 100 locust nymphs was investigated in a homogeneous laboratory environment, helping us to establish the intrinsic dynamics of locust marching bands. We determine the mean time between direction switches as a function of group density for the experimental data and the self-propelled particle model. This systematic approach allows us to identify key differences between the experimental data and the model, revealing that individual locusts appear to increase the randomness of their movements in response to a loss of alignment by the group. We give a quantitative description of how locusts use noise to maintain swarm alignment. We discuss further how properties of individual animal behavior, inferred by using the Fokker–Planck equation coefficient estimation approach, can be implemented in the self-propelled particle model to replicate qualitatively the group level dynamics seen in the experimental data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“facilitate coherence”
From Stewardship of global collective behavior (2021) · cites this paper as [101]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Novel Type of Phase Transition in a System of Self-Driven Particles
Vicsek, T., Czirók, A., Ben‐Jacob, E., Cohen, I., & Shochet, O. (1995) · Physical Review Letters · doi:10.1103/physrevlett.75.1226 · Cited by 7,395
Traffic and related self-driven many-particle systems
Helbing, D. (2001) · Reviews of Modern Physics · doi:10.1103/revmodphys.73.1067 · Cited by 3,285
Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Giorgio Parisi, A., Procaccini, A., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0711437105 · Cited by 1,978
Noise enhancement of information transfer in crayfish mechanoreceptors by stochastic resonance
Douglass, J. K., Wilkens, L. A., Pantazelou, E., & Moss, F. (1993) · Nature · doi:10.1038/365337a0 · Cited by 1,433
Complexity, Pattern, and Evolutionary Trade-Offs in Animal Aggregation
Parrish, J. K., & Leah Edelstein-keshet, A. (1999) · Science · doi:10.1126/science.284.5411.99 · Cited by 1,230
From Disorder to Order in Marching Locusts
Buhl, C., Sumpter, D. J. T., Couzin, I. D., Hale, J. J., Despland, E., Miller, E. R., & Simpson, S. J. (2006) · Science · doi:10.1126/science.1125142 · Cited by 1,153
Self-citationThe principles of collective animal behaviour
Sumpter, D. J. T. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1733 · Cited by 1,019
Self-citationSelf-Organization and Collective Behavior in Vertebrates
Couzin, I. D., & Krause, J. (2003) · Advances in the study of behavior · doi:10.1016/s0065-3454(03)01001-5 · Cited by 921
Self-citationGear, C. W., Hyman, J. M., Kevrekidid, P. G., Kevrekidis, I. G., Runborg, O., & Theodoropoulos, C. (2003) · Communications in Mathematical Sciences · doi:10.4310/cms.2003.v1.n4.a5 · Cited by 778
Self-citationSelf-Organized Fish Schools: An Examination of Emergent Properties
Parrish, J. K., Viscido, S. V., & Grünbaum, D. (2002) · Biological Bulletin · doi:10.2307/1543482 · Cited by 706
Cited by
Sorted by citations
Items 101–200 of 302
Derivation of a Micro-Macro Link for Collective Decision-Making Systems
Hamann, H., Valentini, G., Khaluf, Y., & Dorigo, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10762-2_18 · Cited by 26
The role of noise in self-organized decision making by the true slime mold Physarum polycephalum
Meyer, B. E., Ansorge, C., & Nakagaki, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172933 · Cited by 26
Time delay can facilitate coherence in self-driven interacting-particle systems
Sun, Y., Lin, W. J., & Radek Erban, A. (2014) · Physical Review E · doi:10.1103/physreve.90.062708 · Cited by 26
Inverse Bayesian inference in swarming behaviour of soldier crabs
Gunji, Y., Murakami, H., Tomaru, T., & Basios, V. (2018) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2017.0370 · Cited by 25
Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories
Genkin, M., Hughes, O., & Engel, T. A. (2021) · Nature Communications · doi:10.1038/s41467-021-26202-1 · Cited by 26
Towards Swarm Calculus: Universal Properties of Swarm Performance and Collective Decisions
Hamann, H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32650-9_15 · Cited by 25
Wang, X., Ren, J., Jin, X., & Manocha, D. (2015) · ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA) · doi:10.1145/2786784.2786790 · Cited by 25
Continuification Control of Large-Scale Multiagent Systems in a Ring
Maffettone, G. C., Boldini, A., di Bernardo, M., & Porfiri, M. (2022) · IEEE Control Systems Letters · doi:10.1109/lcsys.2022.3226619 · Cited by 24
Eledlebi, K., Hildmann, H., Ruta, D., & Isakovic, A. F. (2020) · Drones · doi:10.3390/drones4030033 · Cited by 24
Eledlebi, K., Ruta, D., Hildmann, H., Saffre, F., Al-Hammadi, Y., & Isakovic, A. F. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3046184 · Cited by 25
Zhang, Z., Huangfu, W., Long, K., Zhang, X., Liu, X., & Zhong, B. (2013) · Science China Information Sciences · doi:10.1007/s11432-013-4894-6 · Cited by 23
From individual to collective behaviour of coupled velocity jump processes: A locust example
Radek Erban, A., & Haškovec, J. (2012) · Kinetic and Related Models · doi:10.3934/krm.2012.5.817 · Cited by 23
A Brief Taxonomy of Hybrid Intelligence
Pescetelli, N. (2021) · Forecasting · doi:10.3390/forecast3030039 · Cited by 24
A mathematical model for the dynamics and synchronization of cows
Sun, J., Bollt, E. M., Porter, M. A., & Dawkins, M. S. (2011) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2011.06.009 · Cited by 23
The Smallest Possible Interaction Radius for Synchronization of Self-Propelled Particles
Chen, G., Liu, Z., & Guo, L. (2014) · SIAM Review · doi:10.1137/140961249 · Cited by 23
A mathematics for medicine: The Network Effect
West, B. J. (2014) · Frontiers in Physiology · doi:10.3389/fphys.2014.00456 · Cited by 22
Biologically‐Inspired Visual Simulation of Insect Swarms
Li, W., Wolinski, D., Pettré, J., & Lin, M. C. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12572 · Cited by 21
Ergodic directional switching in mobile insect groups
Escudero, C., Yates, C. A., Buhl, C., Couzin, I. D., Radek Erban, A., Kevrekidis, I. G., & Maini, P. K. (2010) · Physical Review E · doi:10.1103/physreve.82.011926 · Cited by 21
Self-citationRefining self-propelled particle models for collective behaviour,
Yates, C. A., Baker, R. E., Radek Erban, A., & Maini, P. K. (2009) · Oxford University Research Archive (ORA) (University of Oxford) · Cited by 21
Self-citationRaghib, M., Levin, S. A., & Kevrekidis, I. G. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.02.030 · Cited by 20
Self-citationAsymptotic Analysis of a Cucker–Smale System with Leadership and Distributed Delay
Pignotti, C., & Vallejo, I. R. (2019) · Springer INdAM series · doi:10.1007/978-3-030-17949-6_12 · Cited by 20
Inherent Noise‐Aware Insect Swarm Simulation
Wang, X., Jin, X., Deng, Z., & Zhou, L. (2014) · Computer Graphics Forum · doi:10.1111/cgf.12277 · Cited by 20
Ren, J., Wang, X., Jin, X., & Manocha, D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155698 · Cited by 19
Relation between cooperative molecular motors and active Brownian particles
Touya, C., Schwalger, T., & Lindner, B. (2011) · Physical Review E · doi:10.1103/physreve.83.051913 · Cited by 19
Learning the temporal evolution of multivariate densities via normalizing flows
Lu, Y., Maulik, R., Gao, T., Dietrich, F., Kevrekidis, I. G., & Duan, J. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0065093 · Cited by 19
Self-citationPromoting collective motion of self-propelled agents by distance-based influence
Yang, H.-X., Zhou, T., & Huang, L. (2014) · Physical Review E · doi:10.1103/physreve.89.032813 · Cited by 19
Optimal information transfer and stochastic resonance in collective decision making
Meyer, B. E. (2017) · Swarm Intelligence · doi:10.1007/s11721-017-0136-7 · Cited by 19
Modeling collective motion for fish schooling via multi-agent reinforcement learning
Wang, X., Liu, S., Yu, Y., Yue, S., Liu, Y., Zhang, F., & Lin, Y. (2022) · Ecological Modelling · doi:10.1016/j.ecolmodel.2022.110259 · Cited by 21
Valentini, G., & Hamann, H. (2015) · Swarm Intelligence · doi:10.1007/s11721-015-0108-8 · Cited by 18
Soorati, M. D., Krome, M., Mora-Mendoza, M. A., Ghofrani, J., & Hamann, H. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967777 · Cited by 18
López-Incera, A., Ried, K., Müller, T., & Briegel, H. J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243628 · Cited by 18
The effect of information uncertainty in road transportation systems
Litescu, S. C., Vaisagh Viswanathan, T., Aydt, H., & Knoll, A. (2016) · Journal of Computational Science · doi:10.1016/j.jocs.2016.04.017 · Cited by 17
Cell Division Induces and Switches Coherent Angular Motion within Bounded Cellular Collectives
Siedlik, M. J., Manivannan, S., Kevrekidis, I. G., & Nelson, C. M. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.05.001 · Cited by 18
Self-citationThe lost art of mathematical modelling
Gyllingberg, L., Birhane, A., & Sumpter, D. J. T. (2023) · Mathematical Biosciences · doi:10.1016/j.mbs.2023.109033 · Cited by 18
Self-citationFluctuation-Driven Flocking Movement in Three Dimensions and Scale-Free Correlation
Niizato, T., & Gunji, Y.-P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035615 · Cited by 17
Collision-based computing implemented by soldier crab swarms
Nishiyama, Y., Gunji, Y., & Adamatzky, A. (2012) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2012.662682 · Cited by 17
Hydrodynamic Equations for Flocking Models without Velocity Alignment
Peruani, F. (2017) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.86.101010 · Cited by 17
Persistent and responsive collective motion with adaptive time delay
Chen, Z., & Zheng, Y. (2024) · Science Advances · doi:10.1126/sciadv.adk3914 · Cited by 16
A framework for modelling desert locust population dynamics and large-scale dispersal
Retkutė, R., Thurston, W., Cressman, K., & Gilligan, C. A. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012562 · Cited by 15
The selfish herd: Noise effects in Local Crowded Horizon and Voronoi models
Ose, N. J., & Ohmann, P. R. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.04.024 · Cited by 15
Milling and meandering: Flocking dynamics of stochastically interacting agents with a field of view
Bagarti, T., & Menon, S. N. (2019) · Physical review. E · doi:10.1103/physreve.100.012609 · Cited by 15
Exploration of effective potential landscapes using coarse reverse integration
Frewen, T. A., Hummer, G., & Kevrekidis, I. G. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3207882 · Cited by 15
Self-citationHorsevad, N., Kwa, H. L., & Bouffanais, R. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.865414 · Cited by 14
Realization of consensus of multi-agent systems with stochastically mixed interactions
Sun, Y., Li, W., & Zhao, D. (2016) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4958927 · Cited by 14
Declarative vs rule-based control for flocking dynamics
Mehmood, U., Paoletti, N., Phan, D., Grosu, R., Lin, S., Stoller, S. D., Tiwari, A., Yang, J., & Smolka, S. A. (2018) · ACM Symposium on Applied Computing · doi:10.1145/3167132.3167222 · Cited by 14
Opinion Dynamics With Mobile Agents: Contrarian Effects by Spatial Correlations
Hamann, H. (2018) · Frontiers in Robotics and AI · doi:10.3389/frobt.2018.00063 · Cited by 14
Khaluf, Y., Rausch, I., & Simoens, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00533-7_20 · Cited by 14
Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection
Mann, R. P., Perna, A., Strömbom, D., Garnett, R., Herbert‐Read, J. E., Sumpter, D. J. T., & Ward, A. J. W. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002308 · Cited by 13
Self-citationRetractedContinuum models of cohesive stochastic swarms: The effect of motility on aggregation patterns
Hughes, B. D., & Fellner, K. (2013) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2013.05.001 · Cited by 13
Noise control for molecular computing
Plesa, T., Zygalakis, K. C., Anderson, D. F., & Radek Erban, A. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2018.0199 · Cited by 14
Effects of phase disorder on transport of globally coupled Brownian motors
Wu, D., & Zhu, S. (2012) · Physical Review E · doi:10.1103/physreve.85.061101 · Cited by 13
Sparse Stabilization and Control of the Cucker-Smale Model
Caponigro, M., Fornasier, M., Piccoli, B., & Trélat, E. (2012) · Cited by 12
Minimal stochastic field equations for one-dimensional flocking
Laighléis, E. Ó., Evans, M. R., & Blythe, R. A. (2018) · Physical review. E · doi:10.1103/physreve.98.062127 · Cited by 12
Yates, C. A., & Burrage, K. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3554385 · Cited by 12
Self-citationNoise-enhanced stability in synchronized systems
Shi, Z., Lü, Q., Fu, M., Wang, X., Huang, Z., Wei, X., Amabili, M., & Huan, R. (2025) · Science Advances · doi:10.1126/sciadv.adx1338 · Cited by 11
Flocking in one dimension: Asters and reversals
Benvegnen, B., Chaté, H., Krapivsky, P. L., Tailleur, J., & Solon, A. P. (2022) · Physical review. E · doi:10.1103/physreve.106.054608 · Cited by 11
Robust Swarm Model Based on Mutual Anticipation
Gunji, Y., Murakami, H., Niizato, T., Nishiyama, Y., Tomaru, T., & Adamatzky, A. (2012) · International Journal of Artificial Life Research · doi:10.4018/jalr.2012010105 · Cited by 11
Two-species-coagulation approach to consensus by group level interactions
Escudero, C., Macià, F., & Velázquez, J. J. L. (2010) · Physical Review E · doi:10.1103/physreve.82.016113 · Cited by 11
Self-citationControl of rare events in reaction and population systems by deterministically imposed transitions
Khasin, M., & Dykman, M. I. (2011) · Physical Review E · doi:10.1103/physreve.83.031917 · Cited by 11
Hamann, H. (2018) · Springer eBooks · doi:10.1007/978-3-032-10584-4_8 · Cited by 11
Olsen, K. S., Angheluta, L., & Flekkøy, E. G. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.043017 · Cited by 10
Multiplicative measurement noise can facilitate consensus of multiagent networks
Dai, H., Sun, Y., Li, W., & Zhao, D. (2019) · Physical review. E · doi:10.1103/physreve.100.022319 · Cited by 10
Intrinsic stochasticity and the emergence of collective behaviours in insect swarms
Reynolds, A. M. (2021) · The European Physical Journal E · doi:10.1140/epje/s10189-021-00040-x · Cited by 10
Delay-induced directional switches and mean switching time in swarming systems
Sun, Y., Li, W., Li, L., Wen, G., Azaele, S., & Lin, W. J. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.033054 · Cited by 10
Information structure of heterogeneous criticality in a fish school
Niizato, T., Sakamoto, K., Mototake, Y.-I., Murakami, H., & Tomaru, T. (2024) · Scientific Reports · doi:10.1038/s41598-024-79232-2 · Cited by 11
Algar, S. D., Stemler, T., & SMALL, M. P. (2019) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2019.03.021 · Cited by 9
Noise-induced escape from bifurcating attractors: Symplectic approach in the weak-noise limit
Demaeyer, J., & Gaspard, P. (2009) · Physical Review E · doi:10.1103/physreve.80.031147 · Cited by 9
Four-Types of IIT-Induced Group Integrity of Plecoglossus altivelis
Niizato, T., Sakamoto, K., Mototake, Y.-I., Murakami, H., Tomaru, T., Hoshika, T., & Fukushima, T. (2020) · Entropy · doi:10.3390/e22070726 · Cited by 9
Coordinating directional switches in pigeon flocks: the role of nonlinear interactions
Chen, D., Sun, Y., Shao, G., Yu, W., Zhang, H., & Lin, W. J. (2021) · Royal Society Open Science · doi:10.1098/rsos.210649 · Cited by 10
Bistability and Switching Behavior in Moving Animal Groups
Strömbom, D., Nickerson, S. J., Futterman, C., DiFazio, A., Costello, C., & Tunstrøm, K. (2022) · Northeast Journal of Complex Systems · doi:10.22191/nejcs/vol4/iss1/1 · Cited by 9
Finite-time flocking of Cucker-Smale systems
Yongzheng, S., Liu, F., Wang, L., & Hongjun, S. (2015) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2015.7260749 · Cited by 9
Ordering dynamics in collectively swimming Surf Scoters
Lukeman, R. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.03.014 · Cited by 9
A DYNAMICAL APPROACH TO STOCK MARKET FLUCTUATIONS
Nicolis, S. C., & Sumpter, D. J. T. (2011) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127411030726 · Cited by 9
Self-citationUpwind flight partially explains the migratory routes of locust swarms
Sorel, M., Gay, P., Vernier, C., Cissé, S., & Piou, C. (2024) · Ecological Modelling · doi:10.1016/j.ecolmodel.2024.110622 · Cited by 8
Reynolds, A. M., & Geritz, S. A. H. (2015) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2015.01.034 · Cited by 8
Noise-induced vortex reversal of self-propelled particles
Chen, H., & Hou, Z. (2012) · Physical Review E · doi:10.1103/physreve.86.041122 · Cited by 8
Gunji, Y.-P., Murakami, H., Niizato, T., Adamatzky, A., Nishiyama, Y., Enomoto, K., Toda, M., Moriyama, T., Matsui, T., & Iizuka, K. (2011) · European Conference on Artificial Life · doi:10.7551/978-0-262-29714-1-ch047 · Cited by 8
Entangled time in flocking: Multi-time-scale interaction reveals emergence of inherent noise
Niizato, T., & Murakami, H. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195988 · Cited by 8
A Model of Scale-Free Proportion Based on Mutual Anticipation
Murakami, H., Niizato, T., & Gunji, Y. (2012) · International Journal of Artificial Life Research · doi:10.4018/jalr.2012010104 · Cited by 8
School formation characteristics and stimuli based modeling of tetra fish
Rahman, S. R., Sajjad, I., Mansoor, M., BELDEN, J. L., Murphy, C. T., & Truscott, T. (2020) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/aba2f6 · Cited by 8
Global-to-Local Design for Self-Organized Task Allocation in Swarms
Valentini, G., Hamann, H., & Dorigo, M. (2022) · Intelligent Computing · doi:10.34133/2022/9761694 · Cited by 9
Passively Active – Actively Passive Mutual Anticipation in a Communicative Swarm
Gunji, Y., Murakami, H., Niizato, T., Sonoda, K., & Adamatzky, A. (2012) · Springer eBooks · doi:10.1007/978-3-642-28111-2_16 · Cited by 8
Deriving Mesoscopic Models of Collective Behavior for Finite Populations
Jhawar, J., Morris, R. G., & Guttal, V. (2018) · Handbook of statistics · doi:10.1016/bs.host.2018.10.002 · Cited by 8
Pignotti, C., & Trélat, E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.05020 · Cited by 8
Genetic architecture of collective behaviors in zebrafishPreprint
Tang, W., Zhang, G., Serluca, F. C., Li, J., Xiong, X., Coble, M., Tsai, T., Li, Z., Molind, G. J., Zhu, P., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/350314 · Cited by 8
Kinetic order-disorder transitions in a pause-and-go swarming model with memory
Rimer, O., & Ariel, G. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.02.009 · Cited by 7
Structure preserving schemes for Fokker–Planck equations with nonconstant diffusion matrices
Loy, N., & Zanella, M. (2021) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2021.04.018 · Cited by 7
Data-driven discovery of stochastic dynamical equations of collective motion
Nabeel, A., Jadhav, V., Danny Raj, M., Sire, C., Théraulaz, G., Escobedo, R., Iyer, S. K., & Guttal, V. (2023) · Physical Biology · doi:10.1088/1478-3975/ace22d · Cited by 7
Predicting Collective Migration of Cell Populations Defined by Varying Repolarization Dynamics
Mathur, J., Sarker, B., & Pathak, A. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2018.11.013 · Cited by 7
JumpDetector: An automated monitoring equipment for the locomotion of jumping insects
Zhou, F., Kang, L., & Wang, X. (2019) · Insect Science · doi:10.1111/1744-7917.12668 · Cited by 7
Robust Swarm of Soldier Crabs, Mictyris guinotae, Based on Mutual Anticipation
Gunji, Y., Murakami, H., Niizato, T., Nishiyama, Y., Enomoto, K., Adamatzky, A., Toda, M., Moriyama, T., & Kawai, T. (2020) · CRC Press eBooks · doi:10.1201/9780429028618-4 · Cited by 7
Alignment with neighbours enables escape from dead ends in flocking models
Joshi, V., Popp, S., Werfel, J., & McCreery, H. F. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0356 · Cited by 7
Mann, R. P., Perna, A., Ströbom, D., Garnett, R., Herbert‐Read, J. E., Sumpter, D. J. T., & Ward, A. J. W. (2012) · PLoS Computational Biology · doi:10.1371/annotation/7bc3a37e-db82-4813-8242-7d34877125c5 · Cited by 7
Self-citationRetractedInferring the Langevin equation with uncertainty via Bayesian neural networks
Bae, Y., Ha, S., & Jeong, H. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116440 · Cited by 9
West, B. J., Turalska, M., & Grigolini, P. (2014) · Springer eBooks · doi:10.1007/978-3-319-04879-6_5 · Cited by 6
Discovering Stochastic Dynamical Equations from Ecological Time Series Data
Nabeel, A., Karichannavar, A., Palathingal, S., Jhawar, J., Brückner, D. B., Danny Raj, M., & Guttal, V. (2024) · The American Naturalist · doi:10.1086/734083 · Cited by 8
Hamann, H. (2018) · Springer eBooks · doi:10.1007/978-3-319-74528-2_4 · Cited by 6
Detection and characterization of chemotaxis without cell tracking
Hywood, J. D., Rice, G., Pageon, S. V., Read, M. N., & Biro, M. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2020.0879 · Cited by 6
Quantum pure noise-induced transitions: A truly nonclassical limit cycle sensitive to number parity
Chia, A., Mok, W., Noh, C., & Kwek, L. C. (2023) · SciPost Physics · doi:10.21468/scipostphys.15.3.121 · Cited by 7
Directional switches in network-organized swarming systems with delay
Xiao, R., Li, W., Zhao, D., & Sun, Y. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0142917 · Cited by 6
Metadata
- Authors
- Yates, C. A., Erban, R., Escudero, C., Couzin, I. D., Buhl, C., Kevrekidis, I. G., Maini, P. K., & Sumpter, D. J. T.
- Affiliations
- Centre for Mathematical Biology and
- Consejo Superior de Investigaciones Científicas (ES)
- Princeton University (US)
- The University of Sydney (AU)
- University of Oxford (GB)
- Uppsala University (SE)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2009-04-07
- Volume
- 106
- Issue
- 14
- Pages
- 5464-5469
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2667078
- OpenAlex
- W2055256257
- Cited by
- 304
- References
- 27
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 15 · 2023: 12 · 2022: 19 · 2021: 13 · 2020: 15 · 2019: 17 · 2018: 20 · 2017: 21 · 2016: 16 · 2015: 23 · 2014: 28 · 2013: 19 · 2012: 28
- FWCI
- 29.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Oxford University Research Archive (ORA) (University of Oxford) (acceptedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
- Funders
- Princeton University
- King Abdullah University of Science and Technology (KUK‐C1‐013‐04)
- Directorate for Biological Sciences
- Engineering and Physical Sciences Research Council
- Biotechnology and Biological Sciences Research Council
- Air Force Office of Scientific Research
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Social Sciences › Safety Research › Evacuation and Crowd Dynamics
- Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
- Keywords
- collective motion, locusts, Sturnidae, directional switching, self-propelled particle model, Fokker-Planck equation, noise, group density, marching bands, animal collective behavior
- MeSH terms
- Animals, Computer Simulation, Flight, Animal, Grasshoppers, Models, Biological, Movement, Noise, Starlings
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
