Prediction of Individual Fish Trajectory from Its Neighbors’ Movement by a Recurrent Neural Network

  • Gang Xiao
  • Yi Li
  • Tengfei Shao
  • Zhenbo Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)


Individuals in large groups respond to the movements and positions of their neighbors by following a set of interaction rules. These rules are central to understanding the mechanisms of collective motion. However, whether individuals actually use these rules to guide their movements remains untested. Here we show that the real-time movements of individual fish can be directly predicted from their neighbors’ motion. We train a recurrent neural network to predict the trajectories of individual fish from input signals. The inputs are projected to the recurrent network as time series representing the movements and positions of neighboring fish. By comparing the data output from the model with the target fish’s trajectory, we provide direct evidence that individuals guide their movements via interaction rules. Because the error between the model output and actual trajectory changes when the fish perceive a noxious contaminant, the model is potentially applicable to water quality monitoring.


Fish Behavior Recurrent Neural Network Force Learning Water Quality Monitoring 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Viscido, S.V., Parrish, J.K., Grünbaum, D.: Individual Behavior and Emergent Properties of Fish Schools: a Comparison of Observation and Theory. Marine Ecology 273, 239–249 (2004)CrossRefGoogle Scholar
  2. 2.
    Landeau, L., Terborgh, J.: Oddity and the ‘Confusion Effect’ in Predation. Animal Behaviour 34, 1372–1380 (1986)CrossRefGoogle Scholar
  3. 3.
    Pitcher, T.J.: Functions of Shoaling Behaviour in Teleosts. In: The Behaviour of Teleost Fishes, pp. 294–337. Springer (1986)Google Scholar
  4. 4.
    Suboski, M.D., Bain, S., Carty, A.E., McQuoid, L.M., Seelen, M.I., Seifert, M.: Alarm Reaction in Acquisition and Social Transmission of Simulated-Predator Recognition by Zebra Danio Fish (Brachydanio Rerio). Journal of Comparative Psychology 104, 12 (1990)CrossRefGoogle Scholar
  5. 5.
    Couzin, I.D.: Collective Cognition in Animal Groups. Trends in Cognitive Sciences 13, 36–43 (2009)CrossRefGoogle Scholar
  6. 6.
    Herbert-Read, J.E., Perna, A., Mann, R.P., Schaerf, T.M., Sumpter, D.J., Ward, A.J.: Inferring the Rules of Interaction of Shoaling Fish. PNAS 108, 18726–18731 (2011)CrossRefGoogle Scholar
  7. 7.
    Katz, Y., Tunstrom, K., Ioannou, C.C., Huepe, C., Couzin, I.D.: Inferring the Structure and Dynamics of Interactions in Schooling Fish. PNAS 108, 18720–18725 (2011)CrossRefGoogle Scholar
  8. 8.
    Giardina, I.: Collective Behavior in Animal Groups: Theoretical Models and Empirical Studies. HFSP Journal 2, 205–219 (2008)CrossRefGoogle Scholar
  9. 9.
    Bode, N.W.F., Franks, D.W., Wood, A.J.: Limited Interactions in Flocks: Relating Model Simulations to Empirical Data. Journal of the Royal Society 8, 301–304 (2011)Google Scholar
  10. 10.
    Haykin, S.S.: Adaptive filter theory. Pearson Education India (2007)Google Scholar
  11. 11.
    Chen, X.L., Huang, H.J.: Cyprininae. In: Wu, X.W. (ed.) Monographs of Cyprinidae in China. Shanghai People’s Press, Shanghai (1982)Google Scholar
  12. 12.
    Xiao, G., Feng, M., Cheng, Z., Zhao, M., Mao, J., Mirowski, L.: Water Quality Monitoring Using Abnormal Tail-Beat Frequency of Crucian Carp. Ecotoxicology and Environmental Safety 111, 185–191 (2015)CrossRefGoogle Scholar
  13. 13.
    Sussillo, D., Abbott, L.F.: Generating Coherent Patterns of Activity from Chaotic Neural Networks. Neuron 63, 544–557 (2009)CrossRefGoogle Scholar
  14. 14.
    Yoshida, T., Minami, M., Mae, Y.: Fish Catching by Visual Servoing Using Neural Network Prediction. In: 2007 Annual Conference, SICE, Takamatsu, pp. 2372–2378 (2007)Google Scholar
  15. 15.
    Gautrais, J., Ginelli, F., Fournier, R., Blanco, S., Soria, M., Chate, H., Theraulaz, G.: Deciphering Interactions in Moving Animal Groups. PLoS Computational Biology 8, e1002678 (2012)Google Scholar
  16. 16.
    Morgan, J.D., Vigers, G.A., Farrell, A.P., Janz, D.M., Manville, J.F.: Acute Avoidance Reactions and Behavioral Responses of Juvenile Rainbow Trout (Oncorhynchus Mykiss) to Garlon 4®, Garlon 3A® and Vision® herbicides. Environmental Toxicology and Chemistry 10, 73–79 (1991)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

<SimplePara><Emphasis Type="Bold">Open Access</Emphasis> This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. </SimplePara> <SimplePara>The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.</SimplePara>

Authors and Affiliations

  • Gang Xiao
    • 1
  • Yi Li
    • 1
  • Tengfei Shao
    • 1
  • Zhenbo Cheng
    • 1
  1. 1.Department of Computer Science and TechnologyZhejiang University of TechnologyHangzhouChina

Personalised recommendations