This page holds/will hold details about the projects undertaken by TEE-Lab interns during the summer of 2021. It is intended to be the single source of information for the interns as well as the mentors. As the internship progresses, we will add details as appropriate and possibly do some logging for later reference.

Projects

All three projects have significant potential for interfacing with each other. As such much of this work will be done in a collaborative manner. For example, Team P and Q can do the basic reading and discussion related to the TRex package before they try and apply it to the two different model systems. Similarly, Team P may provide the tracks to Team R to check the validity of results from [Murakami and Niizato et. al, 2005]. All teams are encouraged to participate in lab meetings and present their progress.

GENERAL READING MATERIAL (Recommended for all teams)

• Ecology: from individuals to collectives – [V. Guttal, 2014]
• Simulating collective motion:
  • Collective Memory And Spatial Sorting in Animal Groups – [Couzin et. al., 2002]
  • Novel Type of Phase Transition in a System of Self-Driven Particles – [Viscek et. al., 1995]
• Empirical work on inferring interactions from which collective motion emerges:
  • Inferring the structure and dynamics of interactions in schooling fish – [Katz et al., 2011]
  • Inferring the rules of interaction of shoaling fish – [Herbert-Read et. al., 2011]
  • Noise-induced schooling of fish – [Jhawar et. al., 2020]

RESOURCES RELATED TO TRACKING (Especially recommended for Team P and Q)

• How a Kalman filter works, in pictures – [Babb, T., 2015]
• Student Dave’s Tutorials!
• TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields [paper][source code]- [Walter and Couzin, 2021]
• Quantitative comparison of object-detection techniques on animal space-use videos – [Rathore et. al., in preparation] This manuscript is in preparation and all rights are reserved by the authors. Please do not redistribute it in any form.
• Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings – [Rathore et. al., 2020]

RESOURCES RELATED TO BROWNIAN MOTION, LEVY WALKS, AND INHERENT NOISE IN FISH SCHOOLS (Especially recommended for Team R)

• 100 years of Einstein’s theory of Brownian motion: from Pollen grains to protein trains – [Chowdhury, D., 2005]
• Beyond Brownian Motion: A Levy Flight in Magic Boots – [Chakravarti, N., 2004]
• A python notebook on Random Walks – Ayan Das
• Brownian motion and random walks – Oscar Mickelin
• Inherent noise appears as a Lévy walk in fish schools – [Murakami, H. et. al., 2015]
• More on inherent noise:
  • Inherent noise can facilitate coherence in collective swarm motion – [Yates, C. A., 2009]
  • Noise-induced effects in collective dynamics and inferring local interactions from data – [Jhawar and Guttal, 2020]
• From Lévy to Brownian: A Computational Model Based on Biological Fluctuation – [Nurzaman, S. G., et. al., 2011]

References:

[1] Walter, T., & Couzin, I. D. (2021). TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields. Elife, 10, e64000.

[2] Murakami, H., Niizato, T., Tomaru, T., Nishiyama, Y., & Gunji, Y. P. (2015). Inherent noise appears as a Lévy walk in fish schools. Scientific reports, 5(1), 1-11.

[3] Jhawar, J., Morris, R. G., Amith-Kumar, U. R., Raj, M. D., Rogers, T., Rajendran, H., & Guttal, V. (2020). Noise-induced schooling of fish. Nature Physics, 16(4), 488-493.

[4] Guttal, V. (2014). Ecology: From individuals to collectives. Resonance, 19(4), 368-375.

[5] Couzin, I. D., Krause, J., James, R., Ruxton, G. D., & Franks, N. R. (2002). Collective memory and spatial sorting in animal groups. Journal of theoretical biology, 218(1), 1-11.

[6] Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., & Shochet, O. (1995). Novel type of phase transition in a system of self-driven particles. Physical review letters, 75(6), 1226.

[7] Katz, Y., Tunstrøm, K., Ioannou, C. C., Huepe, C., & Couzin, I. D. (2011). Inferring the structure and dynamics of interactions in schooling fish. Proceedings of the National Academy of Sciences, 108(46), 18720-18725.

[8] Herbert-Read, J. E., Perna, A., Mann, R. P., Schaerf, T. M., Sumpter, D. J., & Ward, A. J. (2011). Inferring the rules of interaction of shoaling fish. Proceedings of the National Academy of Sciences, 108(46), 18726-18731.

[9] Babb, T. (2015). How a Kalman filter works, in pictures. Available at link.

[10] Rathore, A., Sharma, A., Sharma, N., & Guttal, V. (2020). Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal space-use studies. bioRxiv.1

[11] Chowdhury, D. (2005). 100 years of Einstein’s theory of Brownian motion: from Pollen grains to protein trains—1. Resonance, 10(9), 63-78.

[12] Chakravarti, N. (2004). Beyond Brownian motion: A Levy flight in magic boots. Resonance, 9(1), 50-60.

[13] Yates, C. A., Erban, R., Escudero, C., Couzin, I. D., Buhl, J., Kevrekidis, I. G., … & Sumpter, D. J. (2009). Inherent noise can facilitate coherence in collective swarm motion. Proceedings of the National Academy of Sciences, 106(14), 5464-5469.

[14] Jhawar, J., & Guttal, V. (2020). Noise-induced effects in collective dynamics and inferring local interactions from data. Philosophical Transactions of the Royal Society B, 375(1807), 20190381.

[15] Nurzaman, S. G., Matsumoto, Y., Nakamura, Y., Shirai, K., Koizumi, S., & Ishiguro, H. (2011). From Lévy to Brownian: a computational model based on biological fluctuation. PloS one, 6(2), e16168.

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Thanks to Ayan Das who drafted and curated this page.