Dimensionality and Dynamics in the Behavior of C. elegans
Open accessAbstract
A major challenge in analyzing animal behavior is to discover some underlying simplicity in complex motor actions. Here, we show that the space of shapes adopted by the nematode Caenorhabditis elegans is low dimensional, with just four dimensions accounting for 95% of the shape variance. These dimensions provide a quantitative description of worm behavior, and we partially reconstruct "equations of motion" for the dynamics in this space. These dynamics have multiple attractors, and we find that the worm visits these in a rapid and almost completely deterministic response to weak thermal stimuli. Stimulus-dependent correlations among the different modes suggest that one can generate more reliable behaviors by synchronizing stimuli to the state of the worm in shape space. We confirm this prediction, effectively "steering" the worm in real time.
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Fish larvae require only three basis functions to describe their backbone shapes, whereas worms analyzed in prior work required four.
“fish larvae require only three basis functions to describe their backbone shapes, whereas worms analyzed in [18] required four.”
From The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog (2015) · cites this paper as [18]
A study of C. elegans by Stephens et al. provided a method to quantify worm locomotion in terms of the worm's shapes.
“A study of the invertebrate C. elegans by Stephens et al. [18] provided a powerful method to quantify worm locomotion in terms of the worm’s shapes.”
From The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog (2015) · cites this paper as [18]
Fish exhibit larger behavioral complexity than worms.
“However, fish exhibit larger behavioral complexity.”
From The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog (2015) · cites this paper as [18]
The Introduction and Results describe the complexity and low-dimensional structure of C. elegans behavior, but do not compare worms with fish. Searches for fish, zebrafish, and comparative behavioral complexity found no passage addressing the claim; page numbers are unavailable in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredFish are stiffer than worms thanks to their backbone, leading to a smaller repertoire of accessible shapes of the fish backbone.
“fish are stiffer than worms thanks to their backbone (see Fig 6D), leading to a smaller repertoire of accessible shapes of the fish backbone.”
From The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog (2015) · cites this paper as [18]
The Results (Eigenworms; page not provided) describe a limited effective dimensionality of C. elegans shapes, but do not compare worms with fish. Searches for fish stiffness, backbones, and accessible shapes found no passage addressing the claimed comparison or its proposed cause. (LLM, conf=0.76)
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Cites
THE GENETICS OF CAENORHABDITIS ELEGANS
Brenner, S. (1974) · Genetics · doi:10.1093/genetics/77.1.71 · Cited by 14,984 · see citing works
(1966) · Medical Entomology and Zoology · Cited by 10,293 · see citing works
Signal detection theory and psychophysics
Ingleby, D. (1967) · Journal of Sound and Vibration · doi:10.1016/0022-460x(67)90197-6 · Cited by 8,616 · see citing works
The structure of the nervous system of the nematode <i>Caenorhabditis elegans</i>
White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1986.0056 · Cited by 5,589 · see citing works
Genome Sequence of the Nematode C. elegans : A Platform for Investigating Biology
Berg, J. (1998) · Science · doi:10.1126/science.282.5396.2012 · Cited by 4,458 · see citing works
Stochastic Processes in Physics and Chemistry
Cercignani, C. (2005) · Elsevier eBooks · doi:10.1016/b0-12-369401-9/00623-9 · Cited by 2,143 · see citing works
Berg, H. C., & Purcell, E. M. (1977) · Biophysical Journal · doi:10.1016/s0006-3495(77)85544-6 · Cited by 1,902 · see citing works
Lectures on Classical Differential Geometry.
Springer, C. E., & Struik, D. J. (1952) · American Mathematical Monthly · doi:10.2307/2307130 · Cited by 1,370 · see citing works
Postural Hand Synergies for Tool Use
Santello, M., Flanders, M., & Soechting, J. F. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-23-10105.1998 · Cited by 1,215 · see citing works
The neural and behavioural organization of goal-directed movements
Jeannerod, M. (1988) · Medical Entomology and Zoology · Cited by 1,164 · see citing works
The neural circuit for touch sensitivity in Caenorhabditis elegans
Chalfie, M., Sulston, J., White, J., Southgate, E., Thomson, J., & Brenner, S. (1985) · The Journal of Neuroscience · doi:10.1523/jneurosci.05-04-00956.1985 · Cited by 1,093 · see citing works
[Genetics of Caenorhabditis elegans].
Munakata, N. (1989) · PubMed · Cited by 886 · see citing works
A circuit for navigation in <i>Caenorhabditis elegans</i>
Gray, J. M., Hill, J. J., & Bargmann, C. I. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409009101 · Cited by 811 · see citing works
An excitable gene regulatory circuit induces transient cellular differentiation
Süel, G. M., García‐Ojalvo, J., Liberman, L. M., & Elowitz, M. B. (2006) · Nature · doi:10.1038/nature04588 · Cited by 714 · see citing works
THE DNA OF CAENORHABDITIS ELEGANS
Sulston, J., & Brenner, S. (1974) · Genetics · doi:10.1093/genetics/77.1.95 · Cited by 584 · see citing works
Ward, S. (1973) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.70.3.817 · Cited by 572 · see citing works
Normal and mutant thermotaxis in the nematode Caenorhabditis elegans.
Hedgecock, E. M., & Russell, R. L. (1975) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.72.10.4061 · Cited by 568 · see citing works
The Fundamental Role of Pirouettes in Caenorhabditis elegans Chemotaxis
Pierce-Shimomura, J. T., Morse, T. M., & Lockery, S. R. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-21-09557.1999 · Cited by 536 · see citing works
Berg, H. C. (2013) · Cited by 439 · see citing works
NEURONAL SUBSTRATES OF COMPLEX BEHAVIORS IN C. ELEGANS
de Bono, M., & Maricq, A. V. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144259 · Cited by 401 · see citing works
A sensory source for motor variation
Osborne, L. C., Lisberger, S. G., & Bialek, W. (2005) · Nature · doi:10.1038/nature03961 · Cited by 299 · see citing works
Self-citationCell Motility as Persistent Random Motion: Theories from Experiments
Selmeczi, D., Mosler, S., Hagedorn, P., Larsen, N. B., & Flyvbjerg, H. (2005) · Biophysical Journal · doi:10.1529/biophysj.105.061150 · Cited by 299 · see citing works
PHYSICAL LIMITS TO SENSATION AND PERCEPTION
Bialek, W. (1987) · Annual Review of Biophysics and Biophysical Chemistry · doi:10.1146/annurev.bb.16.060187.002323 · Cited by 237 · see citing works
Self-citationHow to Quantify Deterministic and Random Influences on the Statistics of the Foreign Exchange Market
Friedrich, R., Peinke, J., & Renner, C. (2000) · Physical Review Letters · doi:10.1103/physrevlett.84.5224 · Cited by 205 · see citing works
Thermotaxis inCaenorhabditis elegansAnalyzed by Measuring Responses to Defined Thermal Stimuli
Ryu, W. S., & Samuel, A. D. T. (2002) · Journal of Neuroscience · doi:10.1523/jneurosci.22-13-05727.2002 · Cited by 197 · see citing works
Self-citationThermal avoidance in Caenorhabditis elegans : An approach to the study of nociception
Wittenburg, N., & Baumeister, R. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.10477 · Cited by 194 · see citing works
Components and patterns in the behaviour of the nematode Caenorhabditis elegans
Croll, N. A. (1975) · Journal of Zoology · doi:10.1111/j.1469-7998.1975.tb03191.x · Cited by 183 · see citing works
An automated system for measuring parameters of nematode sinusoidal movement
Cronin, C. J., Mendel, J., Mukhtar, S., Kim, Y.-M., Stirbl, R. C., Bruck, J., & Sternberg, P. W. (2005) · BMC Genetics · doi:10.1186/1471-2156-6-5 · Cited by 179 · see citing works
Genetic and Cellular Analysis of Behavior in C. Elegans
Bargmann, C. I. (1993) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.16.030193.000403 · Cited by 152 · see citing works
Human Arm Movements Described by a Low-Dimensional Superposition of Principal Components
Sanger, T. D. (2000) · Journal of Neuroscience · doi:10.1523/jneurosci.20-03-01066.2000 · Cited by 109 · see citing works
Step-Response Analysis of Chemotaxis inCaenorhabditis elegans
Miller, A. C., Thiele, T. R., Faumont, S., Moravec, M. L., & Lockery, S. R. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.5133-04.2005 · Cited by 105 · see citing works
Conservation rules, their breakdown, and optimality in Caenorhabditis sinusoidal locomotion
Karbowski, J., Cronin, C. J., Seah, A., Mendel, J. E., Cleary, D., & Sternberg, P. W. (2006) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2006.04.012 · Cited by 97 · see citing works
Sensorimotor control during isothermal tracking in Caenorhabditis elegans
Luo, L., Clark, D. A., Biron, D., Mahadevan, L., & Samuel, A. D. T. (2006) · Journal of Experimental Biology · doi:10.1242/jeb.02590 · Cited by 75 · see citing works
Machine vision based detection of omega bends and reversals in C. elegans
Huang, K.-M., Cosman, P. C., & Schafer, W. R. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.06.007 · Cited by 75 · see citing works
Quantitative Classification and Natural Clustering of Caenorhabditis elegans Behavioral Phenotypes
Geng, W., Cosman, P. C., Baek, J.-H., Berry, C. C., & Schafer, W. R. (2003) · Genetics · doi:10.1093/genetics/165.3.1117 · Cited by 71 · see citing works
Kernel Principal Component Analysis and the construction of non-linear Active Shape Models
Twining, C., & Taylor, C. (2001) · British Machine Vision Conference (BMVC) · doi:10.5244/c.15.4 · Cited by 69 · see citing works
Low dimensionality of supraspinally induced force fields
d’Avella, A., & Bizzi, E. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.13.7711 · Cited by 65 · see citing works
A Computational Model forC. elegansLocomotory Behavior: Application to Multiworm Tracking
Roussel, N., Morton, C. A., Finger, F. P., & Roysam, B. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.894981 · Cited by 65 · see citing works
Faumont, S., & Lockery, S. R. (2005) · Journal of Neurophysiology · doi:10.1152/jn.01050.2005 · Cited by 61 · see citing works
Step Response Analysis of Thermotaxis inCaenorhabditis elegans
Zariwala, H. A., Miller, A. C., Faumont, S., & Lockery, S. R. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-10-04369.2003 · Cited by 61 · see citing works
Influence of odorant receptor repertoire on odor perception in humans and fruit flies
Keller, A., & Vosshall, L. B. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605321104 · Cited by 60 · see citing works
Lectures on classical differential geometry
Struik, D. J. (1988) · CERN Document Server (European Organization for Nuclear Research) · Cited by 50 · see citing works
Computer-driven automatic identification of locomotion states in Caenorhabditis elegans
Hoshi, K., & Shingai, R. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.05.002 · Cited by 40 · see citing works
Effects of aging on habituation in the nematode Caenorhabditis elegans
Beck, C., & Rankin, C. H. (1993) · Behavioural Processes · doi:10.1016/0376-6357(93)90088-9 · Cited by 34 · see citing works
Langevin equations from time series
Racca, E., & Porporato, A. M. (2005) · Physical Review E · doi:10.1103/physreve.71.027101 · Cited by 8 · see citing works
Flagged first, then by citations
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- [2]Bolhuis J Giraldeau L 2004 The Behavior of Animals: Mechanism, Function and Evolution. Oxford Blackwell Publishing
Cited by
Sorted by citations
Whole-animal connectomes of both Caenorhabditis elegans sexes
Cook, S. J., Jarrell, T. A., Brittin, C. A., Wang, Y., Bloniarz, A. E., Yakovlev, M. A., Nguyen, K. C. Q., Tang, L. T.-H., Bayer, E. A., Duerr, J. S., et al. (2019) · Nature · doi:10.1038/s41586-019-1352-7 · Cited by 921 · see citing works
Mapping Sub-Second Structure in Mouse Behavior
Wiltschko, A. B., Johnson, M. J., Iurilli, G., Peterson, R. E., Katon, J. M., Pashkovski, S. L., Abraira, V. E., Adams, R. P., & Datta, S. R. (2015) · Neuron · doi:10.1016/j.neuron.2015.11.031 · Cited by 760 · see citing works
Mapping the stereotyped behaviour of freely moving fruit flies
Berman, G. J., Choi, D. M., Bialek, W., & Shaevitz, J. W. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0672 · Cited by 565 · see citing works
Self-citationGlobal Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans
Kato, S., Kaplan, H. S., Schrödel, T., Skora, S., Lindsay, T. H., Yemini, E., Lockery, S. R., & Zimmer, M. (2015) · Cell · doi:10.1016/j.cell.2015.09.034 · Cited by 523 · see citing works
High-throughput behavioral analysis in C. elegans
Swierczek, N. A., Giles, A. C., Rankin, C. H., & Kerr, R. A. (2011) · Nature Methods · doi:10.1038/nmeth.1625 · Cited by 460 · see citing works
Computational Neuroethology: A Call to Action
Datta, S. R., Anderson, D. J., Branson, K., Perona, P., & Leifer, A. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.038 · Cited by 453 · see citing works
Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans
Nguyen, J. P., Shipley, F. B., Linder, A. N., Plummer, G. S., Liu, M., Setru, S. U., Shaevitz, J. W., & Leifer, A. M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507110112 · Cited by 399 · see citing works
Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion
Dunn, T. W., Mu, Y., Narayan, S., Randlett, O., Naumann, E. A., Yang, C.-T., Schier, A. F., Freeman, J., Engert, F., & Ahrens, M. B. (2016) · eLife · doi:10.7554/elife.12741 · Cited by 312 · see citing works
Network control principles predict neuron function in the Caenorhabditis elegans connectome
Yan, G. F., Vértes, P. E., Towlson, E. K., Chew, Y. L., Walker, D. S., Schafer, W. R., & Barabási, A.-Ĺ. (2017) · Nature · doi:10.1038/nature24056 · Cited by 349 · see citing works
Quantifying behavior to understand the brain
Pereira, T., Shaevitz, J. W., & Murthy, M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00734-z · Cited by 302 · see citing works
Neural circuit policies enabling auditable autonomy
Lechner, M., Hasani, R., Amini, A., Henzinger, T. A., Rus, D. L., & Grosu, R. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00237-3 · Cited by 313 · see citing works
A database of Caenorhabditis elegans behavioral phenotypes
Yemini, E., Jucikas, T., Grundy, L. J., Brown, A. E., & Schafer, W. R. (2013) · Nature Methods · doi:10.1038/nmeth.2560 · Cited by 309 · see citing works
Big behavioral data: psychology, ethology and the foundations of neuroscience
Gómez-Marín, À., Paton, J. J., Kampff, A. R., Costa, R. M., & Mainen, Z. F. (2014) · Nature Neuroscience · doi:10.1038/nn.3812 · Cited by 286 · see citing works
B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors
Hsu, A. I., & Yttri, E. A. (2021) · Nature Communications · doi:10.1038/s41467-021-25420-x · Cited by 295 · see citing works
Structure of the Zebrafish Locomotor Repertoire Revealed with Unsupervised Behavioral Clustering
Marques, J. C., Lackner, S., Félix, R., & Orger, M. B. (2018) · Current Biology · doi:10.1016/j.cub.2017.12.002 · Cited by 245 · see citing works
Aguilar, J., Zhang, T., Qian, F., Kingsbury, M., McInroe, B. W., Mazouchova, N., Li, C., Maladen, R. D., Gong, C., Travers, M. J., et al. (2016) · Reports on Progress in Physics · doi:10.1088/0034-4885/79/11/110001 · Cited by 289 · see citing works
Parameter Space Compression Underlies Emergent Theories and Predictive Models
Machta, B. B., Chachra, R., Transtrum, M. K., & Sethna, J. P. (2013) · Science · doi:10.1126/science.1238723 · Cited by 273 · see citing works
Active sampling and decision making in Drosophila chemotaxis
Gómez-Marín, À., Stephens, G. J., & Louis, M. (2011) · Nature Communications · doi:10.1038/ncomms1455 · Cited by 255 · see citing works
Self-citationPan-neuronal imaging in roaming Caenorhabditis elegans
Venkatachalam, V., Ji, N., Wang, X., Clark, C., Mitchell, J. K., Klein, M., Tabone, C. J., Florman, J., Ji, H., Greenwood, J. S. F., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507109113 · Cited by 253 · see citing works
Pan-neuronal calcium imaging with cellular resolution in freely swimming zebrafish
Kim, D. H., Kim, J., Marques, J. C., Grama, A., Hildebrand, D. G. C., Gu, W., Li, J. M., & Robson, D. N. (2017) · Nature Methods · doi:10.1038/nmeth.4429 · Cited by 228 · see citing works
Challenges and solutions for studying collective animal behaviour in the wild
Hughey, L. F., Hein, A. M., Strandburg‐Peshkin, A., & Jensen, F. H. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0005 · Cited by 229 · see citing works
Stirman, J. N., Crane, M. M., Husson, S. J., Wabnig, S., Schultheis, C., GOTTSCHALK, A. R., & Lu, H. (2011) · Nature Methods · doi:10.1038/nmeth.1555 · Cited by 217 · see citing works
Geometric deep learning enables 3D kinematic profiling across species and environments
Dunn, T., Marshall, J. D., Severson, K. S., Aldarondo, D. E., Hildebrand, D. G. C., Chettih, S. N., Wang, W. L., Gellis, A., Carlson, D., Aronov, D., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01106-6 · Cited by 199 · see citing works
Optogenetic analysis of synaptic function
Liewald, J. F., Brauner, M., Stephens, G. J., Bouhours, M., Schultheis, C., Zhen, M., & GOTTSCHALK, A. R. (2008) · Nature Methods · doi:10.1038/nmeth.1252 · Cited by 201 · see citing works
Self-citationComputational Analysis of Behavior
Egnor, S. E. R., & Branson, K. M. (2016) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070815-013845 · Cited by 189 · see citing works
Biomechanical analysis of gait adaptation in the nematode <i>Caenorhabditis elegans</i>
Fang-Yen, C., Wyart, M., Xie, J., Kawai, R., Kodger, T., Chen, S., Wen, Q., & Samuel, A. D. T. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1003016107 · Cited by 200 · see citing works
Garrity, P. A., Goodman, M. B., Samuel, A. D., & Sengupta, P. (2010) · Genes & Development · doi:10.1101/gad.1953710 · Cited by 197 · see citing works
Physical manipulation of the Escherichia coli chromosome reveals its soft nature
Pelletier, J. F., Halvorsen, K., Ha, B., Paparcone, R., Sandler, S. J., Woldringh, C., Wong, W. P., & Jun, S. (2012) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1208689109 · Cited by 200 · see citing works
Brown, A. E., Yemini, E., Grundy, L. J., Jucikas, T., & Schafer, W. R. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1211447110 · Cited by 186 · see citing works
Measuring behavior across scales
Berman, G. J. (2018) · BMC Biology · doi:10.1186/s12915-018-0494-7 · Cited by 184 · see citing works
Ethology as a physical science
Brown, A. E., & de Bivort, B. L. (2018) · Nature Physics · doi:10.1038/s41567-018-0093-0 · Cited by 163 · see citing works
Probabilistic Models of Larval Zebrafish Behavior Reveal Structure on Many Scales
Johnson, R. E., Linderman, S. W., Panier, T., Wee, C. L., Song, E. Y., Herrera, K. J., Miller, A. T., & Engert, F. (2019) · Current Biology · doi:10.1016/j.cub.2019.11.026 · Cited by 161 · see citing works
Restif, C., Ibáñez‐Ventoso, C., Vora, M., Guo, S., Metaxas, D., & Driscoll, M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003702 · Cited by 158 · see citing works
Big behavior: challenges and opportunities in a new era of deep behavior profiling
von Ziegler, L., Sturman, O., & Bohacek, J. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0751-7 · Cited by 156 · see citing works
Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire
Marshall, J. D., Aldarondo, D. E., Dunn, T., Wang, W. L., Berman, G. J., & Ölveczky, B. P. (2020) · Neuron · doi:10.1016/j.neuron.2020.11.016 · Cited by 139 · see citing works
Deconstructing Hunting Behavior Reveals a Tightly Coupled Stimulus-Response Loop
Mearns, D. S., Donovan, J. C., Fernandes, A. M., Semmelhack, J. L., & Baier, H. (2019) · Current Biology · doi:10.1016/j.cub.2019.11.022 · Cited by 151 · see citing works
An open-source platform for analyzing and sharing worm-behavior data
Javer, A., Currie, M., Lee, C. W., Hokanson, J., Li, K., Martineau, C. N., Yemini, E., Grundy, L. J., Li, C., Ch'ng, Q.-L., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0112-1 · Cited by 151 · see citing works
ZebraZoom: an automated program for high-throughput behavioral analysis and categorization
Mirat, O., Sternberg, J. R., Severi, K. E., & Wyart, C. (2013) · Frontiers in Neural Circuits · doi:10.3389/fncir.2013.00107 · Cited by 143 · see citing works
BiPOLES is an optogenetic tool developed for bidirectional dual-color control of neurons
Vierock, J., Rodriguez-Rozada, S., Dieter, A., Pieper, F., Sims, R. R., Tenedini, F. M., Bergs, A. C. F., Bendifallah, I., Zhou, F., Zeitzschel, N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24759-5 · Cited by 145 · see citing works
Cellular Contact Guidance through Dynamic Sensing of Nanotopography
Driscoll, M., Sun, X., Güven, C., Fourkas, J. T., & Losert, W. (2014) · ACS Nano · doi:10.1021/nn406637c · Cited by 141 · see citing works
Navigational Decision Making in Drosophila Thermotaxis
Luo, L., Gershow, M. H., Rosenzweig, M. R., Kang, K., Fang-Yen, C. M., Garrity, P., & Samuel, A. D. T. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.4090-09.2010 · Cited by 143 · see citing works
Glauser, D. A., Chen, W. C., Agin, R., MacInnis, B. L., Hellman, A. B., Garrity, P., Tan, M., & Goodman, M. B. (2011) · Genetics · doi:10.1534/genetics.111.127100 · Cited by 131 · see citing works
Keeping track of worm trackers
Husson, S. J. (2012) · WormBook · doi:10.1895/wormbook.1.156.1 · Cited by 130 · see citing works
Reverse engineering the euglenoid movement
Arroyo, M., Heltai, L., Millán, D., & DeSimone, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1213977109 · Cited by 114 · see citing works
Stochastic nonlinear dynamics of confined cell migration in two-state systems
Brückner, D. B., Fink, A., Schreiber, C., Röttgermann, P. J. F., Rädler, J. O., & Broedersz, C. P. (2019) · Nature Physics · doi:10.1038/s41567-019-0445-4 · Cited by 119 · see citing works
Systems Neuroscience of Natural Behaviors in Rodents
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Paveley, R. A., Mansour, N. R., Hallyburton, I., Bleicher, L. S., Benn, A. E., Mikic, I., Guidi, A., Gilbert, I. H., Hopkins, A. L., & Bickle, Q. D. (2012) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0001762 · Cited by 113 · see citing works
The zebrafish subcortical social brain as a model for studying social behavior disorders
Geng, Y., & Peterson, R. T. (2019) · Disease Models & Mechanisms · doi:10.1242/dmm.039446 · Cited by 111 · see citing works
Roberts, W. M., Augustine, S. B., Lawton, K. J., Lindsay, T. H., Thiele, T. R., Izquierdo, E. J., Faumont, S., Lindsay, R. A., Britton, M. C., Pokala, N., et al. (2016) · eLife · doi:10.7554/elife.12572 · Cited by 110 · see citing works
Caenorhabditis elegans: a model system for systems neuroscience
Sengupta, P., & Samuel, A. D. T. (2009) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2009.09.009 · Cited by 109 · see citing works
Ristroph, L., Berman, G. J., Bergou, A. J., Wang, Z. J., & Cohen, I. (2009) · Journal of Experimental Biology · doi:10.1242/jeb.025502 · Cited by 108 · see citing works
Human sperm steer with second harmonics of the flagellar beat
Saggiorato, G., Alvarez, L., Jikeli, J. F., Kaupp, U. B., Gompper, G., & Elgeti, J. (2017) · Nature Communications · doi:10.1038/s41467-017-01462-y · Cited by 106 · see citing works
Modulation of orthogonal body waves enables high maneuverability in sidewinding locomotion
Astley, H. C., Gong, C., Dai, J. Y., Travers, M. J., Serrano, M. M., Vela, P. A., Choset, H., MENDELSON, J. R. I., Hu, D. L., & Goldman, D. I. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418965112 · Cited by 105 · see citing works
Luo, L., Cook, N., Venkatachalam, V., Martinez-Velazquez, L., Zhang, X., Calvo, A. C., Hawk, J. D., MacInnis, B. L., Frank, M. M., Ng, J. H. R., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1315205111 · Cited by 105 · see citing works
Learning Force Fields from Stochastic Trajectories
Frishman, A., & Ronceray, P. (2020) · Physical Review X · doi:10.1103/physrevx.10.021009 · Cited by 104 · see citing works
Deep attention networks reveal the rules of collective motion in zebrafish
Heras, F. J. H., Romero-Ferrero, F., Hinz, R. C., & de Polavieja, G. G. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007354 · Cited by 103 · see citing works
Automatically tracking neurons in a moving and deforming brain
Nguyen, J. P., Linder, A. N., Plummer, G. S., Shaevitz, J. W., & Leifer, A. M. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005517 · Cited by 102 · see citing works
Vortex arrays and ciliary tangles underlie the feeding–swimming trade-off in starfish larvae
Gilpin, W., Prakash, V. N., & Prakash, M. (2016) · Nature Physics · doi:10.1038/nphys3981 · Cited by 102 · see citing works
Xian, B., Shen, J., Chen, W., Sun, N., Qiao, N., Jiang, D., Yu, T., Men, Y., Han, Z., Pang, Y., et al. (2013) · Aging Cell · doi:10.1111/acel.12063 · Cited by 102 · see citing works
Intermittent Motion in Desert Locusts: Behavioural Complexity in Simple Environments
Bazazi, S., Bartumeus, F., Hale, J. J., & Couzin, I. D. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002498 · Cited by 101 · see citing works
Active sensation during orientation behavior in the Drosophila larva: more sense than luck
Gómez-Marín, À., & Louis, M. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.11.008 · Cited by 98 · see citing works
Wang, W., Douglas, D. L., Zhang, J., Kumari, S., Enuameh, M. S., Dai, Y., Wallace, C. T., Watkins, S. C., Shu, W., & Xing, J. (2020) · Science Advances · doi:10.1126/sciadv.aba9319 · Cited by 90 · see citing works
Backholm, M., Ryu, W. S., & Dalnoki-Veress, K. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1219965110 · Cited by 94 · see citing works
Self-citationEmergence of long timescales and stereotyped behaviors in Caenorhabditis elegans
Stephens, G. J., de Mesquita, M. B., Ryu, W. S., & Bialek, W. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1007868108 · Cited by 89 · see citing works
Self-citationC. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays
Kauffman, A., Parsons, L. R., Stein, G. M., Wills, A. A., Kaletsky, R. L., & Murphy, C. T. (2011) · Journal of Visualized Experiments · doi:10.3791/2490 · Cited by 92 · see citing works
Anticipation of temporally structured events in the brain
Lee, C. S., Aly, M., & Baldassano, C. A. (2021) · eLife · doi:10.7554/elife.64972 · Cited by 91 · see citing works
The surveillance state of behavioral automation
Schaefer, A. T., & Claridge‐Chang, A. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.11.004 · Cited by 90 · see citing works
Javer, A., Ripoll-Sánchez, L., & Brown, A. E. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0375 · Cited by 89 · see citing works
Biderman, D., Whiteway, M. R., Hurwitz, C., Greenspan, N., Lee, R. S., Vishnubhotla, A., Warren, R., Pedraja, F., Noone, D., Schartner, M. M., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02319-1 · Cited by 100 · see citing works
Resolving coiled shapes reveals new reorientation behaviors in C. elegans
Broekmans, O. D., Rodgers, J. B., Ryu, W. S., & Stephens, G. J. (2016) · eLife · doi:10.7554/elife.17227 · Cited by 86 · see citing works
Self-citationInferring the Dynamics of Underdamped Stochastic Systems
Brückner, D. B., Ronceray, P., & Broedersz, C. P. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.058103 · Cited by 85 · see citing works
Capturing the continuous complexity of behaviour in Caenorhabditis elegans
Ahamed, T., Costa, A. C., & Stephens, G. J. (2020) · Nature Physics · doi:10.1038/s41567-020-01036-8 · Cited by 80 · see citing works
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Gallagher, T., Bjorness, T., Greene, R., You, Y.-J., & Avery, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059865 · Cited by 85 · see citing works
Cermak, N., Yu, S. K., Clark, R., Huang, Y.-C., Baskoylu, S., & Flavell, S. W. (2020) · eLife · doi:10.7554/elife.57093 · Cited by 78 · see citing works
Decision-making behaviors: weighing ethology, complexity, and sensorimotor compatibility
Juavinett, A. L., Erlich, J. C., & Churchland, A. K. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.11.001 · Cited by 82 · see citing works
Learning the dynamics of cell–cell interactions in confined cell migration
Brückner, D. B., Arlt, N., Fink, A., Ronceray, P., Rädler, J. O., & Broedersz, C. P. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016602118 · Cited by 82 · see citing works
Gómez-Marín, À., Partoune, N., Stephens, G. J., & Louis, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041642 · Cited by 81 · see citing works
Self-citationIwanir, S., Tramm, N., Nagy, S., Wright, C. W., Ish, D., & Biron, D. (2013) · SLEEP · doi:10.5665/sleep.2456 · Cited by 78 · see citing works
Han, S., Taralova, E. H., Dupré, C., & Yuste, R. (2018) · eLife · doi:10.7554/elife.32605 · Cited by 74 · see citing works
Mechanisms of odor-tracking: multiple sensors for enhanced perception and behavior
Gómez-Marín, À. (2010) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2010.00006 · Cited by 74 · see citing works
Adaptive, locally linear models of complex dynamics
Costa, A. C., Ahamed, T., & Stephens, G. J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1813476116 · Cited by 69 · see citing works
Self-citationMechanical intelligence simplifies control in terrestrial limbless locomotion
Wang, T., Pierce, C., Kojouharov, V., Chong, B., Diaz, K., Lu, H., & Goldman, D. I. (2023) · Science Robotics · doi:10.1126/scirobotics.adi2243 · Cited by 73 · see citing works
Kaplan, H. S., Nichols, A. L. A., & Zimmer, M. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0371 · Cited by 71 · see citing works
The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog
Girdhar, K., Gruebele, M., & Chemla, Y. R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128668 · Cited by 60 · see citing works
“It may be surprising at a first glance that fish larvae require only three basis functions to describe their backbone shapes, whereas worms analyzed in [18] required four.”
BackgroundMethodInfluentialStirman, J. N., Brauner, M., GOTTSCHALK, A. R., & Lu, H. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.05.019 · Cited by 69 · see citing works
Systematic exploration of unsupervised methods for mapping behavior
Todd, J. G., Kain, J. S., & de Bivort, B. L. (2017) · Physical Biology · doi:10.1088/1478-3975/14/1/015002 · Cited by 62 · see citing works
Nearest neighbours reveal fast and slow components of motor learning
Kollmorgen, S., Hahnloser, R. H. R., & Mante, V. (2020) · Nature · doi:10.1038/s41586-019-1892-x · Cited by 66 · see citing works
Wang, S. J., & Wang, Z. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069653 · Cited by 67 · see citing works
Flow-Based Network Analysis of the Caenorhabditis elegans Connectome
Bacik, K. A., Schaub, M. T., Beguerisse-Díaz, M., Billeh, Y. N., & Barahona, M. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005055 · Cited by 67 · see citing works
WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans
Hebert, L., Ahamed, T., Costa, A. C., O’Shaughnessy, L., & Stephens, G. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008914 · Cited by 66 · see citing works
Self-citationFrom Modes to Movement in the Behavior of Caenorhabditis elegans
Stephens, G. J., Johnson-Kerner, B., Bialek, W., & Ryu, W. S. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013914 · Cited by 65 · see citing works
Self-citationTemporal processing and context dependency in Caenorhabditis elegans response to mechanosensation
Liu, M., Sharma, A. K., Shaevitz, J. W., & Leifer, A. M. (2018) · eLife · doi:10.7554/elife.36419 · Cited by 58 · see citing works
Schwarz, R. F., Branicky, R. S., Grundy, L. J., Schafer, W. R., & Brown, A. E. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004322 · Cited by 62 · see citing works
Stirman, J. N., Crane, M. M., Husson, S. J., GOTTSCHALK, A. R., & Lu, H. (2012) · Nature Protocols · doi:10.1038/nprot.2011.433 · Cited by 61 · see citing works
Kinematic gait synthesis for snake robots
Gong, C., Travers, M. J., Astley, H. C., Lu, L., MENDELSON, J. R. I., Goldman, D. I., & Choset, H. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364915593793 · Cited by 61 · see citing works
Hums, I., Riedl, J., Mende, F., Kato, S., Kaplan, H. S., Latham, R., Sonntag, M., Traunmüller, L., & Zimmer, M. (2016) · eLife · doi:10.7554/elife.14116 · Cited by 60 · see citing works
The role of hydrodynamics in collective motions of fish schools and bioinspired underwater robots
Ko, H., LAUDER, G. V., & Nagpal, R. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0357 · Cited by 57 · see citing works
Identifying Prototypical Components in Behaviour Using Clustering Algorithms
Braun, E., Geurten, B. R. H., & Egelhaaf, M. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009361 · Cited by 60 · see citing works
Serotonin promotes exploitation in complex environments by accelerating decision-making
Iwanir, S., Brown, A. S., Nagy, S., Najjar, D., Kazakov, A., Lee, K. S., Zaslaver, A., Levine, E., & Biron, D. (2016) · BMC Biology · doi:10.1186/s12915-016-0232-y · Cited by 57 · see citing works
Mechanistic analysis of the search behaviour of Caenorhabditis elegans
Salvador, L. C. M., Bartumeus, F., Levin, S. A., & Ryu, W. S. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.1092 · Cited by 58 · see citing works
Self-citation
- Authors
- Stephens, G. J., Johnson-Kerner, B., Bialek, W., & Ryu, W. S.
- Affiliations
- Princeton University (US)
- Corresponding author
- Stephens, Greg J.
- Ryu, William S.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-04-25
- Volume
- 4
- Issue
- 4
- Pages
- e1000028
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2276863
- OpenAlex
- W2142254043
- Cited by
- 509
- References
- 45
- Citations by year
- 2026: 28 · 2025: 28 · 2024: 41 · 2023: 31 · 2022: 38 · 2021: 47 · 2020: 45 · 2019: 48 · 2018: 29 · 2017: 26 · 2016: 22 · 2015: 23 · 2014: 19 · 2013: 19 · 2012: 24
- FWCI
- 5.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurobiology and Insect Physiology Research
- Physical Sciences › Engineering › Mechanical Engineering › Advanced Materials and Mechanics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- Caenorhabditis elegans, worm locomotion, body shape, equations of motion, attractors, thermal stimuli, behavioral synchronization
- MeSH terms
- Animals, Behavior, Animal, Computer Simulation, Gait, Locomotion, Models, Biological, Caenorhabditis elegans
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