- Open Access
Logarithmic theory of visuomotor stabilization
Phys. Rev. Research 8, 023027 – Published 8 April, 2026
DOI: https://doi.org/10.1103/2s81-6lr6
Abstract
Although many animals rely on visual information to navigate, optic flow is inherently ambiguous as it confounds information about motion speed and object distance. As a result, the visual feedback produced by a given motor command is context dependent and requires an appropriately adapted response. Recent experiments have investigated how the fish Danionella cerebrum use visual cues to stabilize their position against simulated external currents. Logarithmic sensorimotor transformations have been proposed to enable adaptive responses to perturbations while preventing delay-induced instabilities. Here, we develop the theoretical framework introduced for continuous locomotion to show how logarithmic coding naturally gives rise to this adaptive behavior. The system is modeled by a nonlinear delay differential equation, which is analyzed using dynamical systems theory. We further analyze experimental data to uncover the mechanisms underlying swimming initiation and positional drift correction. Finally, we extend our framework to intermittent locomotion, resulting in a nonlinear difference equation, and show that it still produces robust adaptive behavior. This is motivated by the literature on zebrafish, where visuomotor stabilization has been extensively studied, but burst-and-coast swimming obscures the underlying adaptation mechanism. We show that our theory can reproduce the experimental results reported for motor adaptation in zebrafish without invoking internal models. Overall, our results highlight logarithmic coding as a unifying principle for visuomotor stability across continuous and intermittent locomotor regimes.
Physics Subject Headings (PhySH)
Article Text
References (62)
- M. F. Land and D.-E. Nilsson, Animal Eyes, 2nd ed., Oxford Animal Biology Series (Oxford University Press, Oxford, 2012).
- J. J. Gibson, Visually controlled locomotion and visual orientation in animals, Br. J. Psychol. 49, 182 (1958).
- R. Shadmehr, M. A. Smith, and J. W. Krakauer, Error correction, sensory prediction, and adaptation in motor control, Annu. Rev. Neurosci. 33, 89 (2010).
- S. G. Tzafestas, Mobile robot control and navigation: A global overview, J. Intell. Robot. Syst. 91, 35 (2018).
- R. C. Miall and D. M. Wolpert, Forward models for physiological motor control, Neural Networks 9, 1265 (1996).
- M. Kawato, Internal models for motor control and trajectory planning, Curr. Opin. Neurobiol. 9, 718 (1999).
- C. Tin and C.-S. Poon, Internal models in sensorimotor integration: Perspectives from adaptive control theory, J. Neural Eng. 2, S147 (2005).
- D. Nguyen-Tuong and J. Peters, Model learning for robot control: A survey, Cogn. Process. 12, 319 (2011).
- J. J. Koenderink, Optic flow, Vision Res. 26, 161 (1986).
- J. R. Serres and F. Ruffier, Optic flow-based collision-free strategies: From insects to robots, Arthropod Struct. Dev. 46, 703 (2017).
- M. V. Srinivasan and S. Zhang, Visual motor computations in insects, Annu. Rev. Neurosci. 27, 679 (2004).
- G. P. Arnold, Rheotropism in fishes, Biol. Rev. 49, 515 (1974).
- G. D. McCann, G. F. MacGinitie, and J. W. S. Pringle, Optomotor response studies of insect vision, Proc. Roy. Soc. London Ser. B 163, 369 (1997).
- E. Shaw and A. Tucker, The optomotor reaction of schooling carangid fishes, Anim. Behav. 13, 330 (1965).
- R. Portugues and F. Engert, The neural basis of visual behaviors in the larval zebrafish, Curr. Opin. Neurobiol. 19, 644 (2009).
- M. B. Orger and G. G. d. Polavieja, Zebrafish behavior: Opportunities and challenges, Annu. Rev. Neurosci. 40, 125 (2017).
- J. H. Bollmann, The zebrafish visual system: From circuits to behavior, Annu. Rev. Vis. Sci. 5, 269 (2019).
- M. B. Ahrens, M. B. Orger, D. N. Robson, J. M. Li, and P. J. Keller, Whole-brain functional imaging at cellular resolution using light-sheet microscopy, Nat. Methods 10, 413 (2013).
- T. Panier, S. A. Romano, R. Olive, T. Pietri, G. Sumbre, R. Candelier, and G. Debrégeas, Fast functional imaging of multiple brain regions in intact zebrafish larvae using Selective Plane Illumination Microscopy, Front. Neural Circuits 7, 65 (2013).
- R. Portugues and F. Engert, Adaptive locomotor behavior in larval zebrafish, Front. Syst. Neurosci. 5, 72 (2011).
- M. B. Ahrens, J. M. Li, M. B. Orger, D. N. Robson, A. F. Schier, F. Engert, and R. Portugues, Brain-wide neuronal dynamics during motor adaptation in zebrafish, Nature (London) 485, 471 (2012).
- E. Yang, M. F. Zwart, B. James, M. Rubinov, Z. Wei, S. Narayan, N. Vladimirov, B. D. Mensh, J. E. Fitzgerald, and M. B. Ahrens, A brainstem integrator for self-location memory and positional homeostasis in zebrafish, Cell 185, 5011 (2022).
- D. A. Markov, L. Petrucco, A. M. Kist, and R. Portugues, A cerebellar internal model calibrates a feedback controller involved in sensorimotor control, Nat. Commun. 12, 6694 (2021).
- J. G. Holman, W. W. K. Lai, P. Pichler, D. Saska, L. Lagnado, and C. L. Buckley, A behavioral and modeling study of control algorithms underlying the translational optomotor response in larval zebrafish with implications for neural circuit function, PLoS Comput. Biol. 19, e1010924 (2023).
- R. Tanaka and R. Portugues, Algorithmic dissection of optic flow memory in larval zebrafish, Curr. Biol. 35, 4870 (2025).
- L. Schulze, J. Henninger, M. Kadobianskyi, T. Chaigne, A. I. Faustino, N. Hakiy, S. Albadri, M. Schuelke, L. Maler, F. Del Bene, and B. Judkewitz, Transparent Danionella translucida as a genetically tractable vertebrate brain model, Nat. Methods 15, 977 (2018).
- R. Britz, K. W. Conway, and L. Rüber, The emerging vertebrate model species for neurophysiological studies is Danionella cerebrum, new species (Teleostei: Cyprinidae), Sci. Rep. 11, 18942 (2021).
- M. Hoffmann, J. Henninger, J. Veith, L. Richter, and B. Judkewitz, Blazed oblique plane microscopy reveals scale-invariant inference of brain-wide population activity, Nat. Commun. 14, 8019 (2023).
- T. J. Lee and K. L. Briggman, Visually guided and context-dependent spatial navigation in the translucent fish Danionella cerebrum, Curr. Biol. 33, 5467 (2023).
- V. A. N. O. Cook, A. H. Groneberg, M. Hoffmann, M. Kadobianskyi, J. Veith, L. Schulze, J. Henninger, R. Britz, and B. Judkewitz, Ultrafast sound production mechanism in one of the smallest vertebrates, Proc. Natl. Acad. Sci. USA 121, e2314017121 (2024).
- J. Veith, T. Chaigne, A. Svanidze, L. E. Dressler, M. Hoffmann, B. Gerhardt, and B. Judkewitz, The mechanism for directional hearing in fish, Nature (London) 631, 118 (2024).
- D. Zada, L. Schulze, J.-H. Yu, P. Tarabishi, J. L. Napoli, J. Milan, and M. Lovett-Barron, Development of neural circuits for social motion perception in schooling fish, Curr. Biol. 34, 3380 (2024).
- A. H. Groneberg, L. E. Dressler, M. Kadobianskyi, J. Müller, and B. Judkewitz, Development of sound production in Danionella cerebrum, J. Exp. Biol. 227, jeb247782 (2024).
- G. Rajan, J. Lafaye, G. Faini, M. Carbo-Tano, K. Duroure, D. Tanese, T. Panier, R. Candelier, J. Henninger, R. Britz, B. Judkewitz, C. Gebhardt, V. Emiliani, G. Debregeas, C. Wyart, and F. Del Bene, Evolutionary divergence of locomotion in two related vertebrate species, Cell Rep. 38, 110585 (2022).
- L. Demarchi, M. Coraggioso, A. Hubert, T. Panier, G. Morvan-Dubois, V. Bormuth, and G. Debrégeas, Logarithmic coding leads to adaptive stabilization in the presence of sensorimotor delays, Proc. Natl. Acad. Sci. USA 122, e2510385122 (2025).
- J. C. Baird and E. J. Noma, Fundamentals of Scaling and Psychophysics (Wiley, New York, 1978).
- E. Henneman, Relation between size of neurons and their susceptibility to discharge, Science 126, 1345 (1957).
- K. E. Severi, R. Portugues, J. C. Marques, D. M. O’Malley, M. B. Orger, and F. Engert, Neural control and modulation of swimming speed in the larval zebrafish, Neuron 83, 692 (2014).
- R. Portugues, M. Haesemeyer, M. L. Blum, and F. Engert, Whole-field visual motion drives swimming in larval zebrafish via a stochastic process, J. Exp. Biol. 218, 1433 (2015).
- T. W. Dunn and J. E. Fitzgerald, Correcting for physical distortions in visual stimuli improves reproducibility in zebrafish neuroscience, eLife 9, e53684 (2020).
- S. D. Brown and A. Heathcote, The simplest complete model of choice response time: Linear ballistic accumulation, Cognit. Psychol. 57, 153 (2008).
- C. Donkin and L. Van Maanen, Piéron's law is not just an artifact of the response mechanism, J. Math. Psychol. 62-63, 22 (2014).
- G. Buzsáki and K. Mizuseki, The log-dynamic brain: How skewed distributions affect network operations, Nat. Rev. Neurosci. 15, 264 (2014).
- R. Ulrich and J. Miller, Information processing models generating lognormally distributed reaction times, J. Math. Psychol. 37, 513 (1993).
- M. Coraggioso, L. Demarchi, R. Wong, V. Dichio, C. Chaumeton, T. Panier, G. Morvan-Dubois, G. Goodhill, V. Bormuth, and G. Debrégeas, A sensorimotor instability drives a locomotor transition during fish development, bioRxiv.
- R. E. Johnson, S. Linderman, T. Panier, C. L. Wee, E. Song, K. J. Herrera, A. Miller, and F. Engert, Probabilistic models of larval zebrafish behavior reveal structure on many scales, Curr. Biol. 30, 70 (2020).
- G. L. Goc, J. Lafaye, S. Karpenko, V. Bormuth, R. Candelier, and G. Debrégeas, Thermal modulation of zebrafish exploratory statistics reveals constraints on individual behavioral variability, BMC Biol. 19, 208 (2021).
- D. Clift, H. Richendrfer, R. J. Thorn, R. M. Colwill, and R. Creton, High-throughput analysis of behavior in zebrafish larvae: Effects of feeding, Zebrafish 11, 455 (2014).
- S. Elaydi, An Introduction to Difference Equations, 3rd ed., Undergraduate Texts in Mathematics (Springer, New York, 2005).
- T.-Y. Li and J. A. Yorke, Period three implies chaos, Am. Math. Mon. 82, 985 (1975).
- H. Hatze, A teleological explanation of Weber's law and the motor unit size law, Bull. Math. Biol. 41, 407 (1979).
- W. Senn, K. Wyler, H. P. Clamann, J. Kleinle, H. R. Lüscher, and L. Müller, Size principle and information theory, Biol. Cybern. 76, 11 (1997).
- R. D. Portugal and B. F. Svaiter, Weber-Fechner law and the optimality of the logarithmic scale, Minds Mach. 21, 73 (2011).
- R. W. Friedrich, G. A. Jacobson, and P. Zhu, Circuit neuroscience in Zebrafish, Curr. Biol. 20, R371 (2010).
- T. Rose, P. M. Goltstein, R. Portugues, and O. Griesbeck, Putting a finishing touch on GECIs, Front. Mol. Neurosci. 7, 88 (2014).
- F. Ali and A. C. Kwan, Interpreting in vivo calcium signals from neuronal cell bodies, axons, and dendrites: A review, Neurophotonics 7, 1 (2019).
- F. R. Harden Jones, The reaction of fish to moving backgrounds, J. Exp. Biol. 40, 437 (1963).
- A. S. Mauss and A. Borst, Optic flow-based course control in insects, Curr. Opin. Neurobiol. 60, 21 (2020).
- Principles of Neural Science, 6th ed., edited by E. R. Kandel, J. Koester, S. Mack, and S. Siegelbaum (McGraw Hill, New York, 2021).
- H. Chao, Y. Gu, and M. Napolitano, A survey of optical flow techniques for robotics navigation applications, J. Intell. Robot. Syst. 73, 361 (2014).
- L. Demarchi, M. Coraggioso, A. Hubert, T. Panier, G. Morvan- Dubois, V. Bormuth, and G. Debrégeas, Data and code— Logarithmic coding leads to adaptive stabilization in the presence of sensorimotor delays, Zenodo (2025), https://doi.org/10.5281/zenodo.15187167.
- L. Demarchi, Code—A logarithmic theory of visuomotor stabilization, Zenodo (2026), https://doi.org/10.5281/zenodo.17628539.