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Context-dependent adaptation in a neural computation

Charles J. Edelson1,2,*,†, Sima Setayeshgar1, William Bialek2, and Rob R. de Ruyter van Steveninck1

  • *Contact author: ce3721@princeton.edu
  • †Present Address: Princeton University, Princeton, NJ 08544.
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Phys. Rev. E 114, 034408 – Published 21 September, 2026

DOI: https://doi.org/10.1103/x56k-n2hb

Abstract

Brains adapt to the statistical structure of their input. In the visual system, local light intensities change rapidly, the variance of the intensity changes more slowly, and the dynamic range of contrast itself changes more slowly still. We use a motion-sensitive neuron in the fly visual system to probe this hierarchy of adaptation phenomena, delivering naturalistic stimuli that have been simplified to have a clear separation of timescales. We show that the neural response to visual motion depends on contrast and that this dependence itself varies with context. Using the spike-triggered average velocity trajectory as a response measure, we find that this context dependence is confined to a low-dimensional space, dominated by a single principal dimension. To quantify the functional consequences of this structure, we measure the mutual information between stimulus and single-spike responses under models with and without adaptive structure. Across a wide range of conditions, incorporating adaptation consistently increases mutual information, indicating that context-dependent changes in the encoding model improve information transmission.

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References (78)

  1. R. E. Lenski, What is adaptation by natural selection? Perspectives of an experimental microbiologist, PLoS Genet. 13, e1006668 (2017).
  2. S. M. Block, J. E. Segall, and H. C. Berg, Adaptation kinetics in bacterial chemotaxis, J. Bacteriol. 154, 312 (1983).
  3. E. D. Adrian, The Basis of Sensation: The Action of the Sense Organs (W. W. Norton, New York, 1928).
  4. B. Wark, B. N. Lundstrom, and A. Fairhall, Sensory adaptation, Curr. Opin. Neurobiol. 17, 423 (2007).
  5. A. I. Weber, K. Krishnamurthy, and A. L. Fairhall, Coding principles in adaptation, Annu. Rev. Vis. Sci. 5, 427 (2019).
  6. H. B. Barlow, National Physical Laboratory Symposium on the Mechanization of Thought Processes (HM Stationery Office, London, 1959), Vol. 2, pp. 537–574.
  7. H. B. Barlow, in Sensory Communication, edited by W. A. Rosenblith (MIT Press, Cambridge, MA, 1961), pp. 217–233.
  8. S. B. Laughlin, A simple coding procedure enhances a neuron's information capacity, Zeitschr. Naturforsch. C 36, 910 (1981).
  9. S. M. Smirnakis, M. J. Berry, D. K. Warland, W. Bialek, and M. Meister, Adaptation of retinal processing to image contrast and spatial scale, Nature (London) 386, 69 (1997).
  10. M. Egelhaaf and R. Kern, Vision in flying insects, Curr. Opin. Neurobiol. 12, 699 (2002).
  11. W. Bialek, R. R. de Ruyter van Steveninck, and N. Tishby, in Proceedings of the IEEE International Symposium on Information Theory (IEEE, Los Alamitos, CA, 2006), pp. 659–663.
  12. A. Kohn, Visual adaptation: Physiology, mechanisms, and functional benefits, J. Neurophysiol. 97, 3155 (2007).
  13. W. Bialek, Biophysics: Searching for Principles (Princeton Univerity Press, Princeton NJ, 2012).
  14. M. A. Webster, Visual adaptation, Annu. Rev. Vis. Sci. 1, 547 (2015).
  15. S. E. Palmer, O. Marre, M. J. Berry, and W. Bialek, Predictive information in a sensory population, Proc. Natl. Acad. Sci. USA 112, 6908 (2015).
  16. G. Tkačik and W. Bialek, Information processing in living systems, Annu. Rev. Condens. Matter Phys. 7, 89 (2016).
  17. M. Potters and W. Bialek, Statistical mechanics and visual signal processing, J. Phys. I 4, 1755 (1994).
  18. Y. Weiss, E. P. Simoncelli, and E. H. Adelson, Motion illusions as optimal percepts, Nat. Neurosci. 5, 598 (2002).
  19. H. Barlow, Conditions for versatile learning, Helmholtz's unconscious inference, and the task of perception, Vision Res. 30, 1561 (1990).
  20. Perception as Bayesian Inference, edited by D. C. Knill and W. Richards (Cambridge University Press, Cambridge, UK, 1996).
  21. F. Rieke, D. Warland, R. de R. van Steveninck, and W. Bialek, Spikes: Exploring the Neural Code (MIT Press, Cambridge, MA, 1997).
  22. E. Chichilnisky, A simple white noise analysis of neuronal light responses, Network: Comput. Neural Syst. 12, 199 (2001).
  23. T. Sharpee, N. C. Rust, and W. Bialek, Analyzing neural responses to natural signals: Maximally informative dimensions, Neural Comput. 16, 223 (2004).
  24. J. W. Pillow and E. P. Simoncelli, Dimensionality reduction in neural models: An information-theoretic generalization of spike-triggered average and covariance analysis, J. Vision 6, 9 (2006).
  25. N. A. Lesica, J. Jin, C. Weng, C.-I. Yeh, D. A. Butts, G. B. Stanley, and J.-M. Alonso, Adaptation to stimulus contrast and correlations during natural visual stimulation, Neuron 55, 479 (2007).
  26. T. O. Sharpee, K. I. Nagel, and A. J. Doupe, Two-dimensional adaptation in the auditory forebrain, J. Neurophysiol. 106, 1841 (2011).
  27. M. A. Goldin, B. Lefebvre, S. Virgili, M. K. Pham Van Cang, A. Ecker, T. Mora, U. Ferrari, and O. Marre, Context-dependent selectivity to natural images in the retina, Nat. Commun. 13, 5556 (2022).
  28. A. L. Fairhall, G. D. Lewen, W. Bialek, and R. R. de R. van Steveninck, Efficiency and ambiguity in an adaptive neural code, Nature (London) 412, 787 (2001).
  29. N. Ulanovsky, L. Las, D. Farkas, and I. Nelken, Multiple time scales of adaptation in auditory cortex neurons, J. Neurosci. 24, 10440 (2004).
  30. B. Wark, A. Fairhall, and F. Rieke, Timescales of inference in visual adaptation, Neuron 61, 750 (2009).
  31. M. Chalk, O. Marre, and G. Tkačik, Toward a unified theory of efficient, predictive, and sparse coding, Proc. Natl. Acad. Sci. USA 115, 186 (2018).
  32. W. F. Młynarski and A. M. Hermundstad, Efficient and adaptive sensory codes, Nat. Neurosci. 24, 998 (2021).
  33. A. S. Dorkenwald, A. Matsliah, A. R. Sterling, P. Schlegel, S.-C. Yu, C. E. McKellar, A. Lin, M. Costa, K. Eichler, Y. Yin, W. Silversmith, C. Schneider-Mizell, C. S. Jordan, D. Brittain, A. Halageri, K. Kuehner, O. Ogedengbe, R. Morey, J. Gager, K. Kruk, et al., Neuronal wiring diagram of an adult brain, Nature (London) 634, 124 (2024).
  34. A. Borst and L. N. Groschner, How flies see motion, Annu. Rev. Neurosci. 46, 17 (2023).
  35. L. G. Bishop and D. G. Keehn, Two types of neurones sensitive to motion in the optic lobe of the fly, Nature (London) 212, 1374 (1966).
  36. R. Pierantoni, A look into the cock-pit of the fly, Cell Tissue Res. 171, 101 (1976).
  37. K. Hausen, in Photoreception and Vision in Invertebrates, edited by M. A. Ali, NATO ASI Series Vol. 74 (Springer, Boston, MA, 1984), pp. 523–559.
  38. H. G. Krapp, Estimation of self-motion for gaze and flight stabilization in flying insects, Navigation 55, 147 (2008).
  39. S. J. Huston and H. G. Krapp, Visuomotor transformation in the fly gaze stabilization system, PLoS Biol. 6, e173 (2008).
  40. T. W. Ullrich, R. Kern, and M. Egelhaaf, Influence of environmental information in natural scenes and the effects of motion adaptation on a fly motion-sensitive neuron during simulated flight, Biol. Open 4, 13 (2015).
  41. H. Wei, H. Y. Kyung, P. J. Kim, and C. Desplan, The diversity of lobula plate tangential cells (LPTCs) in the Drosophila motion vision system, J. Comp. Physiol. A 206, 139 (2020).
  42. T. Maddess and S. B. Laughlin, Adaptation of the motion-sensitive neuron H1 is generated locally and governed by contrast frequency, Proc. Royal Soc. Lond. Ser. B. Biol. Sci. 225, 251 (1985).
  43. R. R. de R. van Steveninck, W. Zaagman, and H. Mastebroek, Adaptation of transient responses of a movement-sensitive neuron in the visual system of the blowfly Calliphora erythrocephala, Biol. Cybern. 54, 223 (1986).
  44. N. Brenner, W. Bialek, and R. de R. van Steveninck, Adaptive rescaling maximizes information transmission, Neuron 26, 695 (2000).
  45. C. Reisenman, J. Haag, and A. Borst, Adaptation of response transients in fly motion vision. I: Experiments, Vision Res. 43, 1293 (2003).
  46. V. Flanagin, Dynamic adaptation in fly motion vision, Ph.D. thesis, Ludwig-Maximilians-Universität, München, 2006.
  47. R. R. de R. van Steveninck, G. D. Lewen, S. P. Strong, R. Koberle, and W. Bialek, Reproducibility and variability in neural spike trains, Science 275, 1805 (1997).
  48. R. de R. van Steveninck and W. Bialek, in Models of Neural Networks IV: Early Vision and Attention (Springer-Verlag, Berlin, 2002), pp. 313–371.
  49. I. Nemenman, G. D. Lewen, W. Bialek, and R. R. de R. van Steveninck, Neural coding of natural stimuli: Information at sub-millisecond resolution, PLoS Comput. Biol. 4, e1000025 (2008).
  50. E. de Boer and P. Kuyper, Triggered correlation, IEEE Trans. Biomed. Eng. BME-15, 169 (1968).
  51. N. Brenner, S. P. Strong, R. Koberle, W. Bialek, and R. R. de R. van Steveninck, Synergy in a neural code, Neural Comput. 12, 1531 (2000).
  52. G. J. Burton and I. R. Moorhead, Color and spatial structure in natural scenes, Appl. Opt. 26, 157 (1987).
  53. D. J. Field, Relations between the statistics of natural images and the response properties of cortical cells, J. Opt. Soc. Am. A 4, 2379 (1987).
  54. D. L. Ruderman and W. Bialek, Statistics of natural images: Scaling in the woods, Phys. Rev. Lett. 73, 814 (1994).
  55. N. Wiener, Nonlinear Problems in Random Theory (MIT Press, Cambridge, MA, 1958).
  56. R. de R. van Steveninck and W. Bialek, Reliability and statistical efficiency of a blowfly movement-sensitive neuron, Phil. Trans. R. Soc. B 348, 321 (1995).
  57. W. Bialek, in Les Houches Summer School Lecture Notes: Theoretical Biological Physics 2023, edited by A.-F. Bitbol, T. Mora, I. Nemenman, and A. M. Walczak (SciPost Physics Lecture Notes, Amsterdam, 2024).
  58. B. Hassenstein and W. Reichardt, Systemtheoretische Analyse der Zeit-, Reihenfolgen- und Vorzeichenauswertung bei der Bewegungsperzeption des Rüsselkäfers Chlorophanus, Zeitschr. Naturforsch. B 11, 513 (1956).
  59. W. Reichardt and T. Poggio, Visual control of orientation behaviour in the fly: Part I. A quantitative analysis, Q. Rev. Biophys. 9, 311 (1976).
  60. M. Egelhaaf, A. Borst, and W. Reichardt, Computational structure of a biological motion-detection system as revealed by local detector analysis in the fly's nervous system, J. Opt. Soc. Am. A 6, 1070 (1989).
  61. R. de R. van Steveninck, W. Bialek, M. Potters, and R. Carlson, in Proceedings of IEEE International Conference on Systems, Man and Cybernetics (IEEE, Los Alamitos, TX, 1994), Vol. 1, pp. 302–307.
  62. R. A. Harris, D. C. O'Carroll, and S. B. Laughlin, Contrast gain reduction in fly motion adaptation, Neuron 28, 595 (2000).
  63. S. R. Sinha, W. Bialek, and R. R. de R. van Steveninck, Optimal local estimates of visual motion in a natural environment, Phys. Rev. Lett. 126, 018101 (2021).
  64. A.-K. Warzecha and M. Egelhaaf, Response latency of a motion-sensitive neuron in the fly visual system: Dependence on stimulus parameters and physiological conditions, Vision Res. 40, 2973 (2000).
  65. A. D. Straw, T. Rainsford, and D. C. O'Carroll, Contrast sensitivity of insect motion detectors to natural images, J. Vision 8, 32 (2008).
  66. D. L. Ruderman, Origins of scaling in natural images, Vision Res. 37, 3385 (1997).
  67. W. E. Vinje and J. L. Gallant, Natural stimulation of the nonclassical receptive field increases information transmission efficiency in V1, J. Neurosci. 22, 2904 (2002).
  68. G. Vasserman, E. Schneidman, and R. Segev, Adaptive colour contrast coding in the salamander retina efficiently matches natural scene statistics, PLoS ONE 8, e79163 (2013).
  69. G. Felsen, J. Touryan, F. Han, and Y. Dan, Cortical sensitivity to visual features in natural scenes, PLoS Biol. 3, e342 (2005).
  70. J. E. Cooke, A. J. King, B. D. B. Willmore, and J. W. H. Schnupp, Contrast gain control in mouse auditory cortex, J. Neurophysiol. 120, 1872 (2018).
  71. M. Maravall, R. S. Petersen, A. L. Fairhall, E. Arabzadeh, and M. E. Diamond, Shifts in coding properties and maintenance of information transmission during adaptation in barrel cortex, PLoS Biol. 5, e19 (2007).
  72. K. M. Tye, E. K. Miller, F. H. Taschbach, M. K. Benna, M. Rigotti, and S. Fusi, Mixed selectivity: Cellular computations for complexity, Neuron 112, 2289 (2024).
  73. J. K. Liu and T. Gollisch, Spike-triggered covariance analysis reveals phenomenological diversity of contrast adaptation in the retina, PLoS Comput. Biol. 11, e1004425 (2015).
  74. D. Beersma, D. Stavenga, and J. Kuiper, Retinal lattice, visual field and binocularities in flies, J. Comp. Physiol. 119, 207 (1977).
  75. J. Schwemer, Pathways of visual pigment regeneration in fly photoreceptor cells, Biophys. Struct. Mech. 9, 287 (1983).
  76. J. Schwemer, Renewal of visual pigment in photoreceptors of the blowfly, J. Comp. Physiol. A 154, 535 (1984).
  77. L. Zhou, The dynamics of adaptation in the fly early visual system, Ph.D. thesis, Indiana University, Bloomington, 2014.
  78. C. J. Edelson, P. Smith, S. Setayeshgar, W. Bialek, and R. R. de R. van Steveninck, The FlEye camera: Sampling the joint distribution of natural scenes and motion, Phys. Rev. E 113, 044412 (2026).

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Phys. Rev. E 114, 034408– Published 21 September, 2026
  • Received 1 September 2025
  • Revised 8 June 2026
  • Accepted 4 August 2026
Article part of CHORUS
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