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Is coding a relevant metaphor for the brain?

  • Romain Brette (a1)


“Neural coding” is a popular metaphor in neuroscience, where objective properties of the world are communicated to the brain in the form of spikes. Here I argue that this metaphor is often inappropriate and misleading. First, when neurons are said to encode experimental parameters, the neural code depends on experimental details that are not carried by the coding variable (e.g., the spike count). Thus, the representational power of neural codes is much more limited than generally implied. Second, neural codes carry information only by reference to things with known meaning. In contrast, perceptual systems must build information from relations between sensory signals and actions, forming an internal model. Neural codes are inadequate for this purpose because they are unstructured and therefore unable to represent relations. Third, coding variables are observables tied to the temporality of experiments, whereas spikes are timed actions that mediate coupling in a distributed dynamical system. The coding metaphor tries to fit the dynamic, circular, and distributed causal structure of the brain into a linear chain of transformations between observables, but the two causal structures are incongruent. I conclude that the neural coding metaphor cannot provide a valid basis for theories of brain function, because it is incompatible with both the causal structure of the brain and the representational requirements of cognition.

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Ahissar, E. & Assa, E. (2016) Perception as a closed-loop convergence process. eLife 5:12830. doi: 10.7554/eLife.12830.
Anderson, M. L. & Chemero, T. (2013) The problem with brain GUTs: Conflation of different senses of “prediction” threatens metaphysical disaster. Behavioral and Brain Sciences 36(3):204205.
Ashida, G. & Carr, C. E. (2011) Sound localization: Jeffress and beyond. Current Opinion in Neurobiology 21(5):745–51.
Barlow, H. (1961) Possible principles underlying the transformations of sensory messages. In: Sensory communication, ed. Rosenblith, W., pp. 217–34. MIT Press.
Barlow, H. B., Fitzhugh, R. & Kuffler, S. W. (1957) Change of organization in the receptive fields of the cat's retina during dark adaptation. Journal of Physiology 137:338–54.
Benichoux, V., Fontaine, B., Karino, S., Joris, P. X. & Brette, R. (2015) Neural tuning matches frequency-dependent time differences between the ears. eLife 4:06072.
Benichoux, V., Rébillat, M. & Brette, R. (2016) On the variation of interaural time differences with frequency. Journal of the Acoustical Society of America 139(4):1810–21.
Bialek, W., Nemenman, I. & Tishby, N. (2001) Predictability, complexity, and learning. Neural Computation 13(11):2409–63.
Bickhard, M. H. (2009) The interactivist model. Synthese 166(3):547–91. Available at:
Bickhard, M. H. (2015c) What could cognition be if not computation … or connectionism, or dynamic systems? Journal of Theoretical and Philosophical Psychology 35(1):5366. Available at:
Bickhard, M. H. & Terveen, L. (1996) Foundational issues in artificial intelligence and cognitive science: Impasse and solution (Advances in psychology, Vol. 109). Elsevier/North-Holland.
Bolz, J. & Gilbert, C. D. (1986) Generation of end-inhibition in the visual cortex via interlaminar connections. Nature 320:362–65.
Bonabeau, E., Theraulaz, G., Deneubourg, J. L., Aron, S. & Camazine, S. (1997) Self-organization in social insects. Trends in Ecology & Evolution 12(5):188–93.
Brette, R. (2010) On the interpretation of sensitivity analyses of neural responses. Journal of the Acoustical Society of America 128(5):2965–72.
Brette, R. (2012) Computing with neural synchrony. PLoS Computational Biology 8(6):e1002561.
Brette, R. (2015) Philosophy of the spike: Rate-based vs. spike-based theories of the brain. Frontiers in Systems Neuroscience 9:151.
Brette, R. (2016) Subjective physics. In: Closed loop neuroscience, ed. El Hady, A., pp. 146–70. Academic Press.
Brooks, R. A. (1991a) Intelligence without representation. Artificial Intelligence 47(1–3):139–59. doi:10.1016/0004-3702(91)90053-M.
Buzsáki, G. (2010) Neural syntax: Cell assemblies, synapsembles, and readers. Neuron 68:362–85.
Chanauria, N., Bharmauria, V., Bachatene, L., Cattan, S., Rouat, J. & Molotchnikoff, S. (2018) Sound induces change in orientation preference of V1 neurons: Audio-visual cross-influence. Preprint. bioRxiv:269589.
Chomsky, N. (1959) A review of B. F. Skinner's Verbal Behavior. Language 35:2658.
Cisek, P. (1999) Beyond the computer metaphor: Behaviour as interaction. Journal of Consciousness Studies 6(11/12):125–42.
Clark, A. (2013) Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Science 36:181204.
Constantinidis, C. & Klingberg, T. (2016) The neuroscience of working memory capacity and training. Nature Reviews Neuroscience 17:438–49.
Crick, F. (1979) Thinking about the brain. Scientific American 241:219–32.
deCharms, R. C. & Zador, A. (2000) Neural representation and the cortical code. Annual Review of Neuroscience 23:613–47.
Deco, G., Jirsa, V. K. & McIntosh, A. R. (2011) Emerging concepts for the dynamical organization of resting-state activity in the brain. Nature Reviews Neuroscience 12:4356.
Dennett, D. C. (1978) Why not the whole iguana? Behavioral and Brain Sciences 1:103–04.
Dewey, J. (1896) The reflex arc concept in psychology. Psychological Review 3(4), 357–70.
Eccles, J. C. (1965) Conscious experience and memory. In: Brain and conscious experience, pp. 314–44. Springer. Available at: [Accessed May 22, 2018].
Eckert, R. (1972) Bioelectric control of ciliary activity. Science 176:473–81.
Eckert, R. & Naitoh, Y. (1970) Passive electrical properties of Paramecium and problems of ciliary coordination. Journal of General Physiology 55:467–83.
Friston, K. (2009) The free-energy principle: A rough guide to the brain? Trends in Cognitive Sciences 13:293301.
Friston, K. (2010) The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11:127–38.
Gibson, J. J. (1979) The ecological approach to visual perception. Routledge.
Gilbert, C. D. & Li, W. (2013) Top-down influences on visual processing. Nature Reviews Neuroscience 14:350–63.
Gomez-Marin, A. (2017) Causal circuit explanations of behavior: Are necessity and sufficiency necessary and sufficient? In: Decoding neural circuit structure and function, ed. Çelik, A. & Wernet, M. F., pp. 283306. Springer. Available at: [Accessed June 27, 2018.]
Gomez-Marin, A. & Mainen, Z. F. (2016) Expanding perspectives on cognition in humans, animals, and machines. Current Opinion in Neurobiology 37:8591.
Goodman, D. F., Benichoux, V. & Brette, R. (2013) Decoding neural responses to temporal cues for sound localization. eLife 2(2):e01312.
Goodman, D. F. M. & Brette, R. (2010) Spike-timing-based computation in sound localization. PLoS Computational Biology 6(11):e1000993.
Grothe, B., Pecka, M. & McAlpine, D. (2010) Mechanisms of sound localization in mammals. Physiological Reviews 90(3):9831012.
Harnad, S. (1990b) The symbol grounding problem. Physica D: Nonlinear Phenomena 42(1–3):335–46.
Harper, N. S. & McAlpine, D. (2004) Optimal neural population coding of an auditory spatial cue. Nature 430:682–86.
Hosoya, T., Baccus, S. A. & Meister, M. (2005) Dynamic predictive coding by the retina. Nature 436:7177.
Hubel, D. H. & Wiesel, T. N. (1968) Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology 195:215–43.
Hurley, S. (2001) Perception and action: Alternative views. Synthese 129(1):340.
Jazayeri, M. & Movshon, J. A. (2006) Optimal representation of sensory information by neural populations. Nature Neuroscience 9(5):690–96.
Jeffress, L. A. (1948) A place theory of sound localisation. Journal of Comparative and Physiological Psychology 41(1):3539.
Jenkins, W. M. & Masterton, R. B. (1982) Sound localization: Effects of unilateral lesions in central auditory system. Journal of Neurophysiology 47:9871016.
Jennings, H. S. (1906) Behavior of the lower organisms. Columbia University Press/Macmillan. Available at: [Accessed December 20, 2015.]
Joris, P. X., Smith, P. H. & Yin, T. C. (1998) Coincidence detection in the auditory system: 50 years after Jeffress. Neuron 21(6):1235–38.
Kawato, M. (1997) Bidirectional theory approach to consciousness. In: Cognition, computation, and consciousness, ed. Ito, M., Miyashita, Y. & Rolls, E. T.. Oxford University Press.
Knill, D. C. & Pouget, A. (2004) The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences 27(12):712719 Available at: [Accessed July 10, 2014].
Kumar, A., Rotter, S. & Aertsen, A. (2010) Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding. Nature Reviews Neuroscience 11(9):615–27.
Lakoff, G. & Johnson, M. (1980a) Metaphors we live by. University of Chicago Press.
Laudanski, J., Zheng, Y. & Brette, R. (2014) A structural theory of pitch. eNeuro 1(1): 0033-14.2014. doi:
Le Mouel, C. & Brette, R. (2017) Mobility as the purpose of postural control. Frontiers in Computational Neuroscience 11:Article 67. Available at: [Accessed June 21, 2018.]
Macmillan, N. A. & Creelman, C. D. (2005) Detection theory: A user's guide (2nd edition). Lawrence Erlbaum Associates.
Maturana, H. R. & Varela, F. J. (1973) Autopoiesis and cognition: The realization of the living. D. Reidel.
McAlpine, D., Jiang, D. & Palmer, A. R. (2001) A neural code for low-frequency sound localization in mammals. Nature Neuroscience 4:396401.
Merker, B. (2013a) Cortical gamma oscillations: The functional key is activation, not cognition. Neuroscience & Biobehavioral Reviews 37(3):401–17.
Moser, E. I., Kropff, E. & Moser, M. B. (2008) Place cells, grid cells, and the brain's spatial representation system. Annual Review of Neuroscience 31(1):6989.
Muckli, L., Naumer, M. J. & Singer, W. (2009) Bilateral visual field maps in a patient with only one hemisphere. Proceedings of the National Academy of Sciences USA 106(31):13034–39.
Naselaris, T., Prenger, R. J., Kay, K. N., Oliver, M. & Gallant, J. L. (2009) Bayesian reconstruction of natural images from human brain activity. Neuron 63(6):902–15.
Noble, D. (2008) The music of life: Biology beyond genes. Oxford University Press.
Olshausen, B. A. & Field, D. J. (2004) Sparse coding of sensory inputs. Current Opinion in Neurobiology 14(3):481–87.
O'Regan, J. K. & Noë, A. (2001) A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24(5):939–73.
Pakan, J. M., Francioni, V. & Rochefort, N. L. (2018) Action and learning shape the activity of neuronal circuits in the visual cortex. Current Opinion in Neurobiology 52:8897.
Palmer, S. E., Marre, O., Berry, M. J. & Bialek, W. (2015) Predictive information in a sensory population. Proceedings of the National Academy of Sciences USA 112:6908–13.
Perkel, D. & Bullock, T. (1968) Neural coding: A report based on an NRP work session, Neuroscience Research Program Bulletin 6. MIT Press.
Pezzulo, G. & Cisek, P. (2016) Navigating the affordance landscape: Feedback control as a process model of behavior and cognition. Trends in Cognitive Sciences 20(6):414–24.
Pouget, A., Dayan, P. & Zemel, R. S. (2003) Inference and computation with population codes. Annual Review of Neuroscience 26:381410.
Powers, W. T. (1973a) Behavior: The control of perception. Aldine.
Quian Quiroga, R. & Panzeri, S. (2009) Extracting information from neuronal populations: Information theory and decoding approaches. Nature Reviews Neuroscience 10:173–85.
Quian Quiroga, R., Reddy, L., Kreiman, G., Koch, C. & Fried, I. (2005) Invariant visual representation by single neurons in the human brain. Nature 435:1102–7.
Rahnev, D. & Denison, R. N. (2018) Suboptimality in perceptual decision making. Behavioral and Brain Sciences 41:E223.
Rao, R. P. & Ballard, D. H. (1999) Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2:7987.
Ricci, M., Kim, J. & Serre, T. (2018) Same-different problems strain convolutional neural networks. Preprint. arXiv1802.03390 Cs Q-Bio. Available at: [Accessed May 28, 2018.]
Rieke, F., Warland, D., van Stevenick R., de Ruyter & Bialek, W. (1997) Spikes: Exploring the neural code. MIT Press.
Rosen, R. (1985) Anticipatory systems: Philosophical, mathematical and methodological foundations. Pergamon Press.
Schnapf, J. L., Kraft, T. W. & Baylor, D. A. (1987) Spectral sensitivity of human cone photoreceptors. Nature 325:439–41.
Seriès, P., Latham, P. E. & Pouget, A. (2004) Tuning curve sharpening for orientation selectivity: Coding efficiency and the impact of correlations. Nature Neuroscience 7:1129–35.
Shackleton, T. M., Skottun, B. C., Arnott, R. H. & Palmer, A. R. (2003) Interaural time difference discrimination thresholds for single neurons in the inferior colliculus of guinea pigs. Journal of Neuroscience 23(2):716–24.
Shannon, C. E. (1948) A mathematical theory of communication. Bell Systems Technical Journal 27(3):379423, 623–56. Available at:
Simoncelli, E. P. (2003) Vision and the statistics of the visual environment. Current Opinion in Neurobiology 13:144–9.
Singer, W. (1999) Neuronal synchrony: A versatile code for the definition of relations? Neuron 24:4965.
Skottun, B. C. (1998) Sound localization and neurons. Nature 393(6685):531.
Somjen, G. (1972) Sensory coding in the mammalian nervous system. Springer. Available at: [Accessed March 26, 2018.]
Syka, J. & Straschill, M. (1970) Activation of superior colliculus neurons and motor responses after electrical stimulation of the inferior colliculus. Experimental Neurology 28:384–92.
Teller, D. Y. (1984) Linking propositions. Vision Research 24:1233–46.
Thompson, F. B. (1968) The organization is the information. American Documentation 19:305–08.
Thompson, S. K., von Kriegstein, K., Deane-Pratt, A., Marquardt, T., Deichmann, R., Griffiths, T. D. & McAlpine, D. (2006) Representation of interaural time delay in the human auditory midbrain. Nature Neuroscience 9:1096–98.
Tonegawa, S., Liu, X., Ramirez, S. & Redondo, R. (2015) Memory engram cells have come of age. Neuron 87:918–31.
Uttal, W. R. (1973) The psychobiology of sensory coding. Psychology Press.
van Gelder, T. (1995) What might cognition be, if not computation? Journal of Philosophy 92(7):345–81.
van Gelder, T. (1998) The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences 21(5):615–28.
von der Malsburg, C. (1999) The what and why of binding: The modeler's perspective. Neuron 24:95104.
von Uexküll, J. (1909) Umwelt und Innenwelt der Tiere. Springer. Available at: [Accessed December 17, 2018.]
Yin, T. C. & Chan, J. C. (1990) Interaural time sensitivity in medial superior olive of cat. Journal of Neurophysiology 64:465–88.
Zylberberg, J. (2018) The role of untuned neurons in sensory information coding. Preprint. bioRxiv:134379. Available at:


Is coding a relevant metaphor for the brain?

  • Romain Brette (a1)


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