Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-06-12T14:44:49.974Z Has data issue: false hasContentIssue false

Is coding a relevant metaphor for the brain?

Published online by Cambridge University Press:  16 July 2018

Romain Brette*
Affiliation:
Institut de la Vision, Université Pierre-and-Marie-Curie 06, Sorbonne Universités, INSERM, CNRS, 75012Paris, Franceromain.brette@inserm.frhttp://romainbrette.fr

Abstract

“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.

Type
Target Article
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahissar, E. & Assa, E. (2016) Perception as a closed-loop convergence process. eLife 5:12830. doi: 10.7554/eLife.12830.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Ashida, G. & Carr, C. E. (2011) Sound localization: Jeffress and beyond. Current Opinion in Neurobiology 21(5):745–51.CrossRefGoogle ScholarPubMed
Barlow, H. (1961) Possible principles underlying the transformations of sensory messages. In: Sensory communication, ed. Rosenblith, W., pp. 217–34. MIT Press.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Bialek, W., Nemenman, I. & Tishby, N. (2001) Predictability, complexity, and learning. Neural Computation 13(11):2409–63.CrossRefGoogle Scholar
Bickhard, M. H. (2009) The interactivist model. Synthese 166(3):547–91. Available at: https://doi.org/10.1007/s11229-008-9375-x.CrossRefGoogle Scholar
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: https://doi.org/10.1037/a0038059.CrossRefGoogle Scholar
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.Google Scholar
Bolz, J. & Gilbert, C. D. (1986) Generation of end-inhibition in the visual cortex via interlaminar connections. Nature 320:362–65.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Brette, R. (2010) On the interpretation of sensitivity analyses of neural responses. Journal of the Acoustical Society of America 128(5):2965–72.CrossRefGoogle ScholarPubMed
Brette, R. (2012) Computing with neural synchrony. PLoS Computational Biology 8(6):e1002561.CrossRefGoogle ScholarPubMed
Brette, R. (2015) Philosophy of the spike: Rate-based vs. spike-based theories of the brain. Frontiers in Systems Neuroscience 9:151.CrossRefGoogle Scholar
Brette, R. (2016) Subjective physics. In: Closed loop neuroscience, ed. El Hady, A., pp. 146–70. Academic Press.Google Scholar
Brooks, R. A. (1991a) Intelligence without representation. Artificial Intelligence 47(1–3):139–59. doi:10.1016/0004-3702(91)90053-M.CrossRefGoogle Scholar
Buzsáki, G. (2010) Neural syntax: Cell assemblies, synapsembles, and readers. Neuron 68:362–85.CrossRefGoogle ScholarPubMed
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.Google Scholar
Chomsky, N. (1959) A review of B. F. Skinner's Verbal Behavior. Language 35:2658.CrossRefGoogle Scholar
Cisek, P. (1999) Beyond the computer metaphor: Behaviour as interaction. Journal of Consciousness Studies 6(11/12):125–42.Google Scholar
Clark, A. (2013) Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Science 36:181204.CrossRefGoogle ScholarPubMed
Constantinidis, C. & Klingberg, T. (2016) The neuroscience of working memory capacity and training. Nature Reviews Neuroscience 17:438–49.CrossRefGoogle ScholarPubMed
Crick, F. (1979) Thinking about the brain. Scientific American 241:219–32.CrossRefGoogle Scholar
deCharms, R. C. & Zador, A. (2000) Neural representation and the cortical code. Annual Review of Neuroscience 23:613–47.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Dennett, D. C. (1978) Why not the whole iguana? Behavioral and Brain Sciences 1:103–04.CrossRefGoogle Scholar
Dewey, J. (1896) The reflex arc concept in psychology. Psychological Review 3(4), 357–70.CrossRefGoogle Scholar
Eccles, J. C. (1965) Conscious experience and memory. In: Brain and conscious experience, pp. 314–44. Springer. Available at: https://link.springer.com/chapter/10.1007/978-3-642-49168-9_14 [Accessed May 22, 2018].CrossRefGoogle Scholar
Eckert, R. (1972) Bioelectric control of ciliary activity. Science 176:473–81.CrossRefGoogle ScholarPubMed
Eckert, R. & Naitoh, Y. (1970) Passive electrical properties of Paramecium and problems of ciliary coordination. Journal of General Physiology 55:467–83.CrossRefGoogle ScholarPubMed
Friston, K. (2009) The free-energy principle: A rough guide to the brain? Trends in Cognitive Sciences 13:293301.CrossRefGoogle Scholar
Friston, K. (2010) The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11:127–38.CrossRefGoogle ScholarPubMed
Gibson, J. J. (1979) The ecological approach to visual perception. Routledge.Google Scholar
Gilbert, C. D. & Li, W. (2013) Top-down influences on visual processing. Nature Reviews Neuroscience 14:350–63.CrossRefGoogle ScholarPubMed
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: https://link.springer.com/chapter/10.1007/978-3-319-57363-2_11. [Accessed June 27, 2018.]CrossRefGoogle Scholar
Gomez-Marin, A. & Mainen, Z. F. (2016) Expanding perspectives on cognition in humans, animals, and machines. Current Opinion in Neurobiology 37:8591.CrossRefGoogle ScholarPubMed
Goodman, D. F., Benichoux, V. & Brette, R. (2013) Decoding neural responses to temporal cues for sound localization. eLife 2(2):e01312.CrossRefGoogle ScholarPubMed
Goodman, D. F. M. & Brette, R. (2010) Spike-timing-based computation in sound localization. PLoS Computational Biology 6(11):e1000993.CrossRefGoogle ScholarPubMed
Grothe, B., Pecka, M. & McAlpine, D. (2010) Mechanisms of sound localization in mammals. Physiological Reviews 90(3):9831012.CrossRefGoogle ScholarPubMed
Harnad, S. (1990b) The symbol grounding problem. Physica D: Nonlinear Phenomena 42(1–3):335–46.CrossRefGoogle Scholar
Harper, N. S. & McAlpine, D. (2004) Optimal neural population coding of an auditory spatial cue. Nature 430:682–86.CrossRefGoogle ScholarPubMed
Hosoya, T., Baccus, S. A. & Meister, M. (2005) Dynamic predictive coding by the retina. Nature 436:7177.CrossRefGoogle ScholarPubMed
Hubel, D. H. & Wiesel, T. N. (1968) Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology 195:215–43.CrossRefGoogle ScholarPubMed
Hurley, S. (2001) Perception and action: Alternative views. Synthese 129(1):340.CrossRefGoogle Scholar
Jazayeri, M. & Movshon, J. A. (2006) Optimal representation of sensory information by neural populations. Nature Neuroscience 9(5):690–96.CrossRefGoogle ScholarPubMed
Jeffress, L. A. (1948) A place theory of sound localisation. Journal of Comparative and Physiological Psychology 41(1):3539.CrossRefGoogle Scholar
Jenkins, W. M. & Masterton, R. B. (1982) Sound localization: Effects of unilateral lesions in central auditory system. Journal of Neurophysiology 47:9871016.CrossRefGoogle ScholarPubMed
Jennings, H. S. (1906) Behavior of the lower organisms. Columbia University Press/Macmillan. Available at: http://archive.org/details/behavioroflowero00jenn. [Accessed December 20, 2015.]Google Scholar
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.CrossRefGoogle ScholarPubMed
Kawato, M. (1997) Bidirectional theory approach to consciousness. In: Cognition, computation, and consciousness, ed. Ito, M., Miyashita, Y. & Rolls, E. T.. Oxford University Press.Google Scholar
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: http://www.ncbi.nlm.nih.gov/pubmed/15541511 [Accessed July 10, 2014].CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Lakoff, G. & Johnson, M. (1980a) Metaphors we live by. University of Chicago Press.Google Scholar
Laudanski, J., Zheng, Y. & Brette, R. (2014) A structural theory of pitch. eNeuro 1(1): 0033-14.2014. doi: https://doi.org/10.1523/ENEURO.0033-14.2014.CrossRefGoogle Scholar
Le Mouel, C. & Brette, R. (2017) Mobility as the purpose of postural control. Frontiers in Computational Neuroscience 11:Article 67. Available at: https://www.frontiersin.org/articles/10.3389/fncom.2017.00067/full. [Accessed June 21, 2018.]Google Scholar
Macmillan, N. A. & Creelman, C. D. (2005) Detection theory: A user's guide (2nd edition). Lawrence Erlbaum Associates.Google Scholar
Maturana, H. R. & Varela, F. J. (1973) Autopoiesis and cognition: The realization of the living. D. Reidel.Google Scholar
McAlpine, D., Jiang, D. & Palmer, A. R. (2001) A neural code for low-frequency sound localization in mammals. Nature Neuroscience 4:396401.CrossRefGoogle ScholarPubMed
Merker, B. (2013a) Cortical gamma oscillations: The functional key is activation, not cognition. Neuroscience & Biobehavioral Reviews 37(3):401–17.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Noble, D. (2008) The music of life: Biology beyond genes. Oxford University Press.Google Scholar
Olshausen, B. A. & Field, D. J. (2004) Sparse coding of sensory inputs. Current Opinion in Neurobiology 14(3):481–87.CrossRefGoogle ScholarPubMed
O'Regan, J. K. & Noë, A. (2001) A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24(5):939–73.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Perkel, D. & Bullock, T. (1968) Neural coding: A report based on an NRP work session, Neuroscience Research Program Bulletin 6. MIT Press.Google Scholar
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.CrossRefGoogle ScholarPubMed
Pouget, A., Dayan, P. & Zemel, R. S. (2003) Inference and computation with population codes. Annual Review of Neuroscience 26:381410.CrossRefGoogle ScholarPubMed
Powers, W. T. (1973a) Behavior: The control of perception. Aldine.Google Scholar
Quian Quiroga, R. & Panzeri, S. (2009) Extracting information from neuronal populations: Information theory and decoding approaches. Nature Reviews Neuroscience 10:173–85.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Rahnev, D. & Denison, R. N. (2018) Suboptimality in perceptual decision making. Behavioral and Brain Sciences 41:E223.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Ricci, M., Kim, J. & Serre, T. (2018) Same-different problems strain convolutional neural networks. Preprint. arXiv1802.03390 Cs Q-Bio. Available at: http://arxiv.org/abs/1802.03390. [Accessed May 28, 2018.]Google Scholar
Rieke, F., Warland, D., van Stevenick R., de Ruyter & Bialek, W. (1997) Spikes: Exploring the neural code. MIT Press.Google Scholar
Rosen, R. (1985) Anticipatory systems: Philosophical, mathematical and methodological foundations. Pergamon Press.Google Scholar
Schnapf, J. L., Kraft, T. W. & Baylor, D. A. (1987) Spectral sensitivity of human cone photoreceptors. Nature 325:439–41.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Shannon, C. E. (1948) A mathematical theory of communication. Bell Systems Technical Journal 27(3):379423, 623–56. Available at: https://archive.org/details/bellsystemtechni27amerrich/page/379.CrossRefGoogle Scholar
Simoncelli, E. P. (2003) Vision and the statistics of the visual environment. Current Opinion in Neurobiology 13:144–9.CrossRefGoogle ScholarPubMed
Singer, W. (1999) Neuronal synchrony: A versatile code for the definition of relations? Neuron 24:4965.CrossRefGoogle ScholarPubMed
Skottun, B. C. (1998) Sound localization and neurons. Nature 393(6685):531.CrossRefGoogle ScholarPubMed
Somjen, G. (1972) Sensory coding in the mammalian nervous system. Springer. Available at: www.springer.com/us/book/9781468417074. [Accessed March 26, 2018.]CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Teller, D. Y. (1984) Linking propositions. Vision Research 24:1233–46.CrossRefGoogle ScholarPubMed
Thompson, F. B. (1968) The organization is the information. American Documentation 19:305–08.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Tonegawa, S., Liu, X., Ramirez, S. & Redondo, R. (2015) Memory engram cells have come of age. Neuron 87:918–31.CrossRefGoogle ScholarPubMed
Uttal, W. R. (1973) The psychobiology of sensory coding. Psychology Press.Google Scholar
van Gelder, T. (1995) What might cognition be, if not computation? Journal of Philosophy 92(7):345–81.CrossRefGoogle Scholar
van Gelder, T. (1998) The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences 21(5):615–28.CrossRefGoogle ScholarPubMed
von der Malsburg, C. (1999) The what and why of binding: The modeler's perspective. Neuron 24:95104.CrossRefGoogle ScholarPubMed
von Uexküll, J. (1909) Umwelt und Innenwelt der Tiere. Springer. Available at: http://archive.org/details/umweltundinnenwe00uexk. [Accessed December 17, 2018.]Google Scholar
Yin, T. C. & Chan, J. C. (1990) Interaural time sensitivity in medial superior olive of cat. Journal of Neurophysiology 64:465–88.CrossRefGoogle ScholarPubMed
Zylberberg, J. (2018) The role of untuned neurons in sensory information coding. Preprint. bioRxiv:134379. Available at: https://doi.org/10.1101/134379.CrossRefGoogle Scholar