Abstract
Various theories have been put forward to provide theoretical unification in neuroscience. The “data rich and theory poor” state of neuroscience makes such theories worth pursuing. An overarching theory can facilitate data interpretation and provide a general framework for explanation and understanding across the various subfields of neuroscience. Neural reuse is a recent and increasingly popular attempt at such a unifying theory. At its core, neural reuse is a claim about the brain’s architecture that centers on the idea that brain regions are used for multiple tasks across multiple domains. Here, I claim that although neural reuse has many merits, it does not provide a fundamental theory of brain structure and function. Neural reuse is appropriately understood as a general organizational principle that is encompassed by a more fundamental theory. That theory is Neural Darwinism, which applies broadly Darwinian selectionist principles across scales of investigation to explain and understand brain structure and function.
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References
Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M.-E., et al. (2013). BigBrain: An ultrahigh-resolution 3D human brain model. Science, 340, 1472–1475.
Anderson, M. L. (2007). Massive redeployment, exaptation, and the functional integration of cognitive operations. Synthese, 159(3), 329–345.
Anderson, M. L. (2008). Circuit sharing and the implementation of intelligent systems. Connection Science, 20, 239–251.
Anderson, M. L. (2010). Neural reuse: A fundamental organizational principle of the brain. Behavioral and Brain Sciences, 33, 245–313.
Anderson, M. L. (2014). After phrenology: Neural reuse and the interactive brain. Cambridge, MA: MIT Press.
Anderson, M. L. (2015). Mining the brain for a new taxonomy of the mind. Philosophy Compass, 10, 68–77.
Anderson, M. L. (2016). Précis of after phrenology: Neural reuse and the interactive brain. Behavioral and Brain Sciences, 39, 1–45. https://doi.org/10.1017/S0140525X15000631.
Anderson, M. L., & Pessoa, L. (2011). Quantifying the diversity of neural activations in individual brain regions. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd annual conference of the cognitive science society (pp. 2421–2426). Austin, TX: Cognitive Science Society.
Anderson, M. L., Kinnison, J., & Pessoa, L. (2013). Describing functional diversity of brain regions and brain networks. NeuroImage, 73, 50–58.
Ascoli, G. A. (2002). Computing the brain and the computing brain. In G. A. Ascoli (Ed.), Computational neuroanatomy: Principles and methods (pp. 3–23). Totowa, NJ: Humana Press.
Bedny, M., Pascual-Leone, A., Dodell-Feder, D., Fedorenko, E., & Saxe, R. (2011). Language processing in the occipital cortex of congenitally blind adults. Proceedings of the National Academy of Sciences, 108(11), 4429–4434.
Berlucchi, G., & Buchtel, H. A. (2009). Neuronal plasticity: Historical roots and evolution meaning. Experimental Brain Research, 192, 307–319.
Bordens, K. S., & Abbott, B. B. (2014). Research design and methods: A process approach (9th ed.). New York: McGraw-Hill Education.
Bressler, S. L., & Kelso, J. A. S. (2016). Coordination dynamics cognitive neuroscience. Frontiers in Neuroscience, 10(397), 1–7. https://doi.org/10.3389/fnins.2016.00397.
Brigandt, I. (2010). Beyond reduction and pluralism: Toward an epistemology of explanatory integration in biology. Erkenntnis, 73(3), 295–311.
Carruthers, P. (2006). The architecture of the mind: Massive modularity and the flexibility of thought. Oxford: Oxford University Press.
Cat, J. (2017). The unity of science. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (fall 2017 edition). Retrieved May 15, 2019 from https://plato.stanford.edu/archives/fall2017/entries/scientific-unity/
Chervyakov, A. V., Sinitsyn, D. O., & Piradov, M. A. (2016). Variability of neuronal responses: Types and functional significance in neuroplasticity and neural Darwinism. Frontiers in Human Neuroscience, 10, 603. https://doi.org/10.3389/fnhum.2016.00603.
Churchland, P. S. (1994). Can neurobiology teach us anything about consciousness? Proceedings and Addresses of the American Philosophical Association, 67, 23–40.
Churchland, P. S., & Sejnowski, T. J. (2016). Blending computational and experimental neuroscience. Nature Reviews: Neuroscience, 17, 667–668.
Cosmides, L., & Tooby. (1987). From evolution to behavior: Evolutionary psychology as the missing link. In J. Dupre (Ed.), The latest on the best: Essays on evolution and optimality (pp. 277–306). Cambridge, MA: MIT Press.
D’Souza, D., & Karmiloff-Smith, A. (2016). Why a developmental perspective is critical for understanding human cognition. Behavioral and Brain Sciences, 39, 11–13.
Dehaene, S. (2005). Evolution of human cortical circuits for reading and arithmetic: The “neuronal recycling” hypothesis. In S. Dehaene, J.-R. Duhamel, M. D. Hauser, & G. Rizzolatti (Eds.), From monkey brain to human brain: A Fyssen Foundation symposium (pp. 133–157). Cambridge, MA: MIT Press.
Doya, K., Ishii, S., Pouget, A., & Rao, R. P. (Eds.). (2007). Bayesian brain: Probabilistic approaches to neural coding. Cambridge, MA: MIT press.
Edelman, G. M. (1987). Neural Darwinism: The theory of neuronal group selection. New York: Basic Books.
Edelman, G. M. (1988). Topobiology: An introduction to molecular embryology. New York: Basic Books.
Edelman, G. M. (1989). The remembered present: A biological theory of consciousness. New York: Basic Books.
Edelman, G. M. (2003). Naturalizing consciousness: A theoretical framework. Proceedings of the National Academy of Sciences, 100, 5520–5524.
Edelman, G. M. (2006). The embodiment of mind. Daedalus, Summer, 23–32.
Edelman, G. M., & Gally, J. A. (1967). Somatic recombination of duplicated genes: An hypothesis on the origin of antibody diversity. Proceedings of the National Academy of Sciences, 57, 353–358.
Edelman, G. M., & Gally, J. A. (2001). Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences, 98, 13763–13768. https://doi.org/10.1073/pnas.231499798.
Edelman, G. M., & Gally, J. A. (2013). Reentry: A key mechanism for integration of brain function. Frontiers in Integrative Neuroscience, 7, 63. https://doi.org/10.3389/fnint.2013.00063.
Edelman, G. M., & Tononi, G. (2000). A universe of consciousness. New York: Basic Books.
Edelman, G. M., Gally, J. A., & Baars, B. J. (2011). Biology of consciousness. Frontiers in Psychology, 2(4), 1–7. https://doi.org/10.3389/fpsyg.2011.00004.
Érdi, P. (2008). Complexity explained. Berlin: Springer.
Favela, L. H. (2009). Biological theories of consciousness: The search for experience. (Thesis). San Diego State University.
Favela, L. H. (2014). Radical embodied cognitive neuroscience: Addressing “grand challenges” of the mind sciences. Frontiers in Human Neuroscience, 8, 796. https://doi.org/10.3389/fnhum.2014.00796.
Fernando, C., Karishma, K. K., & Szathmáry, E. (2008). Copying and evolution of neuronal topology. PLoS One, 3(11), e3775.
Fodor, J. A. (1998). Concepts: Where cognitive science went wrong. New York: Oxford University Press.
Freeman, W. J. (1975). Mass action in the nervous system. New York: Academic.
Freeman, W. J. (2005). A field-theoretic approach to understanding scale-free neocortical dynamics. Biological Cybernetics, 92(6), 350–359.
Frégnac, Y. (2017). Big data and the industrialization of neuroscience: A safe roadmap for understanding the brain? Science, 358(6362), 470–477.
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.
Fuchs, E., & Flugge, G. (2014). Adult neuroplasticity: More than 40 years of research. Neural Plasticity, 2014, 541870, 1–10.
Gallese, V. (2008). Mirror neurons and the social nature of language: The neural exploitation hypothesis. Social Neuroscience, 3(3–4), 317–333.
Gaser, C., & Schlaug, G. (2003). Brain structures differ between musicians and non-musicians. Journal of Neuroscience, 23, 9240–9245.
Goldinger, S. D., Papesh, M. H., Barnhart, A. S., Hansen, W. A., & Hout, M. C. (2016). The poverty of embodied cognition. Psychonomic Bulletin & Review, 23(4), 959–978.
Grumet, M., & Edelman, G. M. (1988). Neuron-glia cell adhesion molecule interacts with neurons and astroglia via different binding mechanism. The Journal of Cell Biology, 106, 487–503.
Hawkins, J., Lewis, M., Klukas, M., Purdy, S., & Ahmad, S. (2019). A framework for intelligence and cortical function based on grid cells in the neocortex. Frontiers in Neural Circuits, 12, 121. https://doi.org/10.3389/fncir.2018.00121.
He, B., Coleman, T., Genin, G. M., Glover, G., Hu, X., Johnson, N., et al. (2013). Grand challenges in mapping the human brain: NSF workshop report. IEEE Transactions on Biomedical Engineering, 60(11), 2983–2992.
Hurley, S. L. (2008). The shared circuits model (SCM): How control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences, 31(1), 1–22.
Izhikevich, E. M. (2006). Polychronization: Computation with spikes. Neural Computation, 18(2), 245–282.
Izhikevich, E. M. (2007). Dynamical systems in neuroscience: The geometry of excitability and bursting. Cambridge, MA: MIT Press.
Izhikevich, E. M., & Edelman, G. M. (2008). Large-scale model of mammalian thalamocortical systems. Proceedings of the National Academy of Sciences, 105(9), 3593–3598.
Klein, C. (2010). Redeployed functions versus spreading activation: A potential confound. Behavioral and Brain Sciences, 33(4), 280–281.
Krichmar, J. L., & Edelman, G. M. (2002). Machine psychology: Autonomous behavior, perceptual categorization and conditioning in a brain-based device. Cerebral Cortex, 12(8), 818–830.
Krichmar, J. L., & Edelman, G. M. (2005). Brain-based devices for the study of nervous systems and the development of intelligent machines. Artificial Life, 11(1–2), 63–77.
Landhuis, E. (2017). Neuroscience: Big brain, big data. Nature, 541, 559–561.
Leversen, J. S., Haga, M., & Sigmundsson, H. (2012). From children to adults: Motor performance across the life-span. PLoS One, 7(6), e38830.
Mayr, O. (1970). The origins of feedback control. Cambridge, MA: MIT Press.
McCaffrey, J. B., & Machery, E. (2016). The reification objection to bottom-up cognitive ontology revision. Behavioral and Brain Sciences, 39, 16–18. https://doi.org/10.1017/S0140525X15001594.
McIntosh, A. R. (2000). Towards a network theory of cognition. Neural Networks, 13, 861–870.
Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.
Nishitani, N., Schürmann, M., Amunts, K., & Hari, R. (2005). Broca’s region: From action to language. Physiology, 20, 60–69.
Poldrack, R. A., & Yarkoni, T. (2016). From brain maps to cognitive ontologies: Informatics and the search for mental structure. Annual Review of Psychology, 67, 587–612.
Pulvermüller, F. (2018). Neural reuse of action perception circuits for language, concepts and communication. Progress in Neurobiology, 160, 1–44.
Reid, D., Hussain, A. J., & Tawfik, H. (2014). Financial time series prediction using spiking neural networks. PLoS One, 9(8), e103656.
Sampaio, E., Maris, S., & Bach-y-Rita, P. (2001). Brain plasticity: ‘Visual’ acuity of blind persons via the tongue. Brain Research, 908(2), 204–207.
Scorzato, L. (2013). On the role of simplicity in science. Synthese, 190(14), 2867–2895.
Sejnowski, T. J., Churchland, P. S., & Movshon, J. A. (2014). Putting big data to good use in neuroscience. Nature Neuroscience, 17(11), 1440.
Seth, A. K., & Baars, B. J. (2005). Neural Darwinism and consciousness. Consciousness and Cognition, 14(1), 140–168.
Seth, A. K., & Edelman, G. M. (2007). Distinguishing causal interactions in neural populations. Neural Computation, 19(4), 910–933.
Sperber, D. (1994). The modularity of thought and the epidemiology of representations. In L. Hirschfeld & S. Gelman (Eds.), Mapping the mind (pp. 39–67). Cambridge, MA: Cambridge University Press.
Sporns, O. (2011). Networks of the brain. Cambridge, MA: MIT Press.
Sporns, O. (2012). Discovering the human connectome. Cambridge, MA: MIT Press.
Sporns, O., & Edelman, G. M. (1993). Solving Bernstein’s problem: A proposal for the development of coordinated movement by selection. Child Development, 64(4), 960–981.
Sporns, O., Tononi, G., & Edelman, G. M. (1991). Modeling perceptual grouping and figure-ground segregation by means of active reentrant connections. Proceedings of the National Academy of Sciences, 88(1), 129–133.
Sporns, O., Tononi, G., & Edelman, G. M. (2000). Connectivity and complexity: The relationship between neuroanatomy and brain dynamics. Neural Networks, 13(8–9), 909–922.
Srinivasan, R., Russell, D. P., Edelman, G. M., & Tononi, G. (1999). Increased synchronization of neuromagnetic responses during conscious perception. Journal of Neuroscience, 19(13), 5435–5448.
Tononi, G., & Edelman, G. M. (2000). Schizophrenia and the mechanisms of conscious integration. Brain Research Reviews, 31(2–3), 391–400.
Tononi, G., Sporns, O., & Edelman, G. M. (1992). Reentry and the problem of integrating multiple cortical areas: Simulation of dynamic integration in the visual system. Cerebral Cortex, 2(4), 310–335.
Tononi, G., Sporns, O., & Edelman, G. M. (1996). A complexity measure for selective matching of signals by the brain. Proceedings of the National Academy of Sciences, 93(8), 3422–3427.
Tononi, G., Edelman, G. M., & Sporns, O. (1998). Complexity and coherency: Integrating information in the brain. Trends in Cognitive Sciences, 2(12), 474–484.
Tooby, J., & Cosmides, L. (1992). The psychological foundations of culture. In J. Barkow, L. Cosmides, & L. Tooby (Eds.), The adapted mind: Evolutionary psychology and the generation of culture (pp. 19–136). New York: Oxford University Press.
U.S. National Academy of Sciences. (2018). Definitions of evolutionary terms. The National Academies of Sciences, Engineering, Medicine: Evolution resources. Washington, DC. Retrieved July 19, 2018 from http://nationalacademies.org/evolution/Definitions.html
Uttal, W. R. (2005). Neural theories of mind: Why the mind-brain problem may never be solved. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Uttal, W. R. (2016). Macroneural theories in cognitive neuroscience. New York: Psychology Press.
von Bernhardi, R., Eugenín-von Bernhardi, L., & Eugenín, J. (2017). What is neural plasticity? In R. von Bernhardi, L. Eugenín-von Bernhardi, & J. Eugenín (Eds.), The plastic brain (pp. 1–15). Cham: Springer.
Woodward, J. F. (2011). Data and phenomena: A restatement and defense. Synthese, 182(1), 165–179.
Zheng, Z., Lauritzen, J. S., Perlman, E., Robinson, C. G., Nichols, M., Milkie, D., et al. (2018). A complete electron microscopy volume of the brain of adult Drosophila melanogaster. Cell, 174(3), 730–743.
Ziegler, J. C., Montant, M., Briesemeister, B. B., Brink, T. T., Wicker, B., Ponz, A., et al. (2018). Do words stink? Neural reuse as a principle for understanding emotions in reading. Journal of Cognitive Neuroscience, 30(7), 1023–1032.
Zimmerman, A. W. (2008). Preface. In A. W. Zimmerman (Ed.), Autism: Current theories and evidence (pp. v–ix). Totowa, NJ: Humana Press.
Acknowledgements
The author thanks audiences at the Neural Mechanisms Online Webconference 2018 New Challenges in the Philosophy of Neuroscience and the meeting of the Southern Society for Philosophy and Psychology 2019 for helpful comments and questions. The author is very thankful for constructive feedback and suggestions from the editors and reviewers. This work is partially based on material from Favela (2009).
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Favela, L.H. (2021). Fundamental Theories in Neuroscience: Why Neural Darwinism Encompasses Neural Reuse. In: Calzavarini, F., Viola, M. (eds) Neural Mechanisms. Studies in Brain and Mind, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-54092-0_7
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