Skip to main content
SpringerLink
Log in

The Computational Units of the Brain

  • Chapter
  • First Online:
Neurosemantics

Part of the book series: Studies in Brain and Mind ((SIBM,volume 10))

  • 482 Accesses

Abstract

Every mathematical framework is developed around some basic computational item, for example, sets, numbers, and vectors. This is the starting point for a computational view of the brain as well, the basic units have to be specified. While in the ethereal world of mathematics the basic components can be arbitrarily assumed, even invented from scratch, the case of the brain is constrained by its biophysical structure. The current view today is dominated by the paradigm constructed by Ramón y Cajal, where the neuronal cell is the basic computational device of the brain. Enormous progress has been achieved in characterizing the computational properties of the brain under this paradigm, which will partly be reviewed in this chapter, limiting ourselves to those aspects that are useful for understanding the representational power of the brain. However, like any scientific paradigm, the so called “neuron dogma” might possibly change in the future, there are scholars for example (London and Häusser, Annu Rev Neurosci 28:503–5032, 2005) that argue that dendrites display autonomous computational capabilities, and might be a better candidate for the title of basic computational unit.

Despite many attempts, it is still difficult to spell out what the most specific function of the neuron as a computational device is, and how that makes it so different from other man-made computational devices. We favor an idea advanced by Turing (1948, Intelligent machinery. Technical report, National Physical Laboratory, London, reprinted in Ince DC (ed) Collected Works of A. M. Turing: Mechanical Intelligence, Edinburgh University Press, 1969) long ago: neurons have no special built in function, but that of being able to learn virtually any function, by experience. Plasticity is the term in neuroscience that includes the biological mechanisms that explain how neurons work as extraordinary learning machines.

The last section of this chapter deals with a special organization of neurons, that has long been held to deserve a specific computational description (Stevens, What form should a cortical theory take? In: Koch C, Davis J (eds) Large-scale neuronal theories of the brain. MIT, Cambridge, pp 239–255, 1994), and seems to be the privileged site of semantic representations: the cortex.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aboitiz, F. (1999). Comparative development of the mammalian isocortex and the reptilian dorsal ventricular ridge. evolutionary considerations. Cerebral Cortex, 9, 783–791.

    Article  Google Scholar 

  • Artola, A., & Singer, W. (1987). Long term potentiation and NMDA receptors in rat visual cortex. Nature, 330, 649–652.

    Article  Google Scholar 

  • Azmitia, E. C., DeFelipe, J., Jones, E. G., Rakic, P., & Ribak, C. E. (Eds.). (2002). Changing Views of Cajal’s Neuron. Progress in Brain Research (Vol. 136). Amsterdam: Elsevier.

    Google Scholar 

  • Bain, A. (1873). Mind and body. The theories of their relation. London: Henry King.

    Google Scholar 

  • Balus̆ka, F., Volkmann, D., & Menzel, D. (2005). Plant synapses: Actin-based domains for cell-to-cell communication. TRENDS in Plant Science, 10, 106–111.

    Google Scholar 

  • Bear, M., & Kirkwood, A. (1993). Neocortical long term potentiation. Current Opinion in Neurobiology, 3, 197–202.

    Article  Google Scholar 

  • Berlin, R. (1858). Beitrag zur structurlehre der grosshirnwindungen. PhD thesis, Medicinischen Fakultät zu Erlangen

    Google Scholar 

  • Bliss, T., & Collingridge, G. (1993). A synaptic model of memory: Long-term potentiation in the hippocampus. Nature, 361, 31–39.

    Article  Google Scholar 

  • Bliss, T., & Lømo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. Journal of Physiology, 232, 331–356.

    Article  Google Scholar 

  • Bogen, J. (2005). Regularities and causality; Generalizations and causal explanations. Studies in History and Philosophy of Science Part C, 36, 397–420.

    Article  Google Scholar 

  • Braak, H. (1980). Architectonics of the human telencephalic cortex. Berlin: Springer.

    Book  Google Scholar 

  • Brenner, E. D., Stahlberg, R., Mancuso, S., Vivanco, J., Balus̆ka, F., & Volkenburgh, E. V. (2006). Plant neurobiology: An integrated view of plant signaling. TRENDS in Plant Science, 11, 413–419.

    Google Scholar 

  • Brodmann, K. (1909). Vergleichende Lokalisationslehre der Grosshirmrinde. Leipzig: Barth.

    Google Scholar 

  • Bruce, L. L., & Neary, T. J. (1995). The limbic system of tetrapods: A comparative analysis of cortical and amygdalar populations. Behavioral and Brain Science, 46, 224–234.

    Article  Google Scholar 

  • Bullock, T. H., Bennett, M. V. L., Josephson, D. J. R., Marder, E., & Fields, R. D. (2005). The neuron doctrine, redux. Science, 310, 791–793.

    Article  Google Scholar 

  • Bush, P. C., & Douglas, R. J. (1991). Synchronization of bursting action potential discharge in a model network of neocortical neurons. Neural Computation, 3, 19–30.

    Article  Google Scholar 

  • Butler, A. B. (1994). The evolution of the dorsal pallium in the telencephalon of amniotes: Cladistic analysis and a new hypothesis. Brain Research Reviews, 19, 66–101.

    Article  Google Scholar 

  • Cao, R. (2011). A teleosemantic approach to information in the brain. Biology and Philosophy, 27, 49–71.

    Article  Google Scholar 

  • Cao, R. (2014). Signaling in the brain: In search of functional units. Philosophy of Science, 81, 891–901.

    Article  Google Scholar 

  • Carew, T. J., Pinsker, H. M., & Kandel, E. R. (1972). Long-term habituation of a defensive withdrawal reflex in Aplysia. Science, 175, 451–454.

    Article  Google Scholar 

  • Carlo, C. N., & Stevens, C. F. (2013). Structural uniformity of neocortex, revisited. Proceedings of the Natural Academy of Science USA, 110, 719–725.

    Google Scholar 

  • Chenn, A., & Walsh, C. A. (2002). Regulation of cerebral cortical size by control of cell cycle exit in neural precursors. Science, 297, 365–369.

    Article  Google Scholar 

  • Connors, B. W., & Long, M. A. (2004). Electrical synapses in the mammalian brain. Annual Review of Neuroscience, 27, 393–418.

    Article  Google Scholar 

  • Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Crozier, R. A., Wang, Y., Liu, C. H., & Bear, M. F. (2007). Deprivation-induced synaptic depression by distinct mechanisms in different layers of mouse visual cortex. Proceedings of the Natural Academy of Science USA, 104, 1383–1388.

    Google Scholar 

  • Dale, H. H. (1935). Pharmacology and nerve endings. Proceedings of the Royal Society of Medicine, 28, 319–332.

    Google Scholar 

  • Davidson, R. S. (1966). Operant stimulus control applied to maze behavior: Heat escape conditioning and discrimination reversal in alligator mississippiensis. Journal of the Experimental Analysis of Behavior, 9, 671–676.

    Article  Google Scholar 

  • Deiters, O. F. K. (1865). Untersuchungen über Gehirn und Rückenmark des Menschen und der Säugethiere. Braunschweig (DE): Vieweg.

    Book  Google Scholar 

  • Dretske, F. I. (1981). Knowledge and the flow of information. Cambridge: MIT.

    Google Scholar 

  • du Bois-Reymond, E. (1849). Untersuchungen über Thierische Elektricität. Berlin: G. Reimer.

    Google Scholar 

  • Eccles, J. C. (1945). An electrical hypothesis of synaptic and neuromuscular transmission. Nature, 156, 680–682.

    Article  Google Scholar 

  • Edinger, L. (1904). Vorlesungen über den Bau der nervösen Zentralorgane des Menschen und der Tiere. Leipzig (DE): Vogel.

    Google Scholar 

  • Farhat, N. H. (2007). Corticonic models of brain mechanisms underlying cognition and intelligence. Physics of Life Reviews, 4, 223–252.

    Google Scholar 

  • Fields, D., & Stevens-Graham, B. (2002). New insights into neuron-glia communication. Science, 298, 556–562.

    Article  Google Scholar 

  • Freud, S. (1885). Eine neue Methode zum Studium des Faserverlaufs in Centralnervensystem. Centralbl Med Wissensch, 22, 461–512.

    Google Scholar 

  • Freud, S. (1895). Project for a scientific psychology. London: The Hogarth Press.

    Google Scholar 

  • Fuchs, E., & Flügge, G. (2014). Adult neuroplasticity: More than 40 years of research. Neural Plasticity, 2014, ID541870.

    Article  Google Scholar 

  • Fukuda, T. (2009). Network architecture of gap junction-coupled neuronal linkage in the striatum. Journal of Neuroscience, 29, 1235–1243.

    Article  Google Scholar 

  • Fuster, J. M. (2008). The prefrontal cortex (4th ed.). New York: Academic.

    Google Scholar 

  • Garzón, P. C., & Keijzer, F. (2011). Plants: Adaptive behavior, root-brains, and minimal cognition. Adaptive Behavior, 19, 155–171.

    Article  Google Scholar 

  • Gerlach, J. (1871). Von dem Rückenmark. In S. Stricker (Ed.), Handbuch der Lehre von den Geweben des Menschen und der Thiere, W. Engelmann, Leipzig (DE).

    Google Scholar 

  • Gerstner, W. (1999). Spiking neurons. In W. Maass & C. M. Bishop (Eds.). Pulsed neural networks. Cambridge: MIT.

    Google Scholar 

  • Gilbert, C. D., Hirsch, J. A., & Wiesel, T. N. (1990). Lateral interactions in visual cortex. In Cold spring harbor symposia on quantitative biology (pp. 663–677). New York: Cold Spring Harbor Laboratory Press.

    Google Scholar 

  • Goldman, D. (1943). Potential, impedence, and rectification in membranes. The Journal of General Physiology, 27, 37–60.

    Article  Google Scholar 

  • Golowasch, J., Buchholz, F., Epstein, I. R., & Marder, E. (1992). Contribution of individual ionic currents to activity of a model stomatogastric ganglion neuron. Journal of Neurophysiology, 67, 341–349.

    Google Scholar 

  • Hebb, D. O. (1949). The organization of behavior. New York: John Wiley.

    Google Scholar 

  • Hille, B. (1992). Ionic channels of excitable membranes. Sunderland: Sinauer

    Google Scholar 

  • Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of ion currents and its applications to conduction and excitation in nerve membranes. Journal of Physiology, 117, 500–544.

    Article  Google Scholar 

  • Hollmann, M., & Heinemann, S. (1994). Cloned glutamate receptors. Annual Review of Neuroscience, 17, 31–108.

    Article  Google Scholar 

  • Hoorweg, J. L. (1889). Ueber die elektrischen Eigenschaften der Nerven. Pflügers Arch ges Physiol, 71, 128–157.

    Article  Google Scholar 

  • Hou, C., Pettet, M. W., Sampath, V., Candy, T. R., & Norcia, A. M. (2003). Development of the spatial organization and dynamics of lateral interactions in the human visual system. Journal of Neuroscience, 23, 8630–8640.

    Google Scholar 

  • Ito, M. (1989). Long-term depression. Annual Review of Neuroscience, 12, 85–102.

    Article  Google Scholar 

  • Kaplan, D. M., & Craver, C. F. (2011). Towards a mechanistic philosophy of neuroscience. In S. French & J. Saatsi (Eds.), Continuum companion to the philosophy of science (pp. 268–292) London: Continuum Press.

    Google Scholar 

  • Karbowski, J. (2014). Constancy and trade-offs in the neuroanatomical and metabolic design of the cerebral cortex. Frontiers in Neural Circuits, 8, 9.

    Article  Google Scholar 

  • Karten, H. J. (1997). Evolutionary developmental biology meets the brain: The origins of mammalian cortex. Proceedings of the Natural Academy of Science USA, 94, 2800–2804.

    Google Scholar 

  • Katz, B. (1959). Mechanisms of synaptic transmission. Reviews of Modern Physics, 31, 524–536.

    Article  Google Scholar 

  • Katz, B. (1971). Quantal mechanism of neural transmitter release. Science, 173, 123–126.

    Article  Google Scholar 

  • Kelvin, W. T. (1855). On the theory of the electric telegraph. Proceedings of the Royal Society of London, 7, 382–399.

    Google Scholar 

  • Kistler, W. M., Gerstner, W., & van Hemmen, L. (1997). Reduction of the Hodgkin-Huxley equations to single-variable threshold model. Neural Computation, 9, 1015–1045.

    Article  Google Scholar 

  • Kölliker, A. (1893). Über die feinere Anatomie und die physiologische Bedeutung des sympathischen Nervensystems. Wiener k1in Wochenschr, 7, 773–776.

    Google Scholar 

  • Koshland, D. E. (1992). The molecule of the year. Science, s58, 1861.

    Google Scholar 

  • Kuhn, T. (1962). The structure of scientific revolutions (2nd ed., 1970). Chicago: Chicago University Press.

    Google Scholar 

  • Kupfermann, I., Castellucci, V., Pinsker, H. M., & Kandel, E. R. (1972). Neuronal correlates of habituation and dishabituation of the gill-withdrawal reflex in Aplysia. Science, 167, 1743–1745.

    Article  Google Scholar 

  • Liebeskind, B. J., Hillisa, D. M., & Zakona, H. H. (2011). Evolution of sodium channels predates the origin of nervous systems in animals. Proceedings of the Natural Academy of Science USA, 108, 9154–9159.

    Google Scholar 

  • London, M., & Häusser, M. (2005). Dendritic computation. Annual Review of Neuroscience, 28, 503–5032.

    Article  Google Scholar 

  • Maienschein, J. (2007). To Evo-Devo through cells, embryos, and morphogenesis. In M. D. Laubichler & J. Maienschein (Eds.), From embryology to Evo-Devo (pp. 109–121). Cambridge: MIT.

    Google Scholar 

  • Markram, H., Gerstner, W., & Sjöström, P. J. (2011). A history of spike-timing-dependent plasticity. Frontiers in Synaptic Neuroscience, 3, 4.

    Article  Google Scholar 

  • McCulloch, W., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.

    Article  Google Scholar 

  • Medina, L., & Abellán, A. (2009). Development and evolution of the pallium. Seminars in Cell & Developmental Biology, 20, 698–711.

    Article  Google Scholar 

  • Miller, E. K., Freedman, D. J., & Wallis, J. D. (2002). The prefrontal cortex: Categories, concepts and cognition. Philosophical Transactions: Biological Sciences, 357, 1123–1136.

    Article  Google Scholar 

  • Møller, A. R. (Ed.). (2006). Neural plasticity and disorders of the nervous system. Cambridge: Cambridge University Press.

    Google Scholar 

  • Moroz, L. L. (2009). On the independent origins of complex brains and neurons. Brain, Behavior and Evolution, 74, 177–190.

    Article  Google Scholar 

  • Nakazawa, M., Cebria, F., andKazuho Ikeo, K. M., Agata, K., & Gojobori, T. (2003). Search for the evolutionary origin of a brain: Planarian brain characterized by microarray. Molecular Biology and Evolution, 20, 784–791.

    Google Scholar 

  • Neher, E., & Sakmann, B. (1976). Noise analysis of drug induced voltage clamp currents in denervated frog muscle fibers. Journal of Physiology, 258, 705–729.

    Article  Google Scholar 

  • Nieder, A. (2009). Prefrontal cortex and the evolution of symbolic reference. Current Opinion in Neurobiology, 19, 99–108.

    Article  Google Scholar 

  • Nieuwenhuys, R. (1994). The neocortex. Anatomy and Embryology, 190, 307–337.

    Article  Google Scholar 

  • Noack, R. A. (2012). Solving the “human problem”: The frontal feedback model. Consciousness and Cognition, 21, 1043–1067.

    Article  Google Scholar 

  • Nordau, M. (1895). Paradoxes psychologiques. Paris: Alcan.

    Google Scholar 

  • Pearce, J. M. (2008). Animal learning and cognition: An introduction. East Sussex: Psychology Press.

    Google Scholar 

  • Pepperberg, I. M. (1999). The alex studies: Cognitive and communicative abilities of grey parrots. Cambridge: Harvard University Press.

    Google Scholar 

  • Peters, A., Palay, S. L., & deF Webster, H. (1991). Fine structure of the nervous system: Neurons and their supporting cells. Oxford: Oxford University Press.

    Google Scholar 

  • Pinsker, H. M., Hening, W. A., Carew, T. J., & Kandel, E. R. (1973). Long-term sensitization of a defensive withdrawal reflex in Aplysia. Science, 182, 1039–1042.

    Article  Google Scholar 

  • Rakic, P. (2008). Confusing cortical columns. Proceedings of the Natural Academy of Science USA, 34, 12099–12100.

    Google Scholar 

  • Rakic, P. (2009). Evolution of the neocortex: A perspective from developmental biology. Nature Reviews Neuroscience, 10, 724–735.

    Article  Google Scholar 

  • Rall, W. (1967). Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. Journal of Neurophysiology, 30, 1138–1168.

    Google Scholar 

  • Ramón y Cajal, S. (1899). Textura del sistema nervioso del hombre y de los vertebrados (Vol I). Imprenta y Librería de Nicolás Moya, Madrid (English translation by P. Pasik and T. Pasik, 1997). Springer.

    Google Scholar 

  • Ramón y Cajal, S. (1906). In J. DeFelipe & E. G. Jones (Eds.), Cajal on the cerebral cortex: An annotated translation of the complete writings. Oxford: Oxford University Press, 1988.

    Google Scholar 

  • Rinzel, J. (1990). Electrical excitability of cells, theory and experiment: Review of the Hodgkin-Huxley foundation and an update. Bulletin of Mathematical Biology, 52, 5–23.

    Article  Google Scholar 

  • Rockel, A., Hiorns, R., & Powell, T. (1980). The basic uniformity in structure of the neocortex. Brain, 103, 221–244.

    Article  Google Scholar 

  • Romer, A. S. (1967). Major steps in vertebrate evolution. Science, 158, 1629–1637.

    Article  Google Scholar 

  • Ryan, T. J., & Grant, S. G. N. (2009). The origin and evolution of synapses. Nature Reviews Neuroscience, 10, 701–713.

    Article  Google Scholar 

  • Schwann, T. (1839). Mikroskopische Untersuchungen über die Uebereinstimmung in der Struktur und dem Wachsthum der Thiere und Pflanzen. Berlin: Sander.

    Google Scholar 

  • Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Urbana: University of Illinois Press.

    Google Scholar 

  • Sherrington, C. S. (1897). The mammalian spinal cord as an organ of reflex action. Proceedings of the Royal Society of London, 61, 220–221.

    Google Scholar 

  • Sherrington, C. S. (1906). On the proprioceptive system, especially in its reflex aspect. Brain, 29, 467–482.

    Article  Google Scholar 

  • Sherrington, C. S. (1941). The integrative action of the nervous system (2nd ed., 8 Vols). Cambridge: Cambridge University Press.

    Google Scholar 

  • Sidiropoulou, K., Pissadaki, E. K., & Poirazi, P. (2006). Inside the brain of a neuron. EMBO reports, 7, 886–892.

    Article  Google Scholar 

  • Stevens, C. (1994). What form should a cortical theory take? In C. Koch & J. Davis (Eds.), Large-scale neuronal theories of the brain (pp. 239–255). Cambridge: MIT.

    Google Scholar 

  • Striedter, G. F. (2003). Principles of brain evolution. Sunderland: Sinauer Associated.

    Google Scholar 

  • Sulloway, F. J. (1982). Freud: Biologie der Seele: Jenseits der psycoanalytischen Legende. Köln-Lövenich: Hohenheim Verlag.

    Google Scholar 

  • Susswein, A. J., Katzoff, A., Miller, N., & Hurwitz, I. (2004). Nitric oxide and memory. Neuroscientist, 10, 153–162.

    Article  Google Scholar 

  • Traub, R. D., Wong, R. K. S., Miles, R., & Michelson, H. (1991). A model of CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of Neurophysiology, 66, 635–650.

    Google Scholar 

  • Turing, A. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 42, 230–265.

    Google Scholar 

  • Turing, A. (1948). Intelligent machinery. Technical report, National Physical Laboratory, London, reprinted in Ince, D. C. (ed.) Collected Works of A. M. Turing: Mechanical Intelligence, Edinburgh University Press, 1969.

    Google Scholar 

  • Ulinski, P. S. (1990). The cerebral cortex of reptiles. In E. G. Jones & A. Peters (Eds.), Cerebral cortex (Vol. 8, pp. 139–215). New York: Plenum Press.

    Google Scholar 

  • Vogt, C., & Vogt, O. (1919). Allgemeine Ergebnisse unserer Hirnforschung. Journal Psychol Neurol, 25, 279–461.

    Google Scholar 

  • von Economo, C., Koskinas, G. N. (1925). Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. Berlin: Springer.

    Google Scholar 

  • Waldeyer-Hartz, H. W. G. (1891). Über einige neuere Forschungen im Febiete der Anatomie des Centralnervensystems. Deutsche medicinische Wochenschrift, 17, 1213–1218, 1244–1246, 1287–1289, 1331–1332, 1350–1356.

    Google Scholar 

  • Waters, J., Schaefer, A., & Sakmann, B. (2005). Backpropagating action potentials in neurones: measurement, mechanisms and potential functions. Progress in biophysics and molecular biology, 87, 145–170.

    Article  Google Scholar 

  • Zakon, H. H. (2012). Adaptive evolution of voltage-gated sodium channels: The first 800 million years. Proceedings of the Natural Academy of Science USA, 109, 10619–10625.

    Google Scholar 

  • Zhuo, M., & Hawkins, R. D. (1995). Long-term depression: A learning-related type of synaptic plasticity in the mammalian central nervous system. Reviews in the Neurosciences, 6, 259–277.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Cognitive Science, University of Messina, Catania, Italy

    Alessio Plebe

  2. Department of Educational Sciences, University of Catania, Catania, Italy

    Vivian M. De La Cruz

Authors
  1. Alessio Plebe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivian M. De La Cruz
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Plebe, A., De La Cruz, V.M. (2016). The Computational Units of the Brain. In: Neurosemantics. Studies in Brain and Mind, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-28552-8_2

Download citation

Publish with us

Policies and ethics

Search

Navigation