Skip to main content
SpringerLink
Log in

Grundlagenfragen der Neurocomputation und Neurokognition

  • Living reference work entry
  • First Online:
Philosophisches Handbuch Künstliche Intelligenz

Part of the book series: Springer Reference Geisteswissenschaften ((SPREFGEIST))

Zusammenfassung

Der Artikel behandelt philosophische Grundlagenfragen der Computation und der Kognition in neuronalen Systemen. Im Zentrum steht die computationale Theorie des Geistes in ihrem Wandel vom symbolistisch-regelbasierten Paradigma der klassischen KI zum konnektionistischen Paradigma auf der Basis neuronaler Netze und neurocomputationaler Modelle der Kognition. Es werden verschiedene Arten der Computation (digital, analog, neuronal) und Konzeptionen von Computation (strukturell-kausal, mechanistisch, semantisch) analysiert. Nach Vorstellung der computationalen und repräsentationalen Theorie des Geistes werden Theorien der Semantik und Repräsentation behandelt. Schließlich werden Fragen des Subsymbolismus und distribuierter neuronaler Repräsentationen und das Konzept strukturaler Repräsentation diskutiert.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Literatur

  • Aaronson, Scott. 2013. Quantum computing since democritus. Cambridge: Cambridge University Press.

    Google Scholar 

  • Bartels, Andreas. 2005. Strukturale Representation. Paderborn: mentis.

    Google Scholar 

  • Bartels, Andreas. 2006. Defending the structural concept of representation. Theoria 55:7–19.

    Google Scholar 

  • Bechtel, William. 2008. Mental mechanisms: Philosophical perspectives on cognitive neuroscience. New York: Routledge.

    Google Scholar 

  • Bechtel, William, und Adele Abrahamsen. 2002. Connectionism and the mind: Parallel processing, dynamics, and evolution in networks, 2. Aufl. Oxford: Blackwell.

    Google Scholar 

  • Bednar, James A., und Stuart P. Wilson. 2015. Cortical maps. The Neuroscientist 22(6): 604–617.

    Article  Google Scholar 

  • Bengio, Yoshua, Dong-Hyun Lee, Jörg Bornschein, Thomas Mesnard, und Zhouhan Lin. 2016. Towards biologically plausible deep learning. arXiv:1502.04156v3.

    Google Scholar 

  • Bennett, Charles H. 1982. The thermodynamics of computation: A review. International Journal of Theoretical Physics 21:905–940.

    Article  Google Scholar 

  • Block, Ned. 1990. Inverted earth. Philosophical Perspectives 4:52–79.

    Article  Google Scholar 

  • Block, Ned. 1998. Semantics, conceptual role. In The Routledge encylopedia of philosophy, Hrsg. E. Craig. London: Routledge.

    Google Scholar 

  • Buzsaki, Gyorgy. 2006. Rhythms of the brain. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Cao, Rosa. 2014. Signaling in the brain: In search of functional units. Philosophy of Science 81(5): 891–901.

    Article  Google Scholar 

  • Chalmers, David J. 1996a. The conscious mind. Oxford: Oxford University Press.

    Google Scholar 

  • Chalmers, David J. 1996b. Does a rock implement every finite-state automaton? Synthese 108:309–333.

    Article  Google Scholar 

  • Chalmers, David J. 2011. A computational foundation for the study of cognition. Journal of Cognitive Science 12:323–357.

    Google Scholar 

  • Chirimuuta, Mazviita. 2018. Explanation in computational neuroscience: Causal and non-causal. The British Journal for the Philosophy of Science 9(3): 849–880.

    Article  Google Scholar 

  • Churchland, Patricia S., und Terrence J. Sejnowski. 1992. The computational brain. Cambridge, MA: MIT Press.

    Book  Google Scholar 

  • Churchland, Paul M. 1989. A neurocomputational perspective. Cambridge, MA: MIT Press.

    Google Scholar 

  • Churchland, Paul M. 1995. The engine of reason, the seat of the soul: A philosophical journey into the brain. Cambridge, MA: MIT Press.

    Google Scholar 

  • Clark, Andy. 1989. Microcognition: Philosophy, cognitive science and parallel distributed processing. Cambridge, MA: MIT Press.

    Google Scholar 

  • Clark, Andy. 1993. Associative engines: Connectionism, concepts and representational change. Cambridge, MA: MIT Press.

    Book  Google Scholar 

  • Clark, Andy. 2014. Mindware: An introduction to the philosophy of cognitive science, 2. Aufl. New York: Oxford University Press.

    Google Scholar 

  • Clark, Andy. 2016. Surfing uncertainty: Prediction, action, and the embodied mind. New York: Oxford University Press.

    Book  Google Scholar 

  • Coltheart, Max. 2017. Grandmother cells and the distinction between local and distributed representation. Language, Cognition and Neuroscience 32(3): 350–358.

    Article  Google Scholar 

  • Craver, Carl. 2007. Explaining the brain: Mechanisms and the Mosaic unity of neuroscience. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Craver, Carl, und David Kaplan. 2011. Towards a mechanistic philosophy of neuroscience: A mechanistic approach. In The continuum companion to the philosophy of science, Hrsg. S. French und J. Saatsi. London: Continuum.

    Google Scholar 

  • Cummins, Robert, und Pierre Poirier. 2004. Representation and indication. In Representation in mind: New approaches to mental representation, Hrsg. H. Clapin, P. Staines und P. Slezak, 21–40. Oxford: Elsevier.

    Google Scholar 

  • Cummins, Robert C. 1989. Meaning and mental representation. Cambridge, MA: MIT Press.

    Google Scholar 

  • Cummins, Robert C. 1996. Representations, targets, and attitudes. Cambridge, MA: MIT Press.

    Book  Google Scholar 

  • Dayan, Peter, und Laurence F. Abbott. 2001. Theoretical neuroscience. Cambridge, MA: MIT Press.

    Google Scholar 

  • Dennett, Daniel C. 1987. The intentional stance. Cambridge, MA: MIT Press.

    Google Scholar 

  • Devitt, Michael. 1996. Coming to our senses. New York: Cambridge University Press.

    Google Scholar 

  • Dewhurst, Joe. 2018. Individuation without representation. The British Journal for the Philosophy of Science 69:103–116.

    Google Scholar 

  • Dretske, Fred. 1981. Knowledge and the flow of information. Cambridge, MA: MIT Press.

    Google Scholar 

  • Dretske, Fred. 1995. Naturalizing the mind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Egan, Frances. 2010. Computational models: A modest role for content. Studies in History and Philosophy of Science 41:253–259.

    Article  Google Scholar 

  • Egan, Frances. 2012. Metaphysics and computational cognitive science: Let’s not let the tail wag the dog. Journal of Cognitive Science 13:39–49.

    Article  Google Scholar 

  • Engel, Andreas K., Andreas K. Kreiter, Peter König, und Wolf Singer. 1991. Synchronization of oscillatory neuronal responses between striate and extrastriate visual cortical areas of the cat. Proceedings of the National Academy of Sciences USA 88(14): 6048–6052.

    Article  Google Scholar 

  • Feldman, Daniel E. 2012. The spike-timing dependence of plasticity. Neuron 75(4): 556–571.

    Article  Google Scholar 

  • Figdor, Carrie. 2009. Semantic externalism and the mechanics of thought. Minds and Machines 19:1–24.

    Article  Google Scholar 

  • Fodor, Jerry A. 1975. The language of thought. New York: Thomas Y. Crowell.

    Google Scholar 

  • Fodor, Jerry A. 1985. Fodor’s guide to mental representation. The intelligent auntie’s Vade-Mecum. Mind 94(373): 76–100.

    Article  Google Scholar 

  • Fodor, Jerry A. 1987. Psychosemantics. Cambridge, MA: MIT Press.

    Book  Google Scholar 

  • Fodor, Jerry A., und Zenon Pylyshyn. 1988. Connectionism and cognitive architecture: A critical analysis. Cognition 28:3–71.

    Article  Google Scholar 

  • Fodor, Jerry, und Ernest Lepore. 1992. Holism: A shopper’s guide. Cambridge, MA: Blackwell.

    Google Scholar 

  • Fresco, Nir. 2014. Physical computation and cognitive science. Berlin: Springer.

    Book  Google Scholar 

  • Gärdenfors, Peter. 2000. Conceptual spaces. Cambridge, MA: MIT Press.

    Book  Google Scholar 

  • Garson, James W. 2001. (Dis)solving the binding problem. Philosophical Psychology 14(4): 381–392.

    Article  Google Scholar 

  • Gazzaniga, Michael S., und George R. Mangun, Hrsg. 2014. The cognitive neurosciences, 5. Aufl. Cambridge, MA: MIT Press.

    Google Scholar 

  • Gazzaniga, Michael S., Richard B. Ivry, und George R. Mangun. 2014. Cognitive neuroscience: The biology of the mind, 4. Aufl. New York: W. W. Norton.

    Google Scholar 

  • Gerstner, Wulfram, Werner M. Kistler, Richard Naud, und Liam Paninski. 2014. Neuronal dynamics from single neurons to networks and models of cognition. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Gładziejewski, Paweł. 2016. Action guidance is not enough, representations need correspondence too: A plea for a two-factor theory of representation. New Ideas in Psychology 40:13–25.

    Article  Google Scholar 

  • Gładziejewski, Paweł, und Marcin Miłkowski. 2017. Structural representations: Causally relevant and different from detectors. Biology and Philosophy 32:337–355.

    Article  Google Scholar 

  • Goodman, Nelson. 1976. Languages of art: An approach to a theory of symbols, 2. Aufl. Indianapolis: Hackett Publishing Company.

    Google Scholar 

  • Harbecke, Jens, und Oron Shagrir. 2019. The role of the environment in computational explanations. European Journal for Philosophy of Science 9:37.

    Article  Google Scholar 

  • Hardcastle, Valerie G. 1994. The binding problem and possible solutions. Journal of Consciousness Studies 1:66–90.

    Google Scholar 

  • Harnad, Stevan. 1990. The symbol grounding problem. Physica D 42:335–346.

    Google Scholar 

  • Harnish, Robert M. 2001. Minds, brains, computers: An historical introduction to the foundations of cognitive science. Cambridge, MA: Blackwell.

    Google Scholar 

  • Hassabis, Demis, Dharshan Kumaran, Christopher Summerfield, und Matthew Botvinick. 2017. Neuroscience-inspired artificial intelligence. Neuron 95:245–258.

    Article  Google Scholar 

  • Haugeland, John. 1981. Semantic engines: An introduction to mind design. In Mind design, Hrsg. J. Haugeland. Cambridge, MA: MIT Press.

    Google Scholar 

  • Hebb, Donald. 1949. The organization of behaviour. New York: Wiley.

    Google Scholar 

  • Hemmo, Meir, und Orly Shenker. 2019. The physics of implementing logic: Landauer’s principle and the multiple-computations theorem. Studies in History and Philosophy of Modern Physics 68:90–105.

    Article  Google Scholar 

  • Hohwy, Jakob. 2013. The predictive mind. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Hubel, David H., und Torsten N. Wiesel. 1959. Receptive fields of single neurones in the cat’s striate cortex. Journal of Physiology 148:574–591.

    Google Scholar 

  • Hubel, David H., und Torsten N. Wiesel. 1962. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology 160:106–154.

    Google Scholar 

  • Isaac, Alistair M. C. 2013. Objective similarity and mental representation. Australasian Journal of Philosophy 91:683–704.

    Article  Google Scholar 

  • Kaplan, David M. 2011. Explanation and description in computational neuroscience. Synthese 183:339–373.

    Article  Google Scholar 

  • Karnath, Hans-Otto, und Peter Thier, Hrsg. 32012. Kognitive neurowissenschaften. Berlin: Springer.

    Google Scholar 

  • Koch, Christof. 1999. Biophysics of computation: Information processing in single neurons. New York: Oxford University Press.

    Google Scholar 

  • Ladyman, James. 2009. What does it mean to say that a physical system implements a computation? Theoretical Computer Science 410:376–383.

    Article  Google Scholar 

  • Landauer, Rolf. 1961. Irreversibility and heat generation in the computing process. IBM Journal of Research and Development 5:183–191.

    Google Scholar 

  • Leff, H. S., und A. Rex, Hrsg. 2003. Maxwell’s demon 2: Entropy, classical and quantum information, computing. Bristol, UK: Institute of Physics Publishing.

    Google Scholar 

  • Lettvin, Jerome Y., Humberto R. Maturana, Warren S. McCulloch, und Walter Pitts. 1959. What the frog’s eye tells the frog’s brain. Proceedings of the IRE 47:1940–1951.

    Article  Google Scholar 

  • Lyre, Holger. 2017. Philosophie der Neurowissenschaften. In Grundriss Wissenschaftsphilosophie: Die Philosophien der Einzelwissenschaften, Hrsg. S. Lohse und T. Reydon, 319–353. Hamburg: Meiner.

    Google Scholar 

  • Lyre, Holger. 2018. Structures, dynamics and mechanisms in neuroscience – an integrative account. Synthese 195(12): 5141–5158.

    Article  Google Scholar 

  • Maass, Wolfgang. 1996. Networks of spiking neurons: The third generation of neural network models. Neural Networks 10(9): 1659–1671.

    Article  Google Scholar 

  • Maley, Corey J. 2018. Toward analog neural computation. Minds and Machines 28(1): 77–91.

    Article  Google Scholar 

  • Malsburg, Christoph von der. 1973. Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14(2): 85–100.

    Google Scholar 

  • Malsburg, Christoph von der. 1999. The what and why of binding: The modeler’s perspective. Neuron 24:95–104.

    Article  Google Scholar 

  • Malsburg, Christoph von der, William A. Phillips, und Wolf Singer. 2010. Dynamic coordination and the brain: From neurons to mind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Marr, David. 1982. Vision: A computational investigation into the human representation and processing of visual information. New York: Freeman.

    Google Scholar 

  • Milkowski, Marcin. 2013. Explaining the computational mind. Cambridge, MA: MIT Press.

    Book  Google Scholar 

  • Millikan, Ruth. 1984. Language, thought and other biological categories. Cambridge, MA: MIT Press.

    Google Scholar 

  • Norton, John. 2013. All shook up: Fluctuations, Maxwell’s demon and the thermodynamics of computation. Entropy 15:4432–4483.

    Article  Google Scholar 

  • O’Brien, Gerard, und Jon Opie. 2009. The role of representation in computation. Cognitive Processing 10(1): 53–62.

    Article  Google Scholar 

  • O’Keefe, John, und Lynn Nadel. 1978. The Hippocampus as a cognitive map. Oxford: Clarendon Press.

    Google Scholar 

  • Opie, Jonathan, und Gerard O’Brien. 2004. Notes toward a structuralist theory of mental representation. In Representation in mind: New approaches to mental representation, Hrsg. H. Clapin, P. Staines und P. Slezak, 1–20. Oxford: Elsevier.

    Google Scholar 

  • Palmer, Stephen E. 1978. Fundamental aspects of cognitive representation. In Cognition and categorization, Hrsg. E. Rosch und B. L. Lloyd, 259–302. Hillsdale: Erlbaum.

    Google Scholar 

  • Peebles, David, und Richard P. Cooper. 2015. Special issue: Thirty years after Marr’s vision: Levels of analysis in cognitive science. Topics in Cognitive Science 7 (2).

    Google Scholar 

  • Piccinini, Gualtiero. 2007. Computing mechanisms. Philosophy of Science 74:501–526.

    Article  Google Scholar 

  • Piccinini, Gualtiero. 2008. Computation without representation. Philosophical Studies 137:205–241.

    Article  Google Scholar 

  • Piccinini, Gualtiero. 2015. Physical computation. A mechanistic account. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Piccinini, Gualtiero, und Sonya Bahar. 2013. Neural computation and the computational theory of cognition. Cognitive Science 37(3): 453–488.

    Article  Google Scholar 

  • Putnam, Hilary. 1988. Representation and reality. Cambridge, MA: MIT Press.

    Google Scholar 

  • Quian Quiroga, Rodrigo, und Stefano Panzeri, Hrsg. 2013. Principles of neural coding. Boca/Raton: CRC Taylor and Francis.

    Google Scholar 

  • Quian Quiroga, Rodrigo, Gabriel Kreiman, Christof Koch, und Itzhak Fried. 2008. Sparse but not ‚grandmother-cell‘ coding in the medial temporal lobe. Trends in Cognitive Sciences 12:87–91.

    Article  Google Scholar 

  • Ramsey, William. 2007. Representation reconsidered. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Ramsey, William. 2016. Untangling two questions about mental representation. New Ideas in Psychology 40:3–12.

    Article  Google Scholar 

  • Rescorla, Michael. 2013. Against structuralist theories of computational implementation. The British Journal for the Philosophy of Science 64:681–707.

    Article  Google Scholar 

  • Rieke, Fred, David Warland, Rob de Ruyter van Steveninck, und William Bialek. 1997. Spikes: Exploring the neural code. Cambridge, MA: MIT Press.

    Google Scholar 

  • Ross, Lauren N. 2015. Dynamical models and explanation in neuroscience. Philosophy of Science 81(1): 32–54.

    Article  Google Scholar 

  • Searle, John R. 1980. Minds, brains and programs. The Behavioral and Brain Sciences 3:417–457.

    Google Scholar 

  • Searle, John R. 1990. Is the brain’s mind a computer program? Scientific American 1:26–31.

    Google Scholar 

  • Searle, John R. 1992. The rediscovery of the mind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Sejnowski, Terrence J. 2018. The deep learning revolution. Cambridge, MA: MIT Press.

    Book  Google Scholar 

  • Shagrir, Oron. 2001. Content, computation and externalism. Mind 110:369–400.

    Article  Google Scholar 

  • Shagrir, Oron. 2010. Marr on computational-level theories. Philosophy of Science 77:477–500.

    Article  Google Scholar 

  • Shagrir, Oron. 2012. Structural representations and the brain. The British Journal for the Philosophy of Science 63:519–545.

    Article  Google Scholar 

  • Shagrir, Oron. 2018a. The brain as an input-output model of the world. Minds and Machines 28:53–75.

    Article  Google Scholar 

  • Shagrir, Oron. 2018b. In defense of the semantic view of computation. Synthese, online first.

    Google Scholar 

  • Shea, Nicolas. 2014. Exploitable isomorphism and structural representation. Proceedings of the Aristotelian Society 114:123–144.

    Article  Google Scholar 

  • Shea, Nicolas. 2018. Representation in cognitive science. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Smolensky, Paul. 1988. On the proper treatment of connectionism. Behavioral and Brain Sciences 11:1–74.

    Article  Google Scholar 

  • Sprevak, Mark. 2010. Computation, individuation, and the received view on representation. Studies in History and Philosophy of Science 41:260–270.

    Article  Google Scholar 

  • Swoyer, Chris. 1991. Structural representation and surrogative reasoning. Synthese 87:449–508.

    Article  Google Scholar 

  • Trappenberg, Thomas P. 2010. Fundamentals of computational neuroscience, 2. Aufl. Oxford: Oxford University Press.

    Google Scholar 

  • Turing, Alan M. 1936. On computable numbers, with an application to the entscheidungsproblem. Proceedings of the London Mathematical Society 42:230–265.

    Google Scholar 

  • Ullman, Shimon. 2019. Using neuroscience to develop artificial intelligence. Science 363(6428): 692–693.

    Article  Google Scholar 

  • van Fraassen, Bas C. 2008. Scientific representation: paradoxes of perspective. Oxford: Oxford University Press.

    Google Scholar 

  • Willshaw, David J., und Christoph von der Malsburg. 1976. How patterned neural connections can be set up by self-organization. Proceedings of the Royal Society of London Series B 194:431–445.

    Google Scholar 

  • Wittgenstein, Ludwig. 1947–1949. Bemerkungen über die Philosophie der Psychologie. Werkausgabe Band 7, 1984. Frankfurt a. M.: Suhrkamp.

    Google Scholar 

  • Wittgenstein, Ludwig. 1953. Philosophische Untersuchungen. Kritisch-genetische Edition 2003. Frankfurt a. M.: Suhrkamp.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lehrstuhl für Theoretische Philosophie, Universität Magdeburg, Magdeburg, Deutschland

    Holger Lyre

Authors
  1. Holger Lyre
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Holger Lyre .

Editor information

Editors and Affiliations

  1. Technische Universität München (TUM), München, Germany

    Klaus Mainzer

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Lyre, H. (2020). Grundlagenfragen der Neurocomputation und Neurokognition. In: Mainzer, K. (eds) Philosophisches Handbuch Künstliche Intelligenz. Springer Reference Geisteswissenschaften. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-23715-8_17-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-658-23715-8_17-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer VS, Wiesbaden

  • Print ISBN: 978-3-658-23715-8

  • Online ISBN: 978-3-658-23715-8

  • eBook Packages: Springer Referenz Sozialwissenschaften und Recht

Publish with us

Policies and ethics

Search

Navigation