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Neural Representations Beyond “Plus X”

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Abstract

In this paper we defend structural representations, more specifically neural structural representation. We are not alone in this, many are currently engaged in this endeavor. The direction we take, however, diverges from the main road, a road paved by the mathematical theory of measure that, in the 1970s, established homomorphism as the way to map empirical domains of things in the world to the codomain of numbers. By adopting the mind as codomain, this mapping became a boon for all those convinced that a representation system should bear similarities with what was being represented, but struggled to find a precise account of what such similarities mean. The euforia was brief, however, and soon homomorphism revealed itself to be affected by serious weaknesses, the primary one being that it included systems embarrassingly alien to representations. We find that the defense attempts that have followed, adopt strategies that share a common format: valid structural representations come as “homomorphism plus X”, with various “X”, provided in descriptive format only. Our alternative direction stems from the observation of the overlooked departure from homomorphism as used in the theory of measure and its later use in mental representations. In the former case, the codomain or the realm of numbers, is the most suited for developing theorems detailing the existence and uniqueness of homomorphism for a wide range of empirical domains. In the latter case, the codomain is the realm of the mind, possibly more vague and more ill-defined than the empirical domain itself. The time is ripe for articulating the mapping between represented domains and the mind in formal terms, by exploiting what is currently known about coding mechanisms in the brain. We provide a sketch of a possible development in this direction, one that adopts the theory of neural population coding as codomain. We will show that our framework is not only not in disagreement with the “plus X” proposals, but can lead to natural derivation of several of the “X”.

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Notes

  1. We thank an anonymous reviewer for offering this insight, our counterexample is a slight modification of his own.

References

  • Abbott, L. F., Rolls, E., & Tovee, M. J. (1996). Representational capacity of face coding in monkeys. Cerebral Cortex, 6, 498–505.

    Article  Google Scholar 

  • Bartels, A. (2006). Defending the structural concept of representation. Theoria, 55, 7–19.

    MathSciNet  MATH  Google Scholar 

  • Bechtel, W. (2011). Mechanism and biological explanation. Philosophy of Science, 78, 533–557.

    Article  Google Scholar 

  • Bednar, J. A. (2009). Topographica: Building and analyzing map-level simulations from Python, C/C++, MATLAB, NEST, or NEURON components. Frontiers in Neuroinformatics, 3, 8.

    Article  Google Scholar 

  • Bednar, J. A. (2012). Building a mechanistic model of the development and function of the primary visual cortex. Journal of Physiology—Paris, 106, 194–211.

    Article  Google Scholar 

  • Bermúdez-Rattoni, F. (Ed.) (2007). Neural plasticity and memory: From genes to brain imaging. Boca Raton, FL: CRC Press.

  • Bernstein, L. E., & Liebenthal, E. (2014). Neural pathways for visual speech perception. Frontiers in Neuroscience, 8, 386.

    Article  Google Scholar 

  • Blahut, R. E. (1987). Principles and practice of information theory. Reading, MA: Addison Wesley.

    MATH  Google Scholar 

  • Brooks, R. A. (1991). Intelligence without representation. In J. Haugeland (Ed.), Mind Design II (2nd ed., pp. 395–420). Cambridge, MA: MIT Press.

    Google Scholar 

  • Brunel, N., & Nadal, J. P. (1998). Mutual information, fisher information, and population coding. Neural Computation, 10, 1731–1757.

    Article  Google Scholar 

  • Buonomano, D. V., & Merzenich, M. M. (1998). Cortical plasticity: From synapses to maps. Annual Review of Neuroscience, 21, 149–186.

    Article  Google Scholar 

  • Calvert, G. A., Bullmore, E. T., Brammer, M. J., Campbell, R., Williams, S. C. R., McGuire, P. K., et al. (1997). Activation of auditory cortex during silent lipreading. Science, 276, 593–596.

    Article  Google Scholar 

  • Casasanto, D., & Lupyan, G. (2015). All concepts are ad hoc concepts. In S. Laurence & E. Margolis (Eds.), The conceptual mind: New directions in the study of concepts. Cambridge, MA: MIT Press.

    Google Scholar 

  • Cerreira-Perpiñán, M., & Goodhill, G. J. (2004). Influence of lateral connections on the structure of cortical maps. Journal of Neurophysiology, 92, 2947–2959.

    Article  Google Scholar 

  • Chalmers, D. (2011). A computational foundation for the study of cognition. Journal of Consciousness Studies, 12, 323–357.

    Google Scholar 

  • Chemero, A. (2000). Anti-representationalism and the dynamical stance. Philosophy of Science, 67, 625–647.

    Article  Google Scholar 

  • Chikazoe, J., Lee, D. H., Kriegeskorte, N., & Anderson, A. K. (2014). Population coding of affect across stimuli, modalities and individuals. Nature Neuroscience, 17, 1114–1122.

    Article  Google Scholar 

  • Church, A. (1941). The Calculi of Lambda Conversion. Princeton, NJ: Princeton University Press.

    MATH  Google Scholar 

  • Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge, MA: MIT Press.

    Google Scholar 

  • Churchland, P. S., & Sejnowski, T. (1994). The computational brain. Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  • Churchland, P. S., & Sejnowski, T. J. (1990). Neural representation and neural computation. Philosophical Perspectives, 4, 343–382.

    Article  Google Scholar 

  • Clark, A. (2001). Reasons, robots and the extended mind. Minds and Language, 16, 121–145.

    Article  Google Scholar 

  • Copeland, J. B., Posy, C. J., & Shagrir, O. (Eds.). (2013). Computability: Turing, gödel, church, and beyond. Cambridge, MA: MIT Press.

  • Cummins, R. (1989). Meaning and mental representation. Cambridge, MA: MIT Press.

    Google Scholar 

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

    MATH  Google Scholar 

  • Falmagne, J. C. (1980). A probabilistic theory of extensive measurement. Philosophy of Science, 47, 277–296.

    Article  MathSciNet  Google Scholar 

  • Feldman, D. E. (2009). Synaptic mechanisms for plasticity in neocortex. Annual Review of Neuroscience, 32, 33–55.

    Article  Google Scholar 

  • Fitzpatrick, D. C., Batra, R., Stanford, T. R., & Kuwada, S. (1997). A neuronal population code for sound localization. Nature, 388, 871–874.

    Article  Google Scholar 

  • Fodor, J. (1981). Representations: Philosofical essay on the foundation of cognitive science. Cambridge, MA: MIT Press.

    Google Scholar 

  • Fodor, J. (1987). Psychosemantics: The problem of meaning in the philosophy of mind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Fodor, J. (1990). A theory of content and other essays. Cambridge: Cambridge University Press.

    Google Scholar 

  • Fresco, N. (2014). Physical computation and cognitive science. Berlin: Springer.

    Book  Google Scholar 

  • Fusi, S., Miller, E. K., & Rigotti, M. (2016). Why neurons mix: High dimensionality for higher cognition. Current Opinion in Neurobiology, 37, 66–74.

    Article  Google Scholar 

  • Gallistel, C. R. (1990). The organization of learning. Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  • Gallistel, C. R., & King, A. P. (2010). Memory and the computational brain: Why cognitive science will transform neuroscience. New York: Wiley.

    Google Scholar 

  • Ganter, B., & Wille, R. (1999). Formal concept analysis: Mathematical foundations. Berlin: Springer.

    Book  MATH  Google Scholar 

  • van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Science, 21, 615–665.

    Google Scholar 

  • Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986). Neuronal population coding of movement direction. Science, 233, 1416–1419.

    Article  Google Scholar 

  • Gładziejewski, P., & Miłkowski, M. (2017). Structural representations: Causally relevant and different from detectors. Biology and Philosophy, 32, 337–355.

    Article  Google Scholar 

  • Godfrey-Smith, P. (1998). Complexity and the function of mind in nature. Cambridge: Cambridge University Press.

    Google Scholar 

  • Grinvald, A., Lieke, E. E., Frostig, R. D., & Hildesheim, R. (1994). Cortical point-spread function and long-range lateral interactions revealed by real-time optical imaging of macaque monkey primary visual cortex. Journal of Neuroscience, 14, 2545–2568.

    Google Scholar 

  • Grush, R. (2004). The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Science, 27, 377–442.

    Google Scholar 

  • Hartmanis, J., & Stearns, R. E. (1965). On the computational complexity of algorithms. Transaction of American Mathematical Society, 117, 285–306.

    Article  MathSciNet  MATH  Google Scholar 

  • Haugeland, J. (1991). Representational genera. In W. Ramsey, S. P. Stich, & D. E. Rumelhart (Eds.), Philosophy and connectionist theory (pp. 61–89). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Hinton, G. E., McClelland, J. L., & Rumelhart, D. E. (1986). Distributed representations. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (1st ed., pp. 77–109). Cambridge: MIT Press.

    Google Scholar 

  • Hutto, D. D., & Myin, E. (2013). Radicalizing enactivism: Basic minds without content. Cambridge, MA: MIT Press.

    Google Scholar 

  • Isaac, A. M. (2013). Objective similarity and mental representation. Australasian Journal of Philosophy, 91, 683–704.

    Article  Google Scholar 

  • Johnson, D. S., & Papadimitriou, C. H. (1985). Computational complexity. In E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, & D. B. Shmoys (Eds.), The travelling salesman problem: A guided tour of combinatorial optimization (pp. 37–85). New York: New York.

    Google Scholar 

  • Johnson-Laird, P. (1983). Mental models: Towards a cognitive science of language, inference and consciousness. Cambridge: Cambridge University Press.

    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 

  • Kolb, B. (1995). Brain plasticity and behavior. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Krantz, D., Luce, D., Suppes, P., & Tversky, A. (1971). Foundations of measurement—Volume I additive and polynomial representations. New York: Academic Press.

    MATH  Google Scholar 

  • Kripke, S. A. (1972). Naming and necessity. In D. Davidson & G. H. Harman (Eds.), Semantics of natural language (pp. 253–355). Dordrecht, NL: Reidel Publishing Company.

    Chapter  Google Scholar 

  • Lehky, S. R., & Tanaka, K. (2016). Neural representation for object recognition in inferotemporal cortex. Current Opinion in Neurobiology, 37, 23–35.

    Article  Google Scholar 

  • Lehky, S. R., Kiani, R., Esteky, H., & Tanaka, K. (2014). Dimensionality of object representations in monkey inferotemporal cortex. Neural Computation, 26, 2135–2162.

    Article  Google Scholar 

  • Levy, W. B., & Baxter, R. A. (1996). Energy efficient neural codes. Neural Computation, 8, 531–543.

    Article  Google Scholar 

  • Luce, D., Krantz, D., Suppes, P., & Tversky, A. (1990). Foundations of measurement—Volume III representation, axiomatization, and invariance. New York: Academic Press.

    MATH  Google Scholar 

  • Malt, B. C. (2013). Context sensitivity and insensitivity in object naming. Language and Cognition, 5, 81–97.

    Article  Google Scholar 

  • McLendon, H. J. (1955). Uses of similarity of structure in contemporary philosophy. Mind, 64, 79–95.

    Article  Google Scholar 

  • Meyers, E. M., Freedman, D. J., Kreiman, G., Miller, E. K., & Poggio, T. (2008). Dynamic population coding of category information in inferior temporal and prefrontal cortex. Journal of Neurophysiology, 100, 1407–1419.

    Article  Google Scholar 

  • Miikkulainen, R., Bednar, J. A., Choe, Y., & Sirosh, J. (1997). Self-organization, plasticity, and low-level visual phenomena in a laterally connected map model of the primary visual cortex. In R. L. Goldstone, P. G. Schyns, & D. L. Medin (Eds.), Psychology of Learning and Motivation (Vol. 36, pp. 257–308). New York: Academic Press.

    Google Scholar 

  • Miikkulainen, R., Bednar, J., Choe, Y., & Sirosh, J. (2005). Computational maps in the visual cortex. New York: Springer.

    Google Scholar 

  • Miłkowski, M. (2013). Explaining the computational mind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Miłkowski, M. (2016). Function and causal relevance of content. New Ideas in Psychology, 40, 94–102.

    Article  Google Scholar 

  • Millikan, R. G. (1984). Language, thought, and other biological categories: New foundations for realism. Cambridge, MA: MIT Press.

    Google Scholar 

  • Millikan, R. G. (1989). Biosemantic. Journal of Philosophy, 86, 242–265.

    Article  Google Scholar 

  • Miura, K., Mainen, Z. F., & Uchida, N. (2012). Odor representations in olfactory cortex: Distributed rate coding and decorrelated population activity. Neuron, 74, 1087–1098.

    Article  Google Scholar 

  • Montague, R. (1974). In R. H. Thomason (Ed.), Formal philosophy: Selected papers of richard montague. New Haven (CO): Yale University Press.

  • Morgan, A. (2014). Representations gone mental. Synthese, 191, 213–244.

    Article  Google Scholar 

  • Murphy, G. L. (2002). The big book of concepts. Cambridge: Cambridge University Press.

    Google Scholar 

  • Nayar, S., & Murase, H. (1995). Visual learning and recognition of 3-d object by appearence. International Journal of Computer Vision, 14, 5–24.

    Article  Google Scholar 

  • Newman, M. H. A. (1928). Mr. Russell’s “causal theory of perception”. Mind, 37, 137–148.

    Article  Google Scholar 

  • O’Brien, G., & Opie, J. (2004). Notes toward a structuralist theory of mental representation. In H. Clapin, P. Staines, & P. Slezak (Eds.), Representation in mind—New approaches to mental representation. Amsterdam: Elsevier.

    Google Scholar 

  • Olshausen, B. A., & Field, D. J. (1996). Natural image statistics and efficient coding. Network: Computation in Neural Systems, 7, 333–339.

    Article  Google Scholar 

  • Pasupathy, A., & Connor, C. E. (2002). Population coding of shape in area v4. Nature Neuroscience, 5, 1332–1338.

    Article  Google Scholar 

  • Petersen, W. (2007). Representation of concepts as frames. In J. Škilters (Ed.), Complex cognition and qualitative science: A legacy of Oswald Külpe (pp. 151–170). Riga: Latvijas Universitate.

    Google Scholar 

  • Piccinini, G. (2008). Computation without representation. Philosophical studies, 137, 205–241.

    Article  MathSciNet  Google Scholar 

  • Piccinini, G. (2015). Physical computation: A mechanistic account. Oxford: Oxford University Press.

    Book  MATH  Google Scholar 

  • Prinz, J. (2002). Furnishing the mind—Concepts and their perceptual basis. Cambridge, MA: MIT Press.

    Google Scholar 

  • Priss, U. (2006). Formal concept analysis in information science. Annual Review of Information Science and Technology, 40, 521–543.

    Article  Google Scholar 

  • Quian Quiroga, R., & Panzeri, S. (Eds.) (2013). Principles of neural coding. Boca Raton, FL: CRC Press.

  • Ramsey, W. M. (2007). Representation reconsidered. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Ramsey, W. M. (2016). Untangling two questions about mental representation. New Ideas in Psychology, 40, 3–12.

    Article  Google Scholar 

  • Rescorla, M. (2015). The representational foundations of computation. Philosophia Mathematica, 23, 338–366.

    Article  MathSciNet  MATH  Google Scholar 

  • Rolls, E., & Tovee, M. J. (1995). Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. Journal of Neurophysiology, 73, 713–726.

    Article  Google Scholar 

  • Rossi, G. B. (2006). A probabilistic theory of measurement. Measurement, 39, 34–50.

    Article  Google Scholar 

  • Russell, B. (1927). The analysis of matter. London: Harcourt.

    MATH  Google Scholar 

  • Ryder, D. (2004). SINBAD neurosemantics: A theory of mental representation. Minds and Machines, 19, 211–240.

    Google Scholar 

  • Ryder, D. (2009). Problems of representation I: nature and role. In J. Symons & P. Calvo (Eds.), The Routledge companion to philosophy of psychology (pp. 233–250). London: Routledge.

    Google Scholar 

  • Sakai, K., Naya, Y., & Miyashita, Y. (1994). Neuronal tuning and associative mechanisms in form representation. Learning and Menory, 1, 83–105.

    Google Scholar 

  • Scheutz, M. (Ed.) (2002). Computationalism—New Directions. Cambridge, MA: MIT Press.

  • Sejnowski, T. J. (1998). Neural populations revealed. Nature, 332, 308.

    Article  Google Scholar 

  • Shea, N. (2014). Exploitable isomorphism and structural representation. Proceedings of the Aristotelian Society, 114, 123–144.

    Article  Google Scholar 

  • Shepard, R. N., & Metzler, J. (1971). Mental rotation of three-dimensional objects. Science, 171, 701–703.

    Article  Google Scholar 

  • Sirosh, J., & Miikkulainen, R. (1997). Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex. Neural Computation, 9, 577–594.

    Article  Google Scholar 

  • Stevens, J. L. R., Law, J. S., Antolik, J., & Bednar, J. A. (2013). Mechanisms for stable, robust, and adaptive development of orientation maps in the primary visual cortex. JNS, 33(40), 15747–15766.

    Google Scholar 

  • Stokes, M. G., Kusunoki, M., Sigala, N., Nili, H., Gaffan, D., & Duncan, J. (2013). Dynamic coding for cognitive control in prefrontal cortex. Neuron, 78, 364–375.

    Article  Google Scholar 

  • Suppes, P., Krantz, D., Luce, D., & Tversky, A. (1989). Foundations of measurement—Volume II geometrical, threshold, and probabilistic representations. New York: Academic Press.

    MATH  Google Scholar 

  • Swoyer, C. (1991). Structural representation and surrogative reasoning. Synthese, 87, 449–508.

    Article  MathSciNet  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  • Turrigiano, G. G. (2011). Too many cooks? Intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement. Annual Review of Neuroscience, 34, 89–103.

    Article  Google Scholar 

  • Turrigiano, G. G., & Nelson, S. B. (2004). Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience, 391, 892–896.

    Google Scholar 

  • Watson, R. (1995). Representatlonal ldeas: From Plato to Patrlcla Churchland. Dordrecht: Kluwer.

    Google Scholar 

  • Watt, A. J., & Desai, N. S. (2010). Homeostatic plasticity and STDP: Keeping a neuron’s cool in a fluctuating world. Frontiers in Synaptic Neuroscience, 2, 5.

    Article  Google Scholar 

  • Wille, R. (2005). Formal concept analysis as mathematical theory of concepts and concept hierarchies. In Formal concept analysis—Foundations and applications (pp. 1–33). Berlin: Springer.

  • Wilson, S. P., Law, J. S., Mitchinson, B., Prescott, T. J., & Bednar, J. A. (2010). Modeling the emergence of whisker direction maps in rat barrel cortex. PLoS ONE, 5, e8778.

    Article  Google Scholar 

  • Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.

    Google Scholar 

  • Woodward, J. (2008). Mental causation and neural mechanisms. In J. Hohwy & J. Kallestrup (Eds.), Being reduced: New essays on reduction, explanation, and causation (pp. 218–262). Oxford: Oxford University Press.

    Chapter  Google Scholar 

  • Yee, E., & Thompson-Schill, S. L. (2016). Putting concepts into context. Psychonomic Bulletin & Review, 23, 1015–1027.

    Article  Google Scholar 

  • Yuste, R. (2015). From the neuron doctrine to neural networks. Nature Reviews Neuroscience, 16, 1–11.

    Google Scholar 

  • Zemel, R. S., Dayan, P., & Pouget, A. (1998). Probabilistic interpretation of population codes. Neural Computation, 10, 403–430.

    Article  Google Scholar 

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Plebe, A., De La Cruz, V.M. Neural Representations Beyond “Plus X”. Minds & Machines 28, 93–117 (2018). https://doi.org/10.1007/s11023-018-9457-6

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