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Modeling Neural Representations

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Neurosemantics

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

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Abstract

As the final part of the semantics of neurons, and as a prelude to the second part, the neurosemantics of language, this chapter seeks mathematical formulations for the mechanisms that enable the construction of representations in the brain. It is not a general review of the rich variety of mathematical solutions proposed so far for simulating neural circuits, currently available on the market. It is the introduction to the mathematical framework adopted in all the neurosemantic models that will be described in the second part.

One of the main challenges any endeavor of mathematical formalization of neural activities must face, is their impressive abundance.

The number of neurons involved in almost all cognitive functions is so large that it is impossible to give an overall sense of their activity by means of individual descriptions. Mathematics only offers the great advantage of synthesis, the possibility of capturing in a concise formulation the principles ruling the behavior of millions of interacting elements. Mathematical formulations can be implemented in a software, and the simulated results can be analyzed in detail.

In the cortex, neurons are characterized not only by their large number, but also by properties such as local cooperative and competitive interactions, which fit well within an established mathematical framework, that of self-organization. The adopted neural architecture derives from this general framework. In the interpretation of the activities of many neurons in the same cortical area, resulting from a simulation, a well established neurocomputational concept will be used, that of population coding, discussed in the last section of this chapter.

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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 

  • Ashby, W. R. (1947). Principles of the self-organizing dynamic system.The Journal Of General Psychology, 37, 125–128.

    Article  Google Scholar 

  • Ashby, W. R. (1962). Principles of the self-organizing system. In H. V. Foerster & G. W. Zopf (Eds.),Principles of Self-Organization: Transactions of the University of Illinois Symposium (pp. 255–278). New York: Pergamon.

    Google Scholar 

  • Bednar, J. A. (2002). Learning to see: Genetic and environmental influences on visual development. PhD thesis, University of Texas at Austin, Tech report AI-TR-02-294.

    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. (2014). Topographica. In D. Jaeger & R. Jung (Eds.),Encyclopedia of computational neuroscience (pp. 1–5). Berlin: Springer.

    Chapter  Google Scholar 

  • Belousov, B. (1959). Periodically acting reaction and its mechanism.Collection of Abstracts on Radiation Medicine, 147, 145. Originale in lingua russa.

    Google Scholar 

  • Bénard, H. (1900). Les tourbillons cellulaires dans une nappe liquide.Revue Générale des Sciences, 11, 1261–1271, 1309–1328.

    Google Scholar 

  • Borg, I., & Groenen, P. (2010).Modern multidimensional scaling: Theory and applications (2nd ed.). Berlin: Springer.

    Google Scholar 

  • Borg, I., & Lingoes, J. (1987).Multidimensional similarity structure analysis. Berlin: Springer.

    Book  Google Scholar 

  • Bower, J. M., & Beeman, D. (1998).The book of GENESIS: Exploring realistic neural models with the GEneral NEural SImulation System (2nd ed.). New York: Springer

    Book  Google Scholar 

  • Bowers, J. (2009). On the biological plausibility of grandmother cells: Implications for neural network theories in psychology and neuroscience.Psychological Review, 116, 220–1078.

    Article  Google Scholar 

  • Broad, C. D. (1925).The mind and its place in nature. London: Kegan Paul.

    Google Scholar 

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

    Article  Google Scholar 

  • Carandini, M., & Heeger, D. (2012). Normalization as a canonical neural computation.Nature Reviews Neuroscience, 13, 51–62.

    Article  Google Scholar 

  • Cerreira-Perpiñán, M., & Goodhill, G. J. (2004). Influence of lateral connections on the structure of cortical maps.Journal of Neurophysiology, 92, 2947–295.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chirimuuta, M. (2014). Minimal models and canonical neural computations: The distinctness of computational explanation in neuroscience.Synthese, 191, 127–153.

    Article  Google Scholar 

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

    Google Scholar 

  • Clayton, P., & Davies, P. (Eds.) (2006).The re-emergence of emergence: The emergentist hypothesis from science to religion. Oxford: Oxford University Press.

    Google Scholar 

  • Cottrell, M., & Fort, J. (1987). Étude d’un processus d’auto-organisation.Annales de l’ institut Henri Poincaré, 23, 1–20.

    Google Scholar 

  • Dayan, P., & Abbott, L. F. (2001).Theoretical neuroscience. Cambridge: MIT.

    Google Scholar 

  • de la Rocha, J., Doiron, B., Shea-Brown, E., Josić, K., & Reyes, A. (2007). Correlation between neural spike trains increases with firing rate.Nature, 448, 802–809.

    Article  Google Scholar 

  • Eliasmith, C., & Trujillo, O. (2014). The use and abuse of large-scale brain models.Current Opinion in Neurobiology, 25, 1–6.

    Article  Google Scholar 

  • Erwin, E., Obermayer, K., & Schulten, K. (1992a). Self-organizing maps: Ordering, convergence properties and energy functions.Biological Cybernetics, 67, 47–55.

    Article  Google Scholar 

  • Erwin, E., Obermayer, K., & Schulten, K. (1992b). Self-organizing maps: Stationary states, metastability and convergence rate.Biological Cybernetics, 67, 35–45.

    Article  Google Scholar 

  • Gerstner, W., & Kistler, W. M. (2002). Mathematical formulations of Hebbian learning.Biological Cybernetics, 87, 404–415.

    Article  Google Scholar 

  • Gilbert, C. D., Hirsch, J. A., Wiesel, T. N. (1990). Lateral interactions in visual cortex.Cold Spring Harbor Symposia on Quantitative Biology, 55, 663–677. Cold Spring Harbor Laboratory 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 

  • Gross, C. (2002). Genealogy of the “grandmother cell”.Neuroscience, 8, 512–518.

    Article  Google Scholar 

  • Haken, H. (1978).Synergetics – An introduction, nonequilibrium phase transitions and self-organization in physics, chemistry and biology (2nd ed.). Berlin: Springer.

    Google Scholar 

  • Hasker, W. (1999).The emergent self. Ithaca: Cornell University Press.

    Google Scholar 

  • Hines, M., & Carnevale, N. (1997). The NEURON simulation environment.Neural Computation, 9, 1179–1209.

    Article  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 (pp. 77–109). Cambridge: MIT.

    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 

  • Hubel, D., & Wiesel, T. (1962). Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex.Journal of Physiology, 160, 106–154.

    Article  Google Scholar 

  • Hubel, D., & Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex.Journal of Physiology, 195, 215–243.

    Article  Google Scholar 

  • Hunt, J. J., Bosking, W. H., & Goodhill, G. J. (2011). Statistical structure of lateral connections in the primary visual cortex.Neural Systems & Circuits, 1, 1–12.

    Article  Google Scholar 

  • Kanerva, P. (1993). Sparse distributed memory and related models. In M. Hassoun (Ed.),Associative neural memories: Theory and implementation. Oxford: Oxford University Press.

    Google Scholar 

  • Kauffman, S. A. (1993).The origins of order – Self-organization and selection in evolution. Oxford: Oxford University Press.

    Google Scholar 

  • Kauffman, S. A. (2008).Reinventing the sacred: A new view of science, reason, and religion. New York: Basic Books.

    Google Scholar 

  • Kim, J. (2006). Emergence: Core ideas and issues.Synthese, 151, 547–559.

    Article  Google Scholar 

  • Kohonen, T. (1982). Self-organizing formation of topologically correct feature maps.Biological Cybernetics, 43, 59–69.

    Article  Google Scholar 

  • Kohonen, T. (1984).Self-organization and associative memory. Berlin: Springer.

    Google Scholar 

  • Kohonen, T. (1995).Self-organizing maps. Berlin: Springer.

    Book  Google Scholar 

  • Kriegeskorte, N. (2009). Relating population-code representations between man, monkey, and computational models.Frontiers in Neuroscience, 3, 363–373.

    Article  Google Scholar 

  • Lehky, S. R., Sereno, M. E., & Sereno, A. B. (2013). Population coding and the labeling problem: Extrinsic versus intrinsic representations.Neural Computation, 25, 2235–2264.

    Article  Google Scholar 

  • Lennie, P. (2003). The cost of cortical computation.Current Biology, 13, 493–497.

    Article  Google Scholar 

  • Linde, Y., Buzo, A., & Gray, R. (1980). An algorithm for vector quantizer design.IEEE Transactions on Communications, 28, 84–95.

    Article  Google Scholar 

  • Markram, H. (2006). The blue brain project.Nature Reviews Neuroscience, 7, 153–160.

    Article  Google Scholar 

  • Mastronarde, D. N. (1983). Correlated firing of retinal ganglion cells: I. Spontaneously active inputs in X- and Y-cells.Journal of Neuroscience, 14, 409–441.

    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 

  • Miller, K. D., & MacKay, D. J. C. (1994). The role of constraints in Hebbian learning.Neural Computation, 6, 100–126.

    Article  Google Scholar 

  • Milner-Brown, H. S., Stein, R. B., & Yemm, R. (1973). Changes in firing rate of human motor units during linearly changing voluntary contractions.Journal of Physiology, 230, 371–390.

    Article  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 

  • Olshausen, B. A., & Field, D. J. (2004). Sparse coding of sensory inputs.Current Opinion in Neurobiology, 14, 481–487.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Plebe, A. (2001). Self-organizing map approaches to the traveling salesman problem. In M. Maggini (Ed.),Limitations and Future Trends in Neural Computation, NATO Advanced Research Workshop, 22–24 Oct 2001, Siena.

    Google Scholar 

  • Plebe, A., & Anile, M. (2001). A neural-network-based approach to the double traveling salesman problem.Neural Computation, 14(2), 437–471.

    Article  Google Scholar 

  • Pribram, K. H. (1971).Languages of the brain: Experimental paradoxes and principles in neuropsychology. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Prigogine, I. (1961).Introduction to thermodynamics of irreversible processes. New York: Interscience.

    Google Scholar 

  • Quian Quiroga, R., & Kreiman, G. (2010). Measuring sparseness in the brain: Comment on bowers (2009).Psychological Review, 117, 291–297.

    Article  Google Scholar 

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

    Google Scholar 

  • Quian Quiroga, R., Reddy, L., Koch, C., & Fried, I. (2007). Decoding visual inputs from multiple neurons in the human temporal lobe.Journal of Neurophysiology, 4, 1997–2007.

    Article  Google Scholar 

  • Quian Quiroga, R., Kreiman, G., Koch, C., & Fried, I. (2008). Sparse but not ‘grandmother-cell’ coding in the medial temporal lobe.Trends in Cognitive Sciences, 12, 87–91.

    Article  Google Scholar 

  • Ritter, H., Martinetz, T., & Schulten, K. (1992).Neural computation and self-organizing maps. Reading: Addison Wesley.

    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.

    Google Scholar 

  • Ruelle, D., & Takens, F. (1971). On the nature of turbulence.Communications in Mathematical Physics, 20, 167–192.

    Article  Google Scholar 

  • Rumelhart, D. E., & McClelland, J. L. (1986). On learning the past tenses of English verbs. In D. E. Rumelhart, & McClelland, J. L. (Eds.)Parallel distributed processing: Explorations in the microstructure of cognition (pp. 216–271). Cambridge: MIT.

    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 

  • Singer, W. (1995). Synchronization of neuronal responses as a putative binding mechanism. InThe handbook of brain theory and neural networks. Cambridge: MIT.

    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 

  • Sirosh, J., Miikkulainen, R., & Choe, Y. (Eds.) (1996).Lateral interactions in the cortex: Structure and function. Austin: The UTCS Neural Networks Research Group.

    Google Scholar 

  • Stettler, D. D., Das, A., Bennett, J., & Gilbert, C. D. (2002). Lateral connectivity and contextual interactions in macaque primary visual cortex.Neuron, 36, 739–750.

    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, 15,747–15,766.

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • von der Malsburg, C. (1995a). Binding in models of perception and brain function.Current Opinion in Neurobiology, 5, 520–526.

    Article  Google Scholar 

  • von der Malsburg, C. (1995b). Network self-organization in the ontogenesis of the mammalian visual system. In S. F. Zornetzer, J. Davis, C. Lau, & T. McKenna (Eds.),An introduction to neural and electronic networks (2nd ed., pp. 447–462). New York: Academic.

    Google Scholar 

  • Willshaw, D. (2006). Self-organization in the nervous system. In R. Morris & L. Tarassenko (Eds.),Cognitive systems: Information processing meets brain science (pp. 5–33). Amsterdam: Elsevier.

    Chapter  Google Scholar 

  • Willshaw, D. J., & von der Malsburg, C. (1976). How patterned neural connections can be set up by self-organization.Proceedings of the Royal Society of London, B194, 431–445.

    Google Scholar 

  • Yu, H., Farley, B. J., Jin, D. Z., & Sur, M. (2005). The coordinated mapping of visual space and response features in visual cortex.Neuron, 47, 267–280.

    Article  Google Scholar 

  • Zhabotinsky, A. (1964). Periodical process of oxidation of malonic acid solution.Biophysics, 56, 178–194.

    Google Scholar 

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Plebe, A., De La Cruz, V.M. (2016). Modeling Neural Representations. In: Neurosemantics. Studies in Brain and Mind, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-28552-8_4

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