Abstract
Humans understand their language thanks to brain processes, and one of the most ambitious endeavors of neurolinguistics is that of bridging the gap between the bare physiology of the brain and linguistic meaning. The aim of this chapter is to elucidate concepts that may help in bridging this gap, through the lens of brain complexity. Unlike several other chapters of this book, our effort is in analyzing complexity in the brain, and in particular, in brain circuits that support language, rather than in language itself, as an abstract entity. The account we offer for brain complexity in relation to language is presented in terms of self-organization, the general phenomenon of the gradual change of local parts of a system, that lead to their interactions become more functional with respect to their initial arrangement. Forms of neural self-organization appear to be essential in scaffolding representations of the external world within cortical areas, and mathematical formulations of self-organization at the level of the cerebral cortex will be described. We will present examples of models based on self-organization that reproduce specific aspects of the semantics of language. These models are examples of a research direction called “neurosemantics”, an enterprise focused on explaining the development of semantics by concentrating on the constituent neural processes.
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References
Aboitiz, F., Garcia, R. R., Bosman, C., & Brunetti, E. (2006). Cortical memory mechanisms and language origins. Brain and Language, 98, 40–56.
Azmitia, E. C., DeFelipe, J., Jones, E. G., Rakic, P., & Ribak, C. E. (Eds.). (2002). Changing views of Cajal’s Neuron. Amsterdam: Elsevier.
Baddeley, A. (1992). Working memory. Science, 255, 556–559.
Bates, E., Dal, P. S., & Thal, D. (1995). Individual differences and their implications for theories of language development. In P. Fletcher & B. M. Whinney (Eds.), Handbook of Child Language (pp. 96–151). Oxford: Basil Blackwell.
Bear, M., & Kirkwood, A. (1993). Neocortical long term potentiation. Current Opinion in Neurobiology, 3, 197–202.
Bechtel, W. (2014). Investigating neural representations: The tale of place cells. Synthese, 1–35.
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.
Bednar, J. A. (2014). Topographica. In D. Jaeger & R. Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 1–5). Berlin: Springer.
Bermúdez-Rattoni, F. (Ed.). (2007). Neural plasticity and memory: From genes to brain imaging. Boca Raton: CRC Press.
Black, A. W., & Taylor, P. A. (1997). The festival speech synthesis system: System documentation, Tech. Rep. HCRC/TR-83. Edinburgh: Human Communciation Research Centre, University of Edinburgh.
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.
Bloom, P. (2000). How children learn the meanings of words. Cambridge: MIT Press.
Bornstein, M. H., & RCote, L. (2004). Cross-linguistic analysis of vocabulary in young children: Spanish, dutch, french, hebrew, italian, korean, and american english. Child Development, 75, 1115–1139.
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.
Broad, C. D. (1925). The mind and its place. Nature. (Kegan Paul, London).
Bullock, T. H. (2002). Grades in neural complexity: How large is the span? Integrative and Comparative Biology, 42, 317–329.
Bullock, T. H. (2006). How do brains evolve complexity? An essay. International Journal of Psychophysiology, 60, 106–109.
Carey, S. (1978). The child as word learner. In M. Halle, J. Bresnan, & G. Miller (Eds.), Linguistic theory and psychological reality (pp. 264–293). Cambridge: MIT Press.
Carey, S., & Spelke, E. (1996). Science and core knowledge. Journal of Philosophy of Science, 63, 515–533.
Changizi, M.A. (2003), The Brain from 25,000 feet—high level explorations of brain complexity, perception, induction and vagueness. Springer: Berlin.
Chemero, A. (2009). Radical embodied cognitive science. Cambridge: MIT Press.
Chittka, L., & Niven, J. (2009). Are bigger brains better? Current Biology, 19, 995–1008.
Clayton, P., & Davies, P. (Eds.). (2006). The re-emergence of emergence: The emergentist hypothesis from science to religion. Oxford: Oxford University Press.
Craver, C. F. (2007). Explaining the brain: mechanisms and the mosaic unity of neuroscience. Oxford: Oxford University Press.
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. In Proceedings of the Natural Academy of Science USA (vol. 104, pp. 1383–1388).
Deco, G., & Rolls, E. (2004). A neurodynamical cortical model of visual attention and invariant object recognition. Vision Research, 44, 621–642.
Dickinson, D. K. (1984). «First impressions: Children’s knowledge of words gained from a single exposure», in. Applied Psycholinguistics, 5, 359–373.
Douglas, R. J., & Martin, K. A. (2004). Neuronal circuits of the neocortex. Annual Review of Neuroscience, 27, 419–451.
Douglas, R. J., Martin, K. A., & Whitteridge, D. (1989). A canonical microcircuit for neocortex. Neural Computation, 1, 480–488.
Dretske, F. I. (1986). Misrepresentation. In R. Bogdan (Ed.), Belief: Form, Content and Function. Oxford: Oxford University Press.
Ebbinghaus, H. (1885). Memory: A contribution to experimental psychology. New York: Dover.
Elman, J. L., Bates, E., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking innateness a connectionist perspective on development. Cambridge: MIT Press.
Felleman, D. J., & van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47.
Freedman, D. J., Riesenhuber, M., Poggio, T., & Miller, E. K. (2003). Visual categorization and the primate prefrontal cortex. Neurophysiology and Behavior. Journal of Neurophysiology, 88, 929–941.
Fresco, N. (2014). Physical computation and cognitive science. Berlin: Springer.
Fuster, J. M. (2001). The prefrontal cortex–an update: Time is of the essence. Neuron, 30, 319–333.
Fuster, J. M. (2008). The prefrontal cortex (4th ed.). New York: Academic Press.
Ganger, J., & Brent, M. R. (2004). Reexamining the vocabulary spurt. Developmental Psychology, 40, 621–632.
Gelder, T. V. (1995). What might cognition be, if not computation? Journal of Phylosophy, 91, 345–381.
Gentner, D. (1978). On relational meaning: The acquisition of verb meaning. Cognitive Development, 49, 988–998.
Gershkoff-Stowe, L., & Smith, L. B. (2004). Shape and the first hundred nouns. Child Development, 75, 1098–1114.
Gerstner, W. (1999). Spiking neurons. In W. Maass, & C. M. Bishop (Eds.), Pulsed Neural Networks. Cambridge: MIT Press.
Grassman, S., Stracke, M., & Tomasello, M. (2009). Two year olds exclude novel objects as potential referents of novel words based on pragmatics. Cognition, 112, 488–493.
Haken, H. (1978). Synergetics—An Introduction, Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry and Biology, 2nd edn. Berlin: Springer.
Hasker, W. (1999). The emergent self. Ithaca: Cornell University Press.
Hebb, D. O. (1949). The organization of behavior. New York: Wiley.
Herculano-Houzel, S. (2009). The human brain in numbers: A linearly scaled-up primate brain. Frontiers in Human Neuroscience, 3(Article 31).
Herculano-Houzel, S. (2012). The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost. In Proceedings of the Natural Academy of Science USA (vol. 109, pp. 10.661–10.668).
Herculano-Houzel, S., de Souza, K. A., Neves, K., Porfirio, J., Messeder, D., & Feijó, L. M., et al. (2014). The elephant brain in numbers. Frontiers in Nauroanatomy, 8(Article 46).
Heyes, C. (2012). Simple minds: A qualified defence of associative learning. Philosophical Transactions of the Royal Society B, 367, 2695–703.
Hines, M., & Carnevale, N. (1997). The NEURON simulation environment. Neural Computation, 9, 1179–1209.
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.
Hooker, C. (Ed.). (2010). Philosophy of complex systems. Handbook of the Philosophy of Science (vol. 10). North Holland, Amsterdam.
Horton, J. C., & Adams, D. L. (2005). The cortical column: A structure without a function. Philosophical transactions of the Royal Society B, 360, 837–862.
Hubel, D., & Wiesel, T. (1959). Receptive fields of single neurones in the cat’s striate cortex. Journal of Physiology, 148, 574–591.
Hubel, D., & Wiesel, T. (1968). Receptive fields and functional architecture of mokey striate cortex. Journal of Physiology, 195, 215–243.
Huey, E. D., Krueger, F., & Grafman, J. (2006). Representations in the human prefrontal cortex. Current Directions in Psychological Science, 15, 167–171.
Hunt, J. J., Bosking, W. H., & Goodhill, G. J. (2011). Statistical structure of lateral connections in the primary visual cortex. Neural Systems and Circuits, 1, 1–12.
Hutto, D. D., & Myin, E. (2013). Radicalizing enactivism: Basic minds without content. Cambridge: MIT Press.
Ito, M. (1989). Long-term depression. Annual Review of Neuroscience, 12, 85–102.
Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14, 1569–1572.
Kaas, J. H., Gharbawie, O. A., & Stepniewska, I. (2011). The organization and evolution of dorsal stream multisensory motor pathways in primates. Frontiers in Nauroanatomy, 5, 34.
Kashimori, Y., Ichinose, Y., & Fujita, K. (2007). A functional role of interaction between IT cortex and PF cortex in visual categorization task. Neurocomputing, 70, 1813–1818.
Katz, L., & Shatz, C. (1996). Synaptic activity and the construction of cortical circuits. Science, 274, 1133–1138.
Khaligh-Razavi, S. M., & Kriegeskorte, N. (2014). Deep supervised, but not unsupervised, models may explain it cortical representation. PLoS Computational Biology, 10(e1003), 915.
Kim, J. (2006). Emergence: Core ideas and issues. Synthese, 151, 547–559.
Kohonen, T. (1982). Self-organizing formation of topologically correct feature maps. Biological Cybernetics, 43, 59–69.
Kohonen, T. (1984). Self-organization and associative memory. Berlin: Springer.
Kohonen, T. (1995). Self-organizing maps. Berlin: Springer.
Kohonen, T., & Hari, R. (2000). Where the abstract feature maps of the brain might come from. Trends in Neurosciences, 22, 135–139.
Kripke, S. A. (1972). Naming and necessity. In D. Davidson & G. H. Harman (Eds.), Semantics of natural language (pp. 253–355). Dordrecht: Reidel Publishing Company.
Krubitzer, L. (1995). The organization of neocortex in mammals: are species differences really so different? Neuroscience, 8, 408–417.
Levy, W., & Steward, O. (1983). Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience, 8, 791–797.
Lifter, K., & Bloom, L. (1989). Object knowledge and the emergence of language. Infant Behavior and Development, 12, 395–423.
Linde, Y., Buzo, A., & Gray, R. (1980). An algorithm for vector quantizer design. IEEE Transactions on Communications, 28, 84–95.
London, M., & Häusser, M. (2005). Dendritic computation. Annual Review of Neuroscience, 28, 503–5032.
Maçarico da Costa, N., & Martin, K. A. C. (2010). Whose cortical column would that be? Frontiers in Nauroanatomy, 4, 16.
MacWhinney, B., & O’Grady, W. (Eds.). (2015). The handbook of language emergence. New York: Wiley.
Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsyn, aptic APs and EPSPs. Science, 275, 213–215.
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.
Mayor, J., & Plunkett, K. (2010). A neurocomputational account of taxonomic responding and fast mapping in early word learning. Psychological Review, 117, 1–31.
Miller, E. K., Freedman, D. J., & Wallis, J. D. (2002). The prefrontal cortex: Categories, concepts and cognition. Philosophical Transactions: Biological Sciences, 357, 1123–1136.
Mintz, T. H., & Gleitman, L. R. (2002). Adjectives really do modify nouns: the incremental and restricted nature of early adjective acquisition. Cognition, 84, 267–293.
Møller, A. R. (Ed.). (2006). Neural plasticity and disorders of the nervous system. Cambridge: Cambridge University Press.
Moroz, L. L. (2009). On the independent origins of complex brains and neurons. Brain, Behavior and Evolution, 74, 177–190.
Mountcastle, V. (1957). Modality and topographic properties of single neurons in cats somatic sensory cortex. Journal of Neurophysiology, 20, 408–434.
Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289–316.
Näger, C., Storck, J., & Deco, G. (2002). Speech recognition with spiking neurons and dynamic synapses: A model motivated by the human auditory pathway. Neurocomputing, 44–46, 937–942.
Nair-Collins, M. (2013). Representation in biological systems: Teleofunction, etiology, and structural preservation. In L. Swan (Ed.), Origins of Mind (pp. 161–185). New York: Academic Press.
Nayar, S., & Murase, H. (1995). Visual learning and recognition of 3-D object by appearence. International Journal of Computer Vision, 14, 5–24.
Neher, E., & Sakmann, B. (1976). Noise analysis of drug induced voltage clamp currents in denervated frog muscle fibers. Journal of Physiology, 258, 705–729.
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.
Pereira, A. F., Smith, L. B., & Yu, C. (2014). A bottom-up view of toddler word learning. Psychonomic Bulletin & Review, 2, 178–185.
Piccinini, G. (2015). Physical computation: A mechanistic account. Oxford: Oxford University Press.
Plebe, A. (2007a). A model of angle selectivity development in visual area V2. Neurocomputing, 70, 2060–2066.
Plebe, A. (2007b). A neural model of object naming. Enformatika, 2, 130–135.
Plebe, A. (2012). A model of the response of visual area V2 to combinations of orientations. Network: Computation in Neural Systems, 23, 105–122.
Plebe, A., & Anile, M. (2001). A neural-network-based approach to the double traveling salesman problem. Neural Computation, 14(2), 437–471.
Plebe, A., & De La Cruz, V. M. (2016). Neurosemantics—neural processes and the construction of language meaning. Berlin: Springer.
Plebe, A., & Domenella, R. G. (2005). The emergence of visual object recognition. In W. Duch, J. Kacprzyk, E. Oja, & S. Zadrony (Eds.), Artificial Neural Networks—ICANN 2005 15th International Conference, Warsaw (pp. 507–512). Berlin: Springer.
Plebe, A., & Domenella, R. G. (2006). Early development of visual recognition. BioSystems, 86, 63–74.
Plebe, A., & Domenella, R. G. (2007). Object recognition by artificial cortical maps. Neural Networks, 20, 763–780.
Plebe, A., De La Cruz, V. M., & Mazzone, M. (2007). Artificial learners of objects and names. In Y. Demiris, B. Scassellati, & D. Mareschal (Eds.), Proceedings of the 6th International Conference on Development and Learning (pp. 300–305). IEEE.
Plebe, A., Mazzone, M., & De La Cruz, V. M. (2010). First words learning: A cortical model. Cognitive Computation, 2, 217–229.
Plebe, A., Mazzone, M., & De La Cruz, V. M. (2011). A biologically inspired neural model of vision-language integration. Neural Network World, 21, 227–249.
Plebe, A., De La Cruz, V. M., & Mazzone, M. (2013). In learning nouns and adjectives remembering matters: A cortical model. In A. Villavincencio, T. Poibeau, A. Korhonen, & A. Alishahi (Eds.), Cognitive Aspects of Computational Language Acquisition (pp. 105–129). Berlin: Springer.
Plunkett, K. (1993). Lexical segmentation and vocabulary growth in early language acquisition. Journal of Child Language, 20, 43–60.
Prigogine, I. (1961). Introduction to thermodynamics of irreversible processes. New York: Interscience.
Putnam, H. (1975). The meaning of “meaning”. In Mind, Language and Reality (vol. 2). Cambridge: MIT Press.
Rakic, P. (2008). Confusing cortical columns. In Proceedings of the Natural Academy of Science USA (vol. 34, pp. 12.099–12.100).
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. Berlin: Springer.
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.
Ramsey, W. M. (2007). Representation reconsidered. Cambridge: Cambridge University Press.
Regier, T. (2005). The emergence of words: Attentional learning in form and meaning. Cognitive Science, 29, 819–865.
Ritter, H., Martinetz, T., & Schulten, K. (1992). Neural computation and self-organizing maps. Reading: Addison Wesley.
Rogers, T. T., & McClelland, J. L. (2006). Semantic cognition—a parallel distributed processing approach. Cambridge: MIT Press.
Rolls, E. T., & Stringer, S. M. (2006). Invariant visual object recognition: A model, with lighting invariance. Journal of Physiology Paris, 100, 43–62.
Roth, G., & Dicke, U. (2013). Evolution of nervous systems and brains. In G. Galizia & P. M. Lledo (Eds.), Neurosciences—from molecule to behavior (pp. 19–45). Berlin: Springer.
Ruelle, D., & Takens, F. (1971). On the nature of turbulence. Communications in Mathematical Physics, 20, 167–192.
Rumelhart, D. E., & McClelland, J. L. (Eds.). (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge: MIT Press.
Ryder, D. (2009a). Problems of representation I: nature and role. In Symons and Calvo (Eds.), (pp. 233–250).
Ryder, D. (2009b). Problems of representation II: Naturalizing content. In Symons and Calvo (Eds.), (pp. 251–279).
Sandhofer, C. M., & Smith, L. B. (2001). Why children learn color and size words so differently: Evidence from adults’ learning of artificial terms. Journal of Experimental Psychology, 130, 600–620.
Scott, A. (Ed.). (2004). Encyclopedia of nonlinear science. London: Routledge.
Seeley, T. D. (2003). What studies of communication have revealed about the minds of worker honey bees. In T. Kikuchi, N. Azuma, & S. Higashi (Eds.), Genes, behaviors and evolution of social insects (pp. 21–33). Sapporo: Hokkaido University Press.
Shagrir, O. (2012). Structural representations and the brain. British Journal for the Philosophy of Science, 63, 519–545.
Shanks, D. R. (1995). The psychology of associative learning. Cambridge: Cambridge University Press.
Shepherd, G. M. (1988). A basic circuit for cortical organization. In M. S. Gazzaniga (Ed.), Perspectives on Memory Research (pp. 93–134). Cambridge: MIT Press.
Shumway, C. A. (2010). The evolution of complex brains and behaviors in african cichlid fishes. Current Zoology, 56, 144–156.
Sidiropoulou, K., Pissadaki, E. K., & Poirazi, P. (2006). Inside the brain of a neuron. EMBO Reports, 7, 886–892.
Silberstein, M. (2006). In defence of ontological emergence and mental causation. In Clayton and Davies (Eds.), (pp. 203–226).
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.
Smith, L. B. (2001). How domain-general processes may create domain-specific biases. In M. Bowerman, & S. Levinson (Eds.), Language Acquisition and Conceptual Development. Cambridge: Cambridge University Press.
Sprevak, M. (2011). William m. ramsey, representation reconsidered. British Journal for the Philosophy of Science, 62, 669–675.
Striedter, G. F. (2003). Principles of brain evolution. Sunderland: Sinauer Associated.
Symons, J., & Calvo, P. (Eds.). (2009). The Routledge Companion to Philosophy of Psychology. London: Routledge.
Taylor, N. R., Hartley, M., & Taylor, J. G. (2005). Coding of objects in low-level visual cortical areas. In W. Duch, J. Kacprzyk, E. Oja, & S. Zadrony (Eds.), Artificial Neural Networks, ICANN ’05, 15th International Conference Proceedings (pp. 57–63). Berlin: Springer.
Thivierge, J. P., & Marcus, G. F. (2007). The topographic brain: From neural connectivity to cognition. Trends in Neuroscience, 30, 251–259.
Thompson, I. (1997). Cortical development: A role for spontaneous activity? Current Biology, 7, 324–326.
Thorndike, E. (1892). Animal intelligence: An experimental study of the associative processes. Animals Psychological Monographs, 2, 192–205.
Tomasello, M. (1999). The cultural origins of human cognition. Cambridge: Harvard University Press.
Tomasello, M. (2003). Constructing a language: A usage-based theory of language acquisition. Cambridge: Harvard University Press.
Turrigiano, G. G., & Nelson, S. B. (2004). Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience, 391, 892–896.
Vallar, G., & Shallice, T. (Eds.). (2007). Neuropsychological Impairments of Short-Term Memory. Cambridge: Cambridge University Press.
Vanduffel, W., Tootell, R. B., Schoups, A. A., & Orban, G. A. (2002). The organization of orientation selectivity throughout the macaque visual cortex. Cerebral Cortex, 12, 647–662.
Verkindt, C., Bertrand, O., Echallier, F., & Pernier, J. (1995). Tonotopic organization of the human auditory cortex: N100 topography and multiple dipole model analysis. Electroencephalography and Clinical Neurophisiology, 96, 143–156.
Volkmer, M. (2004). A pulsed neural network model of spectro-temporal receptive fileds and population coding in auditory cortex. Neural Computing, 3, 177–193.
von der Malsburg, C. (1973). Self-organization of orientation sensitive cells in the striate cortex. Kybernetic, 14, 85–100.
von der Malsburg, C. (1995). 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 (pp. 447–462) (2nd Edn.). New York: Academic Press.
Wallis, G., & Rolls, E. (1997). Invariant face and object recognition in the visual system. Progress in Neurobiology, 51, 167–194.
Wiesel, T., & Hubel, D. (1965). Binocular interaction in striate cortex of kittens reared with artificial squint. Journal of Neurophysiology, 28, 1041–1059.
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.
Yu, C., & Smith, L. B. (2012). Embodied attention and word learning by toddlers. Cognition, 125, 244–262.
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.
Zakon, H. H. (2012). Adaptive evolution of voltage-gated sodium channels: The first 800 million years. In Proceedings of the Natural Academy of Science USA, 109, 10.619–10.625.
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Plebe, A., De La Cruz, V.M. (2017). Language and Brain Complexity. In: La Mantia, F., Licata, I., Perconti, P. (eds) Language in Complexity. Lecture Notes in Morphogenesis. Springer, Cham. https://doi.org/10.1007/978-3-319-29483-4_10
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