Taal tussen de Oren: Op weg naar een Cybernetische Taalkunde
‘Hoe ontstaat een zin in je hoofd?’ Het lijkt vanzelfsprekend, maar het antwoord is allesbehalve eenvoudig. Taal is meer dan woorden op een rij; het is een spel van ideeën, patronen en complexe verbindingen in ons brein. Hoe die patronen ontstaan, hoe ze samenwerken en hoe ze betekenis vormen, is nog grotendeels een raadsel. In dit artikel neem ik je mee op een ontdekkingstocht van symbolen naar patronen, en misschien zelfs tot een nieuwe manier om taal te begrijpen.
Wat maakt taal menselijk?
Taalkundigen wijzen vaak op ons uniek vermogen om steeds opnieuw ideeën samen te voegen en verder uit te bouwen. Eén gedachte kan worden gekoppeld aan een ander, en dat resultaat kan dan weer uitgebreid worden tot een nog groter geheel. Zo kunnen we eindeloos nieuwe betekenissen creëren – iets waar andere primaten niet toe in staat zijn.
Vele onderzoekers hebben geprobeerd die hiërarchie en opbouw terug te vinden in het brein. Vaak vertrokken ze vanuit een cognitief perspectief: ze zochten naar functies, naar hersengebieden die daarvoor verantwoordelijk zouden zijn, en probeerden zo een top-down verband te leggen met taal.
Mijn thesis koos een andere weg. Ik begon niet bij functies, maar bij de kleinste bouwsteen: de individuele zenuwcel. Van daaruit onderzocht ik of taalkundige eigenschappen opnieuw konden worden geformuleerd in de taal van de wiskundige neurobiologie.
“Het doel? Een nieuwe manier om taal en brein te verbinden.”
Hiervoor bouwde ik een model dat vorm en werking van neuronen koppelt aan taaldynamieken.
Neuronen en syntaxis
Informatie stroomt razendsnel door het brein. Een neuron ontvangt signalen, filtert die, en geeft ze door naar de volgende. Dat doorgeven wordt ook het ‘vuren’ van neuronen genoemd. Wanneer grote groepen neuronen tegelijk vuren, ontstaan er patronen in hun activiteit. Zulke dynamieken vormen de basis voor hogere functies, zoals taal. Biofysische modellen proberen die dynamieken te vatten in formules, om ze zo beter te begrijpen.
Eén van die hogere functies is het verwerken en produceren van taal, dat uiteenvalt in verschillende domeinen. Een belangrijk domein is de syntaxis: de studie van hoe woorden samen een zin vormen. Elk woord heeft zijn eigen kenmerken, en het is de taak van de syntacticus die te beschrijven en te begrijpen. Vaak blijft de taalkunde hierbij echter hangen in symbolische systemen: een woord behoort tot een categorie A of B, zonder tussenvorm. De biofysica, daarentegen, spreek de taal van getallen en vloeiende overgangen: een waarde kan bijvoorbeeld 1,4 zijn, wat ergens tussen 1 en 2 ligt.
“Maar hoe laat je twee velden met elkaar praten, als ze niet dezelfde taal spreken?”
In mijn thesis probeerde ik een eerste brug te slaan: symbolische kenmerken uit de taalkunde omzetten in getallen. Soms komt dat neer op afronden: 1,4 ligt dichter bij 1 dan bij 2, en valt dus in categorie A. Op die manier onderzocht ik hoe zulke classificatiesystemen kunnen werken en waar de raakvlakken liggen tussen taalkunde en biofysica.
Van woorden naar neuronen
Het idee is dat de kleinste bouwstenen van taal – de ‘elementaire deeltjes’ van de taalkunde – kunnen begrepen worden door te kijken naar de vorm en dynamieken van neuronen. In plaats van woorden en zinsdelen alleen als symbolen te zien, kun je ze hervertalen naar patronen van neuronale activiteit. Wanneer we woorden samenbrengen in een zin, komt dat overeen met het optellen en combineren van patronen over een bepaalde tijd. Zo ontstaat een manier om taal te beschrijven in termen die ook voor de neurobiologie betekenisvol zijn.
Neem, bijvoorbeeld, de zin ‘de kat slaapt’. Voor de taalkundige bestaat dit uit drie woorden, elk met hun eigen categorie: een lidwoord (de), een zelfstandig naamwoord (kat), en een werkwoord (slaapt). Voor de neurobioloog gaat het om drie groepen neuronen die elk op hun manier actief zijn. Wanneer die groepen tegelijk vuren en hun signalen bundelen, ontstaat er een patroon dat overeenkomt met de hele zin. Zo kun je taal zien als een optelsom van hersenactiviteit in de tijd, in plaats van enkel een rij symbolen op papier.
Klinisch relevant?
Een spannende uitbreiding van dit model is dat het ook iets kan zeggen over taalstoornissen. Als taalelementen inderdaad terug te voeren zijn op de vorm en dynamiek van neuronen, dan kan een verandering in die neuronen ook doorwerken in taal. Dat idee sluit aan bij wat we al zien in de praktijk. Neem bijvoorbeeld afasie: mensen met deze aandoening hebben moeite om woorden en zinnen samen te stellen, en kunnen vaak nog maar beperkt communiceren. Met een biofysisch model kun je zulke taalproblemen simuleren: door de parameters van neuronen te veranderen, kun je onderzoeken hoe dat het taalvermogen beïnvloedt. Zo zou het in de toekomst mogelijk worden om taalstoornissen beter te begrijpen, te voorspellen en misschien zelfs gerichter te behandelen.
Conclusie
“Misschien moeten we de taalkunde daarom opnieuw leren spreken: niet langer alleen in symbolen, maar ook in getallen en patronen van activiteit.”
Deze thesis was een zoektocht naar een nieuwe manier om taal en brein met elkaar te verbinden. In plaats van taal te zien als iets dat uit losse hersengebieden komt, zoomde ik in op de kleinste spelers: de zenuwcellen, die met hun ritmes en patronen samen het samenspel van taal vormen. Zo ontstaat een ander perspectief: taalkunde niet alleen als een spel van symbolen, maar als een dynamisch systeem dat in het brein tot leven komt.
En dat is precies waar dit artikel voor pleit: Taal tussen de Oren – een eerste stap naar een cybernetische taalkunde, waarin taal niet alleen wordt bestudeerd, maar ook écht wordt doorgrond.
Bibliografie
Acquaviva, P. (2009). Roots and Lexicality in Distributed Morphology. York-Essex Morphology Meeting 5: Special Issue of York Working Papers in Linguistics, 1–21.
Akman, T. E., & Kullmann, D. M. (2010). Oscillations and Filtering Networks Support Flexible Routing of Information. Neuron, 67, 308–320.
Akman, T. E., & Kullmann, D. M. (2012). Efficient ‘Communication through Coherence’ Requires Oscillations Structured to Minimize Interference Between Signals. PLOS Computational Biology, 8, 1–15.
Akman, T. E., & Kullmann, D. M. (2014). Oscillatory Multiplexing of Population Codes for Selective Communication in the Mammalian Brain. Nature Reviews Neuroscience, 15, 111–122.
Albertini, Tettamanti, M., & Moro, A. (2015). Sintassi e working memory: Un nuovo paradigma di valutazione. Sistemi Intelligenti, 27, 27–44.
Alexiadou, A., & Anagnostopoulou, E. (2004). Voice Morphology in the Causative-inchoative Alternation: Evidence for a Non-Unified Structural Analysis of Unaccusatives. In: A. Alexiadou, E. Anagnostopoulou, & M. B. H. Everaert (Edd.), The Unaccusativity Puzzle. Oxford: Oxford University Press, 114–136.
Amaral, D. G., Lavenex, P., & Insausti, R. (2024). Hippocampal Neuroanatomy. In: R. Morris, D. G. Amaral, T. Bliss, K. Duff, & J. O’Keefe (Edd.), The Hippocampus Book. Oxford: Oxford University Press, 37–120.
Aru, J., Aru, J., Priesemann, V., Wibral, M., Lana, L., Pipa, G., Singer, W., Vincente, R. (2015). Untangling Cross-Frequency Coupling in Neuroscience. Current Opinion in Neurobiology, 31, 51–61.
Axmacher, N., Henseler, M. M., Jensen, O., Weinreich, I., Elger, C. E., & Fell, J. (2010). Cross-Frequency Coupling Supports Multi-Item Working Memory in the Human Hippocampus. PNAS, 107: 7, 3228–3233.
Azouz, R., & Gray, C. (2000). Dynamic Spike Threshold Reveals a Mechanism for Synaptic Coincidence Detection in Cortical Neurons in Vivo. PNAS, 97, 8110–8115.
Baker, M. C. (2008). The Macroparameter in a Microparameter World. In: T. Biberauer (Ed.), The Limits of Syntactic Variation. Amsterdam: John Benjamins Publishing Company, 351–373.
Barbieri, F., & Brunel, N. (2008). Can Attractor Network Models Account for the Statistics of Firing During Persistent Activity in the Prefrontal Cortex? Frontiers in Neuroscience, 2, 114–122.
Baroni, M., & Lenci, A. (2010). Distributional Memory: A General Framework for Corpus-Based Semantics. Computational Linguistics, 36, 673–721.
Barsalou, L. W. (2003). Abstraction in Perceptual Symbol Systems. Philosophical Transactions of the Royal Society B: Biological Sciences, 385, 1177–1187.
Barsalou, L. W. (2017). What does Semantic Tiling of the Cortex tell us about Semantics? Neuropsychologia, 105, 18–38.
Baunaz, L., & Lander, E. (2018). Nanosyntax: The Basics. In: L. Baunaz, K. De Clercq, L. Haegeman, & E. Lander (Edd.), Exploring Nanosyntax. Oxford: Oxford University Press, 3–56.
Beiran, M., Dubreuil, A., Valente, A., Mastrogiuseppe, F., & Ostojic, S. (2021). Shaping Dynamics with Multiple Populations in Low-Rank Recurrent Networks. Neural Computation, 33, 1572–1615.
Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A Neural Probabilistic Language Model. Journal of Machine Learning Research, 3, 1137–1155.
Bernardi, S., Benna, M. K., Rigotti, M., Munuera, J., Fusi, S., & Salzman, D. C. (2020). The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex. Cell, 183, 954–967.
Berwick, R. C., & Chomsky, N. (2016). Why Only us? Cambridge, Massachusetts: MIT Press.
Berwick, R. C., Friederici, A. D., Chomsky, N., & Bolhuis, J. J. (2013). Evolution, Brain, and the Nature of Language. Trends in Cognitive Sciences, 17, 89–98.
Biberauer, T. (2019). Factors 2 and 3: Towards a principled approach. Catalan Journal of Linguistic Special Issue, 45–88.
Biberauer, T., & Roberts, I. (2010). Comments on Jäger ‘Anything is Nothing is Something’: On the Diachrony of Polarity Types of Indefinites. Natural Language & Linguistic Theory, 28, 823–836.
Biberauer, T., & Zeijlstra, H. (2012). Negative Concord in Afrikaans: Filling a Typological Gap. Journal of Semantics, 29, 345–371.
Bick, C., Goodfellow, I., Laing, C. R., & Martens, E. A. (2020). Understanding the Dynamics of Biological and Neural Oscillator Networks Through Exact Mean-Field Reductions: A Review. Journal of Mathematical Neuroscience, 10, 9, 1–43.
Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009). Where is the Semantic System? A Critical Review and Meta-Analysis of 120 Functional Neuroimaging Studies. Cerebral Cortex, 19, 2767–2769.
Bjorkman, B. M., & Zeijlstra, H. (2014). Upward Agree is Superior. Ms., University of Toronto and University of Göttingen.
Bjorkman, B. M., & Zeijlstra, H. (2019). Checking up on (Phi-)Agreement. Linguistic Inquiry, 50, 527–569.
Bobaljik, J. D. (2008). Where’s Phi? Agreement as a Postsyntactic Operation. In: D. Harbour, D. Adger, & S. Béjar (Edd.), Phi Theory: Phi-Features across Modules and Interfaces. Oxford: Oxford University Press, 295–328.
Boeckx, C. (2013). Merge: Biolinguistic Considerations. English Linguistics, 30, 463–484.
Boleda, G. (2020). Distributional Semantics and Linguistic Theory. Annual Review of Linguistics, 6, 213–234.
Bolhuis, J. J., Crain, S., Fong, S., & Moro, A. (2024). Three Reasons Why AI doesn’t Model Human Language. Nature, 627, 489.
Bonnabel, S. (2013). Stochastic Gradient Descent on Riemannian Manifolds. IEEE Transactions on Automatic Control, 58, 2217–2229.
Borer, H. (2005). Structuring Sense, Vol. 1: In Name Only. Oxford: Oxford University Press.
Borer, H. (2013). Structuring Sense, Vol. 3: Taking Form. Oxford: Oxford University Press.
Börgers, C., Epstein, S., & Kopell, N. J. (2008). Gamma Oscillations Mediate Stimulus Competition and Attentional Selection in a Cortical Network Model. PNAS, 105, 18023–18028.
Bowers, J. S. (2009). On the Biological Plausibility of Grandmother Cells: Implications for Neural Network Theories in Psychology and Neuroscience. Psychological Review, 116, 220–251.
Breakspear, M. (2017). Dynamic Models of Large-Scale Brain Activity. Nature Neuroscience, 20, 340–352.
Bruening, B. (2018). The Lexicalist Hypothesis: Both Wrong and Superfluous. Language, 94, 1–42.
Bruni, E., Tran, N. K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47.
Buschman, T. J., & Miller, E. K. (2008). Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices. Science Reports, 315, 1860–1862.
Buzsáki, G. (2006). Rhythms of the Brain. Oxford: Oxford University Press.
Buzsáki, G. (2010). Neural Syntax: Cell Assemblies, Synapsembles, and Readers. Neuron, 68, 362–385.
Buzsáki, G., & Draguhn, A. (2004). Neuronal Oscillations in Cortical Networks. Science, 304, 1926–1929.
Buzsáki, G., & Wang, X.-J. (2012). Mechanisms of Gamma Oscillations. Annual Review of Neuroscience, 35, 203–225.
Caha, P. (To Appear). Minimalism and Nanosyntax: Reconciling Late Insertion and the Borer-Chomsky Conjecture. In: The Cambridge Handbook of Minimalism.
Callens, M. (2024). On Cyclic Domains and the Archetypal Phasal Head. University of Ghent: Master thesis.
Canolty, R. T., & Knight, R. T. (2010). The Functional Role of Cross-Frequency Coupling. Trends in Cognitive Sciences, 14: 11, 506–515.
Caucheteux, C., & King, J.-R. (2020). Language Processing in Brains and Deep Neural Networks: Computational Convergence and its Limits. BioRxiv, 1–13.
Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A., & Fiete, I. (2019). The Intrinsic Attractor Manifold and Population Dynamics of a Canonical Cognitive Circuit across Waking and Sleep. Nature Neuroscience, 22, 1512–1520.
Chomsky, N. (1993). A Minimalist Program for Linguistic Theory. In: K. Hale & S. J. Keyser (Edd.), The View from Building 20: Essays in Linguistics in Honor of Sylvain Bromberger. Cambridge, Massachusetts: MIT Press, 1–52.
Chomsky, N. (1995). The Minimalist Program. Cambridge, Massachusetts: MIT Press.
Chomsky, N. (2000). Minimalist Inquiries: The Framework. In: R. Martin, D. Michaels, J. Uriagereka, & S. J. Keyser (Edd.), Step by Step: Essays on Minimalis Syntax in Honor of Howard Lasnik. Cambridge, Massachusetts: MIT Press, 89–155.
Chomsky, N. (2001). Derivation by Phase. In: M. Kenstowicz (Ed.), Ken Hale: A Life in Language. Cambridge, Massachusetts: MIT Press, 1–52.
Chomsky, N. (2004). Beyond Explanatory Adequacy. In: A. Belletti (Ed.), Structures and Beyond: The Cartography of Syntactic Structures, Vol. 3: Restructuring and Functional Heads. Oxford: Oxford University Press, 104–131.
Chomsky, N. (2005). Three Factors in Language Design. Linguistic Inquiry, 36, 1–22.
Chomsky, N. (2007). Approaching UG from Below. In: U. Sauerland & H.-M. Gärtner (Edd.), Interfaces + Recursion = Language? Chomsky’s Minimalism and the View from Syntax-Semantics. Berlin: Mouton de Gruyter, 1–30.
Chomsky, N. (2008). On Phases. In: R. Freidin, C. P. Otero, & M. L. Zubizarreta (Edd.), Foundational Issues in Linguistic Theory: Essays in Honor of Jean-Roger Vergnaud. Cambridge, Massachusetts: MIT Press, 133–166.
Chomsky, N. (2019a). Some Puzzling Foundational Issues: The Reading Program. Catalan Journal of Linguistic Special Issue, 263–285.
Chomsky, N. (2019b). The UCLA Lectures. Retrieved from https://lingbuzz.net/lingbuzz/005485
Chomsky, N. (2021a). Linguistics Then and Now: Some Personal Reflections. Annual Review of Linguistics, 7, 1–11.
Chomsky, N. (2021b). Minimalism: Where are we Now, and Where can we Hope to Go. Gengo Kenkyu, 160, 1–41.
Chomsky, N., Gallego, Á. J., & Ott, D. (2019). Generative Grammar and the Faculty of Language: Insights, Questions, and Challenges. Catalan Journal of Linguistic Special Issue, 229–261.
Chomsky, N., & Lasnik, H. (1991). Principles and Parameters Theory. In: J. Jacobs, A. von Stechow, W. Sternefeld, & T. Venneman (Edd.), Syntax: An International Handbook of Contemporary Research. Berlin: De Gruyter, 506–569.
Chomsky, N., & Moro, A. (2022). The Secrets of Words. Cambridge, Massachusetts: MIT Press.
Chomsky, N., Seely, T. D., Berwick, R. C., Fong, S., Huybregts, M. A. C., Kitahara, H., McInnerney, A., Sugimoto, Y. (2023). Merge and the Strong Minimalist Thesis. Cambridge: Cambridge University Press.
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012). Neural Population Dynamics during Reaching. Nature, 487, 51–56.
Churchland, P. M. (1993). State-Space Semantics and Meaning Holism. Philosophy and Phenomenological Research, 53, 667–672.
Colgin, L. L., Denninger, T., Fyhn, M., Hafting, T., Bonnevie, T., Jensen, O., Moser, M.-B., Moser, E. I. (2009). Frequency of Gamma Oscillations Routes Flow of Information in the Hippocampus. Nature Letters, 462, 353–358.
Connell, L., & Lynott, D. (2014). Principles of Representation: Why You Can’t Represent the Same Concept Twice. Topics in Cognitive Science, 6, 390–406.
Corver, N., & Nunes, J. (Edd.). (2007). The Copy Theory of Movement. Amsterdam: John Benjamins Publishing Company.
Csicsvari, J., Jamieson, B., Wise, K. D., & Buzsáki, G. (2003). Mechanisms of Gamma Oscillations in the Hippocampus of the Behaving Rat. Neuron, 37, 311–322.
Cumin, D., & Unsworth, C. P. (2007). Generalising the Kuramoto Model for the Study of Neuronal Sychronization in the Brain. Physica D., 226, 181–196.
Cunningham, J. P., & Yu, B. M. (2014). Dimensionality Reduction for Large-Scale Neural Recordings. Nature Neuroscience, 17, 1500–1509.
Daido, H. (1996). Onset of Cooperative Entrainment in Limit-Cycle Oscillators with Uniform-All-to-All Interactions: Bifurcation of the Order Function. Physica D, 91, 24–66.
Damasio, H., Grabowski, T. J., Tranel, D., Hichwa, R. D., & Damasio, A. R. (1996). A Neural Basis for Lexical Retrieval. Nature,380, 499–505.
De Belder, M., & van Craenenbroeck, J. (2015). How to Merge a Root. Linguistic Inquiry, 46, 625–655.
De Clercq, K. (2017). The Nanosyntax of French Negation: A Diachronic Perspective. In: S. Cruschina, K. Hartmann, & E.-M. Remberger (Edd.), Negation: Syntax, Semantics, and Variation. Vienna: Vienna University Press, 49–80.
De Clercq, K. (2019). French Negation and the Superset Principle. In: M. Bouzouita, A. Breithbarth, L. Danckaert, & E. Witzenhausen (Edd.), Cycles in Language Change. Oxford: Oxford University Press, 199–227.
Deal, A. R. (2015). Interaction and Satisfaction in φ-agreement. In: T. Bui & D. Ozyildiz (Edd.), NELS 45: Proceedings of the Forty-Fifth Annual Meeting of the North East Linguistic Society (Vol. 1). Amherst: GLSA, 179–192.
Deneve, S., Latham, P. E., & Pouget, A. (1999). Reading Population Codes: A Neural Implementation of Ideal Observers. Nature Neuroscience, 2, 740–745.
DeVito, L. M., Kanter, B. R., & Eichenbaum, H. (2010). The Hippocampus Contributes to Memory Expression during Transitive Inference in Mice. Hippocampus, 20, 208–217.
Diedrichsen, J. (2020). Representational Models and the Feature Fallacy. In: D. Poeppel, G. R. Mangun, & M. S. Gazzaniga (Edd.), The Cognitive Neurosciences (6th ed.). Cambridge, Massachusetts: MIT Press, 669–678.
Dubreuil, A., Valente, A., Beiran, M., Mastrogiuseppe, F., & Ostojic, S. (2022). The Role of Population Structure in Computations through Neural Dynamics. Nature Neuroscience, 25, 783–794.
Egorov, A., Hamam, B. N., Fransén, E., Hasselmo, M., & Alonso, A. A. (2002). Graded Persistent Activity in Entorhinal Cortex Neurons. Nature, 420, 173–178.
Eichenbaum, H. (2014). Time Cells in the Hippocampus: A New Dimension for Mapping Memories. Nature Reviews Neuroscience, 15, 732–744.
Embick, D. (2000). Features, Syntax, and Categories in the Latin Perfect. Linguistic Inquiry, 31, 185–230.
Embick, D. (2015). The Morpheme: A Theoretical Introduction. Berlin: De Gruyter Mouton.
Embick, D., & Noyer, R. (1999). Locality in Post-Syntactic Operations. MIT Working Papers in Linguistics, 34, 265–317.
Embick, D., & Noyer, R. (2001). Movement Operations After Syntax. Linguistic Inquiry, 32, 555–595.
Epstein, S. D. (2007). On I(nternalist)-Functional Explanation in Minimalism. Linguistic Analysis: Dynamic Interfaces, 33, 20–53.
Epstein, S. D., Kitahara, H., & Seely, T. D. (2010). Uninterpretable Features: What are they and What do they do? In: M. T. Putnam (Ed.), Exploring Crash-Proof Grammars. Amsterdam: John Benjamins Publishing Company, 125–142.
Epstein, S. D., Kitahara, H., & Seely, T. D. (2012). Structure Building that Can’t Be. In: M. Uribe-Etxebarria & V. Valmala (Edd.), Ways of Structure Building. Oxford: Oxford University Press, 253–270.
Epstein, S. D., Kitahara, H., & Seely, T. D. (2022). Some Concepts and Consequences of 3rd Factor-Compliant Simplest MERGE. In: S. D. Epstein, H. Kitahara, & T. D. Seely (Edd.), A Minimalist Theory of Simplest Merge. New York: Routledge, 116–139.
Epstein, S. D., & Seely, T. D. (2002). Rule Application as Cycles in a Level-Free Syntax. In: S. D. Epstein & T. D. Seely (Edd.), Derivation and Explanation in the Minimalist Program. Massachusetts, Malden: Blackwell, 65–89.
Everaert, M. B. H., Huybregts, M. A. C., Chomsky, N., Berwick, R. C., & Bolhuis, J. J. (2015). Structures, Not Strings: Linguistics as Part of the Cognitive Sciences. Trends in Cognitive Sciences, 19, 729–743.
Fell, J., & Axmacher, N. (2011). The Role of Phase Synchronization in Memory Processes. Nature Reviews Neuroscience, 12, 105–118.
Fell, J., Klaver, P., Lehnertz, K., Grunwald, T., Schaller, C., Elger, C. E., & Fernández, G. (2001). Human Memory Formation is Accompanied by Rhinal-Hippocampal Coupling and Decoupling. Nature Neuroscience, 4, 1259–1264.
Feng, Y., & Lapata, M. (2010). Visual Information in Semantic Representation. In: Proceeding of the 2010 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. New York: Association for Computational Linguistics, 91–99.
Fernández, J. R. (2015). Locality in Language and Locality in Brain Oscillatory Structures. Biolinguistics, 9, 74–95.
Fong, S. (2021). Some Third Factor Limits on Merge. Tucson, Arizona. Retrieved from https://sandiway.arizona.edu/fong2021ms.pdf
Frank, S. L., & Yang, J. (2018). Lexical Representation Explains Cortical Entrainment during Speech Comprehension. PLOS ONE, 13, e0197304, 1–11.
Franks, S. L., Bod, R., & Christiansen, M. H. (2012). How Hierarchical is Language Use? Proceedings of the Royal Society, 279, 4522–4531.
Freidin, R. (2016). Chomsky’s Linguistics: Goals of the Generative Enterprise. Language, 92, 671–723.
Friederici, A. D. (2017). Language in Our Brain: The Origins of a Uniquely Human Capacity. Cambridge, Massachusetts: MIT Press.
Friederici, A. D., Bahlmann, J., Heim, S., Schubotz, R. I., & Anwander, A. (2006). The Brain Differentiates Human and non-Human Grammars: Functional Localization and Structural Connectivity. Proceedings in the National Academy of Sciences, 103, 2458–2463.
Fries, P. (2005). A Mechanism for Cognitive Dynamics: Neuronal Communication Through Neuronal Coherence. Trends in Cognitive Sciences, 9, 474–480.
Fries, P. (2015). Rhythms for Cognition: Communication Through Coherence. Neuron, 88, 220–235.
Fries, P., Nikolić, D., & Singer, W. (2007). The Gamma Cycle. Trends in Neuroscience, 30, 309–316.
Fries, P., Reynolds, J. H., Rorie, A. E., & Desimone, R. (2001). Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention. Science, 291, 1560–1563.
Fusi, S., Miller, E. K., & Rigotti, M. (2016). Why Neurons Mix: Higher Dimensionality for Higher Cognition. Current Opinion in Neurobiology, 37, 66–74.
Gallant, J. L., & Popham, S. F. (2020). Semantic Representation in the Human Brain under Rich, Naturalistic Conditions. In: D. Poeppel, G. R. Mangun, & M. S. Gazzaniga (Edd.), The Cognitive Neurosciences (6th ed.). Cambridge, Massachusetts: MIT Press, 469–480.
Gallego, Á. J., & Orús, R. (2022). Language Design as Information Renormalization. SN Computer Science, 3, 140, 1–25.
Gallego, J. A., Perich, M. G., Chowdhury, R. H., Solla, S. A., & Miller, L. E. (2020). Long-Term Stability of Cortical Population Dynamics underlying Consistent Behavior. Nature Neuroscience, 23, 260–270.
Gallego, J. A., Perich, M. G., Miller, L. E., & Solla, S. A. (2017). Neural Manifold for the Control of Movement. Neuron, 94, 978–984.
Gauthier, J., & Ivanova, A. (2018). Does the Brain Represent Words? An Evaluation of Brain Decoding Studies of Language Understanding. ArXiv, 1–4.
Gazzaniga, M. S., Ivry, R. B., & Mangun, G. (2019). Cognitive Neuroscience: The Biology of the Mind (5th ed.). New York: W. W. Norton & Company.
Geeraerts, D. (2010). Theories of Lexical Semantics. Oxford: Oxford University Press.
Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986). Neuronal Population Coding of Movement. Science, 233, 1416–1419.
Gerstner, W. (1995). Time Structure of the Activity in Neural Network Models. Physical Review E, 51, 738–758.
Gerstner, W. (2000). Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking. Neural Computation, 12, 43–89.
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal Dynamics: From Single Neurons to Networks of Models of Cognition. Cambridge: Cambridge University Press.
Gerstner, W., & van Hemmen, L. J. (1992). Associative Memory in a Network of ‘Spiking’ Neurons. Network, 3, 139–164.
Gerstner, W., van Hemmen, L. J., & Cowan, J. D. (1996). What Matters in Neuronal Locking. Neural Computation, 8, 1653–1676.
Gielen, S., Krupa, M., & Zeitler, M. (2010). Gamma Oscillations as a Mechanism for Selective Information Transmission. Biological Cybernetics, 103, 151–165.
Giraud, A.-L., & Poeppel, D. (2012). Cortical Oscillations and Speech Processing: Emerging Computational Principles and Operations. Nature Neuroscience, 15, 511–517.
Goldman-Rakic, P. S. (1995). Cellular basis of working memory. Neuron, 14, 477–485.
Greco, M., Cometa, A., Artoni, F., Frank, R., & Moro, A. (2023). False Perspectives on Human Language: Why Statistics Needs Linguistics. Frontiers in Language Sciences, 2, 1178932, 1–8.
Gross, C. G. (2002). The Genealogy of the ‘Grandmother Cell’. The Neuroscientist, 8, 512–518.
Haegeman, L., & Lohndal, T. (2010). Negative Concord and (Multiple) Agree: A Case Study of West Flemish. Linguistic Inquiry,41, 181–211.
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., & Moser, E. I. (2005). Microstructure of a Spatial Map in the Entorhinal Cortex. Nature, 436, 801–806.
Halle, M., & Marantz, A. (1993). Distributed Morphology and the Pieces of Inflection. In: K. Hale & S. J. Keyser (Edd.), The View from Building 20: Essays in Linguistics in Honor of Sylvain Bromberger. Cambridge, Massachusetts: MIT Press, 111–176.
Harley, H. (2014). On the Identity of Roots. Theoretical Linguistics, 40, 225–276.
Harley, H., & Ritter, E. (2002). Person and Number in Pronouns: A Feature-Geometric Analysis. Language, 78, 482–526.
Haspelmath, M. (2011). The Indeterminacy of Word Segmentation and the Nature of Morphology and Syntax. Folia Linguistica, 51, 31–80.
Haspelmath, M. (2023). Defining the Word. Word, 69, 283–297.
Hasselmo, M., Bodelón, C., & Wyble, B. P. (2002). A Proposed Function for Hippocampal Theta Rhythm: Separate Phases Encoding and Retrieval Enhance Reversal of Prior Learning. Neural Computation, 14, 793–817.
Hauser, M., Chomsky, N., & Fitch, T. (2002). The Faculty of Language: What is it, Who has it, and How did it Evolve? Science,298, 1569–1579.
Helfrich, R. F., & Knight, R. T. (2016). Oscillatory Dynamics of Prefrontal Cognitive Control. Trends in Cognitive Sciences, 20, 916–930.
Henke, K. (2010). A Model for Memory Systems Based on Processing Modes rather than Consciousness. Nature Reviews Neuroscience, 11, 523–532.
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Shakir, M., Lerchner, A. (2017). β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. International Conference on Learning Representations. Toulon, France.
Hinton, G. E., McClelland, J. L., Manning, C. D., & Rumelhart, D. E. (1986). Distributed Representations. In: D. E. Rumelhart & J. L. McClelland (Edd.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1). Cambridge, Massachusetts: MIT Press, 77–109.
Hopfield, J. J. (1982). Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings in the National Academy of Sciences, 79, 2554–2558.
Hopfield, J. J. (1984). Neurons with Graded Response Have Collective Computational Properties like Those of Two-State Neurons. Proceedings in the National Academy of Sciences, 81, 3088–3092.
Hopfield, J. J. (2007). Hopfield Network. Scholarpedia, 2, 1977.
Hoshi, K. (2023). Reconceptualizing Merge in Search for the Link with Brain Oscillatory Nature of Language in Biolinguistics. Biolinguistics, 17, e12561: 1-12.
Howard, M. W., Fotedar, M. S., Datey, A. V., & Hasselmo, M. (2005). The Temporal Context Model in Spatial Navigation and Relational Learning: Toward A Common Explanation of Medial Temporal Lobe Function Across Domains. Psychological Review, 112, 75–116.
Howard, M. W., & Kahana, M. J. (2002). A Distributed Representation of Temporal Context. Journal of Mathematical Psychology, 46, 269–299.
Howard, M. W., MacDonald, C. J., Tiganj, Z., Shankar, K. H., Du, Q., Hasselmo, M., & Eichenbaum, H. (2014). A Unified Mathematical Framework for Coding Time, Space, and Sequences in the Hippocampal Region. Journal of Neuroscience, 34, 4692–4707.
Howard, M. W., Rizzuto, D. S., Caplan, J. B., Madsen, J. R., Lisman, J., Aschenbrenner-Scheibe, R., Schulze-Bonhage, A., Kahana, M. J. (2003). Gamma Oscillations Correlate with Working Memory Load in Humans. Cerebral Cortex, 13, 1369–1374.
Howard, M. W., Shankar, K. H., & Jagadisan, U. K. K. (2010). Constructing Semantic Representations from a Gradually Changing Represeentation of Temporal Context. Topics in Cognitive Science, 3, 48–73.
Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016). Natural Speech Reveals the Semantic Maps that Tile Human Cerebral Cortex. Nature, 532, 453–458.
Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. J. (2012). A Continuous Semantic Space describes the Representation of Thousands of Object and Action Categories across the Human Brain. Neuron, 76, 1210–1224.
Indefrey, P. (2007). Brain-Imaging Studies of Language Production. In: G. M. Gaskell (Ed.), The Oxford Handbook of Psycholinguistics. Oxford: Oxford University Press, 547–564.
Indefrey, P., & Levelt, W. J. M. (2004). The Spatial and Temporal Signatures of Word Production. Cognition, 92, 101–144.
Ingason, A. K. (To Appear). Phases/Cyclicity. In: A. Alexiadou, R. Kramer, A. Marantz, & I. Oltra-Massuet (Edd.), The Cambridge Handbook of Distributed Morphology. Cambridge: Cambridge University Press.
Jäger, A. (2010). Anything is Nothing is Something: On the Diachrony of Polarity Types of Indefinites. Natural Language & Linguistic Theory, 28, 787–822.
Jensen, O., & Lisman, J. E. (2005). Hippocampal Sequence-Encoding Driven by Cortical Multi-Item Working Memory Buffer. Trends in Neuroscience, 28, 67–72.
Jolivet, R., Lewis, T. J., & Gerstner, W. (2003). The Spike Response Model: A Framework to Predict Neuronal Spike Trains. In: O. Kaynak, E. Alpaydin, E. Oja, & L. Xu (Edd.), Artificial Neural Networks and Neural Information Processing—ICANN/ICONIP 2003. Berlin: Springer Nature, 846–853.
Jolivet, R., Rauch, A., Lüscher, H.-R., & Gerstner, W. (2006). Predicting Spike Timing of Neocortical Pyramidal Neurons by Simple Threshold Models. Journal of Computational Neuroscience, 21, 35–49.
Jurafsky, D., & Martin, J. H. (2025). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models (3rd ed.). Retrieved from https://web.stanford.edu/~jurafsky/slp3
Kallini, J., Papadimitriou, I., Futrell, R., Mahowald, K., & Potts, C. (2024). Mission: Impossible Language Models. In: Proceeding of the 62nd Annual Meeting of the Association for Computational Linguistics, Vol. 1: Long Papers. New Jersey: Association for Computational Linguistics, 14691–14714.
Kauf, C., Tuckute, G., Levy, R., Andreas, J., & Fedorenko, E. (2024). Lexical-Semantic Content, Not Syntactic Structure, Is the Main Contributor to ANN-Brain Similarity of fMRI Responses in the Language Network. Neurobiology of Language, 5, 7–42.
Kaufman, M. T., Seely, J. S., Sussillo, D., Ryu, S. I., Shenoy, K. V., & Churchland, M. M. (2016). The Largest Response Component in the Motor Cortex Reflects Movement Tuning but not Movement Type. eNeuro, 3, e0085: 1–25.
Kistler, W. M., Gerstner, W., & van Hemmen, L. J. (1997). Reduction of the Hodgkin-Huxley Equations to a Single-Variable Threshold Model. Neural Computation, 9, 1015–1045.
Koch, C. (2004). The Quest for Consciousness: A Neurobiological Approach. Macmillan Publishers.
Kopell, N. J., Ermentrout, G. B., Whittington, M. A., & Traub, R. D. (2000). Gamma rhythms and beta rhythms have different synchronization properties. PNAS, 97, 1867–1872.
Kopell, N. J., Gritton, H. J., Whittington, M. A., & Kramer, M. A. (2014). Beyond the Connectome: The Dynome. Neuron, 83, 1319–1328.
Kratzer, A. (1996). Severing the External Argument form its Verb. In: J. Rooryck & L. Zaring (Edd.), Phrase Structure and the Lexicon. Dordrecht: Kluwer, 109–137.
Kučerová, I. (2018). φ-Features at the Syntax-Semantics Interface: Evidence from Nominal Inflection. Linguistic Inquiry, 49, 1–77.
Kuhn, A., Aertsen, A., & Rotter, S. (2004). Neuronal Integration of Synaptic Input in the Fluctuation-Driven Regime. Journal of Neuroscience, 24, 2345–2356.
Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I., & Schroeder, C. E. (2008). Entrainment of Neuronal Oscillations as a Mechanism of Attention Selection. Science, 320, 110–113.
Lamb, S. M. (1999). Pathways of the Brain: The Neurocognitive Basis of Language. Amsterdam: John Benjamins Publishing Company.
Lambon Ralph, M. A., Jefferies, E., Patterson, K., & Rogers, T. T. (2017). The Neural and Computational Base of Semantic Cognition. Nature Reviews Neuroscience, 18, 42–55.
Lambon Ralph, M. A., & Patterson, K. (2008). Generalization and Differentiation in Semantic Memeory: Insights from Semantic Dementia. Annuals of the New York Academy of Sciences, 1124, 61–67.
Lavenex, P., & Amaral, D. G. (2000). Hippocampal-Neocortical Interaction: A Hierarchy of Associativity. Hippocampus, 10, 420–430.
LeBel, A., Jain, S., & Huth, A. G. (2021). Voxelwise Encoding Models Show that Cerebellar Language Representations are Highly Conceptual. Journal of Neuroscience, 41, 10341–10355.
Lebois, L. A. M., Wilson-Mendenhall, C. D., & Barsalou, L. W. (2015). Are Automatic Conceptual Cores the Gold Standard of Semantic Processing? The Context-Dependence of Spatial Meaning in Grounded Congruency Effects. Cognitive Science, 39, 1764–1801.
Lenci, A. (2018). Distributional Models of Word Meaning. Annual Review of Linguistics, 4, 151–171.
Levelt, W. J. M. (2001). Spoken Word Production: A Theory of Lexical Access. Proceedings in the National Academy of Sciences, 98, 13464–13471.
Levelt, W. J. M., Roelofs, A., & Mayer, A. S. (1999). A Theory of Lexical Access in Speech Production. Behavioral and Brain Sciences, 22, 1–75.
Levy, O., & Goldberg, Y. (2014). Neural Word Embedding as Implicit Matrix Factorization. In: Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, & K. Q. Weinberger (Edd.), Advances in Neural Processing Systems 27 (Vol. 1). New York: Curran Associates, 2177–2185.
Linderman, S., Johnson, M., Miller, A., Adams, R., Blei, D., & Paninski, L. (2017). Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54, 914–922.
Lisman, J. E., & Idiart, M. A. (1995). Storage of 7 ± 2 Short-Term Memories in Oscillatory Subcycles. Science, 267, 1512–1515.
Lisman, J. E., & Jensen, O. (2013). The Theta-Gamma Neural Code. Neuron, 77, 1002–1016.
Lo, C.-W., Tung, T.-Y., Ke, A. H., & Brennan, J. R. (2022). Hierarchy, Not Lexical Regularity, Modulates Low-Frequency Neural Synchrony During Language Comprehension. Neurobiology of Language, 3, 538–555.
Lundqvist, M., Compte, A., & Lansner, A. (2010). Bistable, irregular Firing and Population Oscillations in a Modular Attractor Memory Network. PLOS Computational Biology, 6, e1000803: 1–12.
Lundqvist, M., Herman, P., & Lansner, A. (2011). Theta and Gamma Power Increases and Alpha/Beta Power Decreases with Memory Load in Attractor Network Model. Journal of Cognitive Neuroscience, 23, 3008–3020.
Lundqvist, M., Herman, P., & Miller, E. K. (2018). Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not. Journal of Neuroscience, 38, 7013–7019.
Lundqvist, M., Rehn, M., Djurfeldt, M., & Lansner, A. (2006). Attractor Dynamics in a Modular Network Model of Neocortex. Network: Computation in Neural Systems, 17, 253–276.
Mahan, M. Y., & Georgopoulos, A. P. (2014). Neuronal Population Vector. In: D. Jaeger & R. Jung (Edd.), Encyclopedia of Computational Neuroscience. Berlin: Springer Nature, 2407–2412.
Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U., & Levy, O. (2020). Emergent Linguistic Structure in Artificial Neural Networks Trained by Self-Supervision. PNAS, 117, 30046–30054.
Marantz, A. (1991). Case and Licensing. In: G. Westphal, B. Ao, & H.-R. Chae (Edd.), Proceedings of the 8th Eastern States Conference on Linguistics (ESCOL 8). New York, Ithaca: CLC Publications, 234–253.
Marantz, A. (1997). No Escape from Syntax: Don’t Try Morphological Analysis in the Privacy of your own Lexicon. In: A. Dimitriadis, L. Siegel, C. Surek-Clark, & A. Williams (Edd.), University of Pennsylvania Working Papers in Linguistics: Proceedings of the 21st Annual Pennsylvania Linguistics Colloquium: Vol. 4.2. Pennsylvania: University of Pennsylvania Press, 201–225.
Marantz, A. (2007). Words and Phases. In: C. Sook-Hee (Ed.), Phases in the Theory of Grammar. Seoul: Dong In, 191–222.
Martin, A. E. (2020). A Compositional Neural Architecture for Language. Journal of Cognitive Neuroscience, 32, 1407–1427.
Martin, A. E., & Doumas, L. A. A. (2019). Tensors and Compositionality in Neural Systems. Philosophical Transactions of the Royal Society B: Biological Sciences, 375, 20190306.
Martin, R., Orús, R., & Uriagereka, J. (2019). Towards Matrix Syntax. Catalan Journal of Linguistic Special Issue, 27–44.
Mastrogiuseppe, F., & Ostojic, S. (2018). Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks. Neuron, 99, 609–623.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv, 1–12.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. In: F. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Edd.), Proceedings of the 26th Conference on Advances in Neural Information Processing System. New York: Curran Associates Inc, 3111–3119.
Miller, E. K., Lundqvist, M., & Bastos, A. M. (2018). Working Memory 2.0. Neuron, 100, 463–475.
Mitchell, T. M., Shinkareva, S. V., Carlson, A., Chang, K.-M., Malave, V. L., Mason, R. A., & Just, M. A. (2008). Predicting Human Brain Activity Associated with the Meanings of Nouns. Science, 30, 1191–1195.
Mitchell-Heggs, R., Prado, S., Gava, G. P., Go, M. A., & Schultz, S. R. (2023). Neural Manifold Analysis of Brain Circuit Dynamics in Health and Disease. Journal of Computational Neuroscience, 51, 1–21.
Moro, A. (2011). A Closer Look at the Turtle’s Eyes. PNAS, 108, 2177–2178.
Moro, A. (2016). Impossible Languages. Cambridge, Massachusetts: MIT Press.
Moro, A., Greco, M., & Cappa, S. F. (2023). Large Languages, Impossible Languages and Human Brains. Cortex, 167, 82–85.
Müller, S., & Wechsler, S. (2014). Lexical Approaches to Argument Structure. Theoretical Linguistics, 40, 1–76.
Murphy, E. (2016). The human Oscillome and its Explanatory Potential. Biolinguistics, 10, 6–20.
Murphy, E. (2020). The Oscillatory Nature of Language. Cambridge: Cambridge University Press.
Naselaris, T., Kay, K. N., Nishimoto, S., & Gallant, J. L. (2011). Encoding and Decoding in fMRI. NeuroImage, 56, 400–410.
Newmeyer, F. J. (2009). Current Challenges to the Lexicalist Hypothesis: An Overview and a Critique. In: W. D. Lewis, S. Karimi, H. Harley, & S. O. Farrar (Edd.), Time and Again: Theoretical Perspectives on Formal Linguistics in Honor of D. Terence Langendoen. Amsterdam: John Benjamins Publishing Company, 91–117.
Olshausen, B. A., & Field, D. J. (2004). Sparse Coding of Sensory Inputs. Current Opinion in Neurobiology, 14, 481–487.
Orús, R., Martin, R., & Uriagereka, J. (2019). Mathematical Foundations of Matrix Syntax. ArXiv, 1–48.
Pallier, C., Devauchelle, A.-D., & Dehaene, S. (2011). Cortical Representation of the Constituent Structure of Sentences. PNAS,108, 2522–2527.
Palva, M. J., Monto, S., Kulashekhar, S., & Palva, S. (2010). Neuronal Synchrony Reveals Working Memory Networks and Predicts Individual Memory Capacity. PNAS, 107, 7580–7585.
Panagiotidis, P. (2014). A Minimalist Approach to Roots. In: P. Kosta, S. L. Franks, T. Radeva-Bork, & L. Schürcks (Edd.), Minimalism and Beyond: Radicalizing the Interfaces. Amsterdam: John Benjamins Publishing Company, 287–303.
Panagiotidis, P., & Nóbrega, V. (To Appear). Why we Need Roots in Minimalism. In: Cambridge Handbook of Minimalism.
Pang, R., Lansdell, B. J., & Fairhall, A. L. (2016). Dimensionality Reduction in Neuroscience. Current Biology, 26, 656–660.
Paninski, L. (2004). Maximum Likelihood Estimation of Cascade Point-Process Neural Encoding Models. Network: Computation in Neural Systems, 15, 243–262.
Paninski, L., Pillow, J., & Lewi, J. (2007). Statistical Models for Neural Encoding, Decoding and Optimal Stimulus Design. Progress in Brain Research, 165, 493-507.
Paradis, M. (2000). Cerebral Representation of Bilingual Concepts. Bilingualism: Language and Cognition, 3, 22–24.
Pearl, L., & Lidz, J. (2013). Parameters in Language Acquisition. In: C. Boeckx & K. K. Grohmann (Edd.), The Cambridge Handbook of Biolinguistics. Cambridge: Cambridge University Press, 129–159.
Perl, Y. S., Geli, S., Pérez-Ordoyo, E., Zonca, L., Idesis, S., Vohryzek, J., Jirsa, V. K., Kringelbach, M. L., Tagliazucchi, E., Deco, G. (2025). Modelling Low-Dimensional Interacting Brain Networks Reveal Organising Principle in Human Cognition. Network Neuroscience, 9, 661–681.
Pesetsky, D., & Torrego, E. (2007). The Syntax of Valuation and the Interpretability of Features. In: S. Karimi, V. Samiian, & W. K. Wilkins (Edd.), Phrasal and Clausal Architecture: Syntactic Derivation and Interpretation. Amsterdam: John Benjamins Publishing Company, 262–294.
Pillow, J. W., Shlens, J., Paninski, L., Sher, A., Litke, A. M., Chichilnisky, E. J., & Simoncelli, E. P. (2008). Spatio-Temporal Correlations and Visual Signalling in a Complete Neuronal Population. Nature Letters, 454, 995–999.
Pittman-Polletta, B., Hsieh, W.-H., Kaur, S., Lo, M.-T., & Hu, K. (2014). Detecting Phase-Amplitude Coupling with High Frequency Resolution using Adaptive Decompositions. Journal in Neuroscience Methods, 226, 15–32.
Pittman-Polletta, B., & Kocsis, B. (2022). Assessing Neural Circuit Interactions and Dynamics with Phase-Amplitude Coupling. In: R. T. Vertes & T. A. Allen (Edd.), Electrophysiological Recoding Techniques. London: Springer Nature, 125-146.
Preminger, O. (2011). Asymmetries between Person and Number in Syntax: A Commentary on Baker’s SCOPA. Natural Language & Linguistic Theory, 29, 917–937.
Preminger, O. (2014). Agreement and its Failures. Cambridge, Massachusetts: MIT Press.
Preminger, O. (2019). What the PCC Tells us about “Abstract” Agreement, Head Movement, and Locality. Glossa: A Journal of General Linguistics, 4.
Putnam, M. T. (Ed.). (2010). Exploring Crash-Proof Grammars. Amsterdam: John Benjamins Publishing Company.
Quiroga, R. Q., Kraskov, A., Koch, C., & Fried, I. (2009). Explicit Encoding of Multimodal Percepts by Single Neurons in the Human Brain. Current Biology, 19, 1308–1313.
Quiroga, R. Q., 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.
Quiroga, R. Q., Reddy, L., Koch, C., & Fried, I. (2007). Decoding Visual Inputs from Multiple Neurons in the Human Temporal Lobe. Journal of Neurophysiology, 98, 1997–2007.
Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005). Invariant Visual Representation by Single Neurons in the Human Brain. Nature, 435, 1102–1107.
Ramchand, G. C. (2008). Verb Meaning and the Lexicon: A First-Phase Syntax. Cambridge: Cambridge University Press.
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., Greiff, V., Kreil, D., Kopp, M., Klambauer, G., Brandstetter, J., Hochreiter, S. (2021). Hopfield Networks is All You Need. ArXiv, 1–10.
Reinnsch, D. R., & Kambylis, A. (Edd.) (2001). Annae Comnenae Alexias. Corpus Fontium Historiae Byzantinae. De Gruyter: Berlin.
Richards, M. D. (2007). On Feature Inheritance: An Argument from the Phase Impenetrability Condition. Linguistic Inquiry, 38, 563–572.
Rieke, F., Warland, D., van Steveninck, R. de R., & Bialek, W. (1999). Spikes: Exploring the Neural Code. Cambridge, Massachusetts: MIT Press.
Rigotti, M., Barak, O., Warden, M., Wang, X.-J., Daw, N. D., Miller, E. K., & Fusi, S. (2013). The Importance of Mixed Selectivity in Complex Cognitive Tasks. Nature, 497, 585–590.
Rigotti, M., Rubin, D. B. D., Wang, X.-J., & Fusi, S. (2010). Internal Representation of Task Rules by Recurrent Dynamic: The Importance of the Diversity of Neural Responses. Frontiers in Computational Neuroscience, 4, 24, 1–29.
Rizzi, L. (2013). Syntactic Cartography and the Syntacticisation of Scope-Discourse Semantics. In: A. Reboul (Ed.), Mind, Values, and Metaphysics: Philosophical Essays in Honor of Kevin Mulligan, Vol. 2. Dordrecht: Springer, 517–534.
Roberts, I. (2007). Diachronic Syntax. Oxford: Oxford University Press.
Roberts, I. (2010). Agreement and Head Movement: Clitics, Incorporation, and Defective Goals. Cambridge, Massachusetts: MIT Press.
Roberts, I. (2019). Parameter Hierarchies and Universal Grammar. Oxford: Oxford University Press.
Roberts, I., & Roussou, A. (2003). Syntactic Change: A Minimalist Approach to Grammaticalization. Cambridge: Cambridge University Press.
Roopun, A. K., Kramer, M. A., Carracedo, L. M., Kaiser, M., Davies, C. H., Traub, R. D., Kopell, N., Whittington, M. A. (2008). Temporal Interactions between Cortical Rhythms. Frontiers in Neuroscience, 2, 145–154.
Roskies, A. L. (1999). The Binding Problem. Neuron, 24, 7–9.
Sadock, J. M. (2012). The Modular Architecture of Grammar. Cambridge: Cambridge University Press.
Sadtler, P. T., Quick, K. M., Golub, M. D., Chase, S. M., Ryue, S. I., Tyler-Kabara, E. C., Yu, B. M., Batista, A. P. (2014). Neural Constraints on Learning. Nature, 512, 423–426.
Santi, A., & Grodzinsky, Y. (2010). fMRI Adaptation Dissociates Syntactic Complexity Dimensions. Neuroimage, 51, 1285–1293.
Sassenhagen, J., & Fiebach, C. J. (2020). Traces of Meaning Itself: Encoding Distributional Word Vectors in Brain Activity. Neurobiology of Language, 5, 54–76.
Scheer, T. (2012). Chunk Definition in Phonology: Prosodic Constituency versus Phase Structure. In: M. Bloch-Trojnar & A. Bloch-Rozmej (Edd.), Modules and Interfaces. Lublin: Catholic University Lublin, 221–253.
Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., Fedorenko, E. (2021). The Neural Architecture of Language: Integrative Modeling Converges on Predictive Processing. PNAS, 118, e2105646118, 1–12.
Schroeder, C. E., & Lakatos, P. (2008). Low-Frequency Neuronal Oscillations as Instruments of Sensory Selection. Trends in Cognitive Sciences, 32, 9–19.
Semedo, J. D., Zandvakili, A., Machens, C. K., Byron, M. Y., & Kohn, A. (2019). Cortical Areas Interact through a Communication Subspace. Neuron, 102, 249–259.
Shankar, K. H., & Howard, M. W. (2012). A Scale-Invariant Internal Representation of Time. Neural Computation, 24, 134–193.
Shankar, K. H., & Howard, M. W. (2013). Optimally Fuzzy Temporal Memory. Journal of Machine Learning Research, 14, 3785–3812.
Shine, J. M., Breakspear, M., Bell, P. T., Martens, K. A. E., Shine, R., Koyejo, O., Sporns, O., Poldrack, R. A. (2019). Human Cognition Involves the Dynamic Integration of Neural Activity and Neuromodulatory Systems. Nature Neuroscience, 22, 289–296.
Singer, W. (2007). Binding by Synchrony. Scholarpedia, 2, 1657.
Sotero, R. C. (2015). Modeling the Generation of Phase-Amplitude Coupling in Cortical Circuits: From Detailed Networks to Neural Mass Models. Hindawi, 1, 1–12.
Soulat, H., Stephen, E. P., Beck, A. M., & Purdon, P. L. (2022). State Space Methods for Phase Amplitude Coupling Analysis. Scientific Reports, 12: 15940, 1–17.
Sporns, O., Tononi, G., & Kötter, R. (2005). The Human Connectome: A Structural Description of the Human Brain. PLOS Computational Biology, 1, e42, 245–251.
Sprague, T. C., Ester, E. F., & Serences, J. T. (2016). Restoring Latent Visual Working Memory Representations in Human Cortex. Neuron, 91, 649–707.
Squire, L. R., Stark, C. E. L., & Clark, R. E. (2004). The Medial Temporal Lobe. Annual Review of Neuroscience, 27, 279–306.
Starke, M. (2009). Nanosyntax: A Short Primer to a New Approach to Language. Nordlyd, 36, 1–6.
Stavisky, S. D., Willett, F. R., Wilson, G. H., Murphy, B. A., Rezaii, P., Avasino, D. T., Memberg, W. D., Miler, J. P., Kirsch, R. F., Hochberg, L. R., Aijboye, A. B., Druckmann, S., Shenoy, K. V., Henderson, J. M. (2019). Neural Ensemble Dynamics in Dorsal Motor Cortex during Speech in People with Paralysis. eLife, 8, e46015.
Steffenach, H.-A., Sloviter, R. S., Moser, E. I., & Moser, M.-B. (2002). Impaired Retention of Spatial Memory After Transection of Longitudinally Oriented Axons of Hippocampal CA3 Pyramidal Cells. PNAS, 99, 3194–3198.
Strogatz, S. H. (2000). From Kuramoto to Crawford: Exploring the Onset of Synchronization in Populations of Coupled Oscillators. Physica D, 143, 1–20.
Suppes, P., & Han, B. (2000). Brain-Wave Representation of Words by Superposition of a Few Sine Waves. PNAS, 97, 8738–8743.
Sussillo, D., & Barak, O. (2013). Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks. Neural Computation, 25, 626–649.
Szczepanski, S. M., Crone, N. E., Kuperman, R. A., Auste, K. I., Parvizi, J., & Knight, R. T. (2014). Dynamic Changes in Phase-Amplitude Coupling Facilitate Spatial Attention Control in Frontal-Parietal Cortex. PLOS Biology, 12, e1001936, 1–14.
Tang, J., LeBel, A., Jain, S., & Huth, A. G. (2023). Semantic Reconstruction of Continuous Language from Non-Invasive Brain Recordings. Nature Neuroscience, 26, 858–866.
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-Based Bayesian Models of Inductive Learning and Reasoning. Trends in Cognitive Sciences, 10, 309–318.
Theofanopoulou, C., & Boeckx, C. (2016). The Central Role of the Thalamus in Language and Cognition. In: K. Fujita & C. Boeckx (Edd.), Advances in Biolinguistics: The Human Language Faculty and its Biological Basis. London, New York: Routledge, 230–255.
Tort, A. B. L., Komorowski, R., Eichenbaum, H., & Kopell, N. (2010). Measuring Phase-Amplitude Coupling between Neuronal Oscillations of Different Frequencies. Journal of Neurophysiology, 104, 1995–1210.
Tort, A. B. L., Komorowski, R., Manns, J. R., Kopell, N., & Eichenbaum, H. (2008). Theta-Gamma Coupling Increases during the Learning of Item-Context Associations. PNAS, 106, 20941–20947.
Treves, A., & Rolls, E. T. (1994). Computational Analysis of the Role of the Hippocampus in Memory. Hippocampus, 4, 374–391.
Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P., & Brown, E. N. (2004). A Point Process Framework for Relating Neural Spiking Activity to Spiking History, Neural Ensemble, and Extrinsic Covariate Effects. Journal of Neurophysiology, 93, 1074–1089.
Tsao, A., Moser, M.-B., & Moser, E. I. (2013). Traces of Experience in the Lateral Entorhinal Cortex. Current Biology, 23, 399–405.
Tsao, A., Sugar, J., Lu, L., Wang, C., Knierim, J., Moser, M.-B., & Moser, E. I. (2018). Integrating Time from Experience in the Lateral Entorhinal Cortex. Nature, 561, 57–62.
Turney, P. D., & Pantel, P. (2014). From Frequency to Meaning: Vector Space Models of Semantics. Journal of Artificial Intelligence Research, 37, 141–188.
Vecchi, E. M., Marelli, M., Zamparelli, R., & Baroni, M. (2017). Spicy Adjectives and Nominal Donkeys: Capturing Semantic Deviance Using Compositionality in Distributional Spaces. Cognitive Science, 41, 102–136.
Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020). Computation Through Neural Population Dynamics. Annual Review of Neuroscience, 43, 249–275.
Waydo, S., Kraskov, A., Quiroga, R. Q., Fried, I., & Koch, C. (2006). Sparse Representation in the Human Medial Temporal Lobe. The Journal of Neuroscience, 26, 10232–10234.
Whittington, M. A., Traub, R. D., Kopell, N. J., Ermetrout, B., & Buhl, E. H. (2000). Inhibition-Based Rhythms: Experimental and Mathematical Observations on Network Dynamics. International Journal of Psychophysiology, 38, 315–336.
Whittington, Miles A., Traub, R. D., & Jefferys, J. G. R. (1995). Synchronized Oscillations in Interneuron Networks Driven by Metabotropic Glutamate Receptor Activation. Nature, 373, 612–615.
Wilson, H. R., & Cowan, J. D. (1972). Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons. Biophysical Journal, 12, 1–24.
Wiskott, L., & Sejnowski, T. J. (2002). Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation, 14, 715–770.
Womelsdorf, T., & Fries, P. (2007). Neuronal Coherence during Selective Attentional Processing and Sensory-Motor Integration. Journal of Physiology (Paris), 100, 182–193.
Wood, J. (2016). How Roots do and don’t Constrain the Interpretation of Voice. Working Papers in Scandinavian Syntax, 96, 1–25.
Wood, J., & Marantz, A. (2017). The Interpretation of External Arguments. In: R. D’Alessandro, I. Franco, & Á. J. Gallego (Edd.), The Verbal Domain. Oxford: Oxford University Press, 255–278.
Wood, J., & Tyler, M. (To Appear). Voice. In: The Cambridge Handbook of Comparative Syntax. Cambridge: Cambridge University Press.
Zaccarella, E., & Friederici, A. D. (2015). Merge in the Human Brain: A Sub-Region Based Functional Investigation in the Left Pars Opercularis. Frontiers in Psychology, 6, 1818, 1–10.
Zeijlstra, H. (2004). Sentential Negation and Negative Concord. University of Amsterdam: PhD Dissertation.
Zeijlstra, H. (2008). On the Syntactic Flexibility of Formal Features. In: T. Biberauer (Ed.), The Limits of Syntactic Variation. Amsterdam: John Benjamins Publishing Company, 143–174.
Zeijlstra, H. (2012). The is Only One Way to Agree. The Linguistic Review, 29, 491–453.
Zeijlstra, H. (2014). On the Uninterpretability of Interpretable Features. In: P. Kosta, S. L. Franks, T. Radeva-Bork, & L. Schürcks (Edd.), Minimalism and Beyond: Radicalizing the Interfaces. Amsterdam: John Benjamins Publishing Company, 109–128.
Zeng, Y., Bao, W., Tao, L., Hu, D., Yang, Z., Yang, L., & Shang, D. (2022). Regularized Spectral Spike Response Model: A Neuron Model for Robust Parameter Reduction. Brain Sciences, 12, 1008, 1–33.
