Abstract
A fundamental prerequisite for language is the ability to distinguish word sequences that are grammatically well-formed from ungrammatical word strings and to generalise rules of syntactic serial order to new strings of constituents. In this work, we extend a neural model of syntactic brain mechanisms that is based on syntactic sequence detectors (SDs). Elementary SDs are neural units that specifically respond to a sequence of constituent words AB, but not (or much less) to the reverse sequence BA. We discuss limitations of the original version of the SD model (Pulvermüller, Theory in Biosciences, 2003) and suggest optimal model variants taking advantage of optimised neuronal response functions, non-linear interaction between inputs, and leaky integration of neuronal input accumulating over time. A biologically more realistic model variant including a network of several SDs is used to demonstrate that associative Hebb-like synaptic plasticity leads to learning of word sequences, formation of neural representations of grammatical categories, and linking of sequence detectors into neuronal assemblies that may provide a biological basis of syntactic rule knowledge. We propose that these syntactic neuronal assemblies (SNAs) underlie generalisation of syntactic regularities from already encountered strings to new grammatical word sequences.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chomsky, N.: Syntactic structures. Mouton, The Hague (1957)
Hauser, M., Chomsky, N., Fitch, W.: The faculty of language: what is it, who has it, and how did it evolve? Science 298(5598), 1569–1579 (2002)
Elman, J.: Finding structure in time. Cognitive Science 14, 179–211 (1990)
Elman, J., Bates, L., Johnson, M., Karmiloff-Smith, A., Parisi, D., Plunkett, K.: Rethinking innateness. A connectionist perspective on development. MIT Press, Cambridge (1996)
Pulvermüller, F.: The neuroscience of language: on brain circuits of words and serial order. Cambridge University Press, Cambridge (2003)
Pulvermüller, F.: Sequence detectors as a basis of grammar in the brain. Theory in Bioscience 122, 87–103 (2003)
Kleene, S.: Representation of events in nerve nets and finite automata. In: Shannon, C., McCarthy, J. (eds.) Automata studies, pp. 3–41. Princeton University Press, Princeton (1956)
Braitenberg, V., Heck, D., Sultan, F.: The detection and generation of sequences as a key to cerebellar function: experiments and theory. Behavioral and Brain Sciences 20, 229–245 (1997)
Reichardt, W., Varju, D.: Ubertragungseigenschaften im Auswertesystem für das Bewegungssehen. Zeitschrift für Naturforschung 14b, 674–689 (1959)
Egelhaaf, M., Borst, A., Reichardt, W.: Computational structure of a biological motion-detection system as revealed by local detector analysis in the fly’s nervous system. Journal of the Optical Society of America (A) 6, 1070–1087 (1989)
Fuster, J.: Memory in the cerebral cortex. MIT Press, Cambridge (1999)
Knoblauch, A., Pulvermüller, F.: Associative learning of discrete grammatical categories and rules (2005) (in preparation)
Destexhe, A., Mainen, Z., Sejnowski, T.: Kinetic models of synaptic transmission. In: [15], ch. 1, pp. 1–25
Knoblauch, A., Wennekers, T., Sommer, F.: Is voltage-dependent synaptic transmission in NMDA receptors a robust mechanism for working memory? Neurocomputing 44-46, 19–24 (2002)
Koch, C., Segev, I. (eds.): Methods in neuronal modeling. MIT Press, Cambridge (1998)
Palm, G.: On associative memories. Biological Cybernetics 36, 19–31 (1980)
Palm, G.: Neural Assemblies. An Alternative Approach to Artificial Intelligence. Springer, Berlin (1982)
Knoblauch, A., Palm, G.: Pattern separation and synchronization in spiking associative memories and visual areas. Neural Networks 14, 763–780 (2001)
Pulvermüller, F.: A brain perspective on language mechanisms: from discrete neuronal ensembles to serial order. Progress in Neurobiology 67, 85–111 (2002)
Knoblauch, A.: Synchronization and pattern separation in spiking associative memory and visual cortical areas. PhD thesis, Department of Neural Information Processing, University of Ulm, Germany (2003)
Pulvermüller, F., Knoblauch, A.: Emergence of discrete combinatorial rules in universal grammar networks (2005) (in preparation)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Knoblauch, A., Pulvermüller, F. (2005). Sequence Detector Networks and Associative Learning of Grammatical Categories. In: Wermter, S., Palm, G., Elshaw, M. (eds) Biomimetic Neural Learning for Intelligent Robots. Lecture Notes in Computer Science(), vol 3575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11521082_3
Download citation
DOI: https://doi.org/10.1007/11521082_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27440-7
Online ISBN: 978-3-540-31896-5
eBook Packages: Computer ScienceComputer Science (R0)