Language models based on Hebbian cell assemblies

Wennekers, Thomas; Garagnani, M. and Pulvermüller, Friedemann. 2006. Language models based on Hebbian cell assemblies. Journal of Physiology-Paris, 100(1-3), pp. 16-30. ISSN 0928-4257 [Article]

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Abstract or Description

This paper demonstrates how associative neural networks as standard models for Hebbian cell assemblies can be extended to implement language processes in large-scale brain simulations. To this end the classical auto- and hetero-associative paradigms of attractor nets and synfire chains (SFCs) are combined and complemented by conditioned associations as a third principle which allows for the implementation of complex graph-like transition structures between assemblies. We show example simulations of a multiple area network for object-naming, which categorises objects in a visual hierarchy and generates different specific syntactic motor sequences (‘‘words’’) in response. The formation of cell assemblies due to ongoing plasticity in a multiple area network for word learning is studied afterwards. Simulations show how assemblies can form by means of percolating activity across auditory and motor-related language areas, a process supported by rhythmic, synchronized propagating waves through the network. Simulations further reproduce differences in own EEG&MEG experiments between responses to word- versus non-word stimuli in human subjects.

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Neo-cortex, Hebbian cell assembly, Associative memory, Attractor network, Synfire chain, Cortical micro-circuit, Language model

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1 November 2006Published

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13 Dec 2019 10:51

Last Modified:

13 Dec 2019 10:51

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Yes, this version has been peer-reviewed.


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