Language models based on Hebbian cell assemblies.
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.
Wennekers
Thomas
T
Centre for Theoretical and Computational Neuroscience, University of Plymouth, PL4 8AA Plymouth, United Kingdom. thomas.wennekers@plymouth.ac.uk
Garagnani
Max
M
Pulvermüller
Friedemann
F
D000818:Animals; D001245:Association Learning / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007802:Language; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010775:Photic Stimulation