TITLE: Language Models based on Hebbian Cell Assemblies

ADDRESS:

Thomas Wennekers

Centre for Theoretical and Computational Neuroscience
University of Plymouth
PL4 8AA Plymouth, United Kingdom

Phone: +44-1752-23-3593 
Fax  : +44-1752-23-3349

Email: thomas.wennekers at plymouth.ac.uk


ABSTRACT:

We demonstrate how associative neural networks as standard models for
Hebbian cell assemblies can be extended to implement language
processes in large-scale brain simulations [1]. 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
[2]. Simulations show how assemblies can form by means of percolating
activity across auditory and motor-related language areas, a process
supported by rhythmic, synchronised propagating waves through the
network.

[1] Thomas Wennekers, Max Garagnani, and Friedemann Pulvermuller
(2006) Language Models based on Hebbian Cell Assemblies. Journal of
Physiology (Paris) 100, 16-30.

[2] Max Garagnani, Thomas Wennekers, Friedemann Pulvermuller (under
revision) Early and late brain reflections of what makes sense:
Attention effects in a neuronal model of the language cortex. European
Journal of Neuroscience.

ACKNOWLEDGEMENT: T.W. acknowledges support by the EPSRC (EP/C010841/1,
COLAMN project) and the EC (FP6-015879, FACETS project). He thanks
Friedemann Pulvermuller, Max Garagnani, Guenther Palm, Andreas
Knoblauch, and Fritz Sommer for many discussions influencing this
work.