Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network

Garagnani, M.; Wennekers, T. and Pulvermüller, F.. 2009. Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network. Cognitive Computation, 1(2), pp. 160-176. ISSN 1866-9956 [Article]

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Abstract or Description

Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly’s halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support.

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M.G. and F.P. acknowledge support by the UK Medical Research Council (U1055.04.003.00001.01, U1055.09.001.00002.01) and the European Community under the ‘‘New and Emerging Science and Technologies’’ Programme (NEST-2005-PATH-HUM contract 043374, NESTCOM). T.W. acknowledges support by the Engineering and Physical Sciences Research Council (grant EP/C010841/1, COLAMN—A Novel Computing Architecture for Cognitive Systems based on the Laminar Microcircuitry of the Neocortex).


Competitive recruitment learning, Language acquisition, Memory trace, Consolidation, LTP/LTD, Localist distributed representations, Neurocomputation, Neurobiologically plausible modelling, Synaptic plasticity, Perisylvian cortex, Perception-action associations

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14 February 2009Published Online
1 December 2008Accepted

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03 Jan 2017 15:15

Last Modified:

29 Apr 2020 16:21

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Yes, this version has been peer-reviewed.


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