ArticleCognitive computation2009
Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network.
Article in Cognitive computation, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed, 59 citations in OpenAlex.
- Article
- From Spontaneous Ignitions to Sensorimotor Cell Assemblies via Dopamine: A Spiking Neurocomputational Model of Infants' Hand Action Acquisition.Brain sciences · 2026Article
- From cortical and white matter structure to meaning: a brain-constrained neural network of semantic grounding in action and perception.Frontiers in human neuroscience · 2026Article
- Hebb's Vision: The Structural Underpinnings of Hebbian Assemblies.bioRxiv : the preprint server for biology · 2025Article
- On the ability of standard and brain-constrained deep neural networks to support cognitive superposition: a position paper.Cognitive neurodynamics · 2024Article
- Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia.Scientific reports · 2023Article
- Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks.Progress in neurobiology · 2023Review
- Brain-constrained neural modeling explains fast mapping of words to meaning.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Modelling concrete and abstract concepts using brain-constrained deep neural networks.Psychological research · 2022Article
- Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning.Scientific reports · 2019Article
- Principles underlying the input-dependent formation and organization of memories.Network neuroscience (Cambridge, Mass.) · 2019Article
- Formation of neocortical memory circuits for unattended written word forms: neuromagnetic evidence.Scientific reports · 2018Article
- A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity.Frontiers in computational neuroscience · 2018Article
- Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- Preventing Neurodegenerative Memory Loss in Hopfield Neuronal Networks Using Cerebral Organoids or External Microelectronics.Computational and mathematical methods in medicine · 2017Article
- Conceptual grounding of language in action and perception: a neurocomputational model of the emergence of category specificity and semantic hubs.The European journal of neuroscience · 2016Article
- A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords.Frontiers in computational neuroscience · 2016Article
- Thinking in circuits: toward neurobiological explanation in cognitive neuroscience.Biological cybernetics · 2014Review
- Neuronal correlates of decisions to speak and act: Spontaneous emergence and dynamic topographies in a computational model of frontal and temporal areas.Brain and language · 2013Article
- Prelimbic cortex ensembles promote appetitive learning-associated behavior.Learning & memory (Cold Spring Harbor, N.Y.)Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.