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Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0003-0281-9450
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
2003 (English)In: Theory in biosciences, ISSN 1431-7613, E-ISSN 1611-7530, Vol. 122, no 1, 19-36 p.Article in journal (Refereed) Published
Abstract [en]

During the last few decades we have seen a convergence among ideas and hypotheses regarding functional principles underlying human memory. Hebb's now more than fifty years old conjecture concerning synaptic plasticity and cell assemblies, formalized mathematically as attractor neural networks, has remained among the most viable and productive theoretical frameworks. It suggests plausible explanations for Gestalt aspects of active memory like perceptual completion, reconstruction and rivalry. We review the biological plausibility of these theories and discuss some critical issues concerning their associative memory functionality in the light of simulation studies of models with palimpsest memory properties. The focus is on memory properties and dynamics of networks modularized in terms of cortical minicolumns and hypercolumns. Biophysical compartmental models demonstrate attractor dynamics that support cell assembly operations with fast convergence and low firing rates. Using a scaling model we obtain reasonable relative connection densities and amplitudes. An abstract attractor network model reproduces systems level psychological phenomena seen in human memory experiments as the Sternberg and von Restorff effects. We conclude that there is today considerable substance in Hebb's theory of cell assemblies and its attractor network formulations, and that they have contributed to increasing our understanding of cortical associative memory function. The criticism raised with regard to biological and psychological plausibility as well as low storage capacity, slow retrieval etc has largely been disproved. Rather, this paradigm has gained further support from new experimental data as well as computational modeling.

Place, publisher, year, edition, pages
2003. Vol. 122, no 1, 19-36 p.
Keyword [en]
biophysical compartmental neuron model, hypercolumns, minicolumns, forgetting, incremental learning, reaction time, visual-cortex, neural-network, neurons, patterns, cat, synapses, columns, monkey, rates
National Category
Neurosciences Bioinformatics (Computational Biology)
URN: urn:nbn:se:kth:diva-22557DOI: 10.1078/1431-7613-00072ISI: 000183327000003OAI: diva2:341255
QC 20100525 QC 20111212Available from: 2010-08-10 Created: 2010-08-10 Last updated: 2011-12-20Bibliographically approved

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Lansner, AndersFransén, ErikSandberg, Anders
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