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Theta and Gamma Power Increases and Alpha/Beta Power Decreases with Memory Load in an Attractor Network Model
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA).
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA).
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA).
2010 (English)In: Journal of cognitive neuroscience, ISSN 0898-929X, E-ISSN 1530-8898, Vol. 23, no 10, 3008-3020 p.Article in journal (Refereed) Published
Abstract [en]

Changes in oscillatory brain activity are strongly correlated with performance in cognitive tasks and modulations in specific frequency bands are associated with working memory tasks. Mesoscale network models allow the study of oscillations as an emergent feature of neuronal activity. Here we extend a previously developed attractor network model, shown to faithfully reproduce single-cell activity during retention and memory recall, with synaptic augmentation. This enables the network to function as a multi-item working memory by cyclic reactivation of up to six items. The reactivation happens at theta frequency, consistently with recent experimental findings, with increasing theta power for each additional item loaded in the network's memory. Furthermore, each memory reactivation is associated with gamma oscillations. Thus, single-cell spike trains as well as gamma oscillations in local groups are nested in the theta cycle. The network also exhibits an idling rhythm in the alpha/beta band associated with a noncoding global attractor. Put together, the resulting effect is increasing theta and gamma power and decreasing alpha/beta power with growing working memory load, rendering the network mechanisms involved a plausible explanation for this often reported behavior.

Place, publisher, year, edition, pages
2010. Vol. 23, no 10, 3008-3020 p.
Keyword [en]
National Category
URN: urn:nbn:se:su:diva-93429DOI: 10.1162/jocn_a_00029ISI: 000294055600030OAI: diva2:646556
Available from: 2011-09-12 Created: 2013-09-09 Last updated: 2015-01-13Bibliographically approved
In thesis
1. Oscillations and spike statistics in biophysical attractor networks
Open this publication in new window or tab >>Oscillations and spike statistics in biophysical attractor networks
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The work of this thesis concerns how cortical memories are stored and retrieved. In particular, large-scale simulations are used to investigate the extent to which associative attractor theory is compliant with known physiology and in vivo dynamics.

The first question we ask is whether dynamical attractors can be stored in a network with realistic connectivity and activity levels. Using estimates of biological connectivity we demonstrated that attractor memories can be stored and retrieved in biologically realistic networks, operating on psychophysical timescales and displaying firing rate patterns similar to in vivo layer 2/3 cells. This was achieved in the presence of additional complexity such as synaptic depression and cellular adaptation.

Fast transitions into attractor memory states were related to the self-balancing inhibitory and excitatory currents in the network. In order to obtain realistic firing rates in the network, strong feedback inhibition was used, dynamically maintaining balance for a wide range of excitation levels. The balanced currents also led to high spike train variability commonly observed in vivo. The feedback inhibition in addition resulted in emergent gamma oscillations associated with attractor retrieval. This is congruent with the view of gamma as accompanying active cortical processing.

While dynamics during retrieval of attractor memories did not depend on the size of the simulated network, above a certain size the model displayed the presence of an emergent attractor state, not coding for any memory but active as a default state of the network. This default state was accompanied by oscillations in the alpha frequency band. Such alpha oscillations are correlated with idling and cortical inhibition in vivo and have similar functional correlates in the model. Both inhibitory and excitatory, as well as phase effects of ongoing alpha observed in vivo was reproduced in the model in a simulated threshold-stimulus detection task.

Place, publisher, year, edition, pages
Stockholm: Numerical Analysis and Computer Science (NADA), Stockholm Univeristy, 2013. 78 p.
Attractor networks, computational neuroscience, cortex
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
urn:nbn:se:su:diva-93316 (URN)978-91-7447-756-6 (ISBN)
Public defence
2013-10-04, sal F3, Lindstedtsvägen 26, KTH, Stockholm, 13:00 (English)

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper8: In press.

Available from: 2013-09-12 Created: 2013-09-06 Last updated: 2013-09-10Bibliographically approved

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Lundqvist, MikaelHerman, PawelLansner, Anders
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