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Bistable, Irregular Firing and Population Oscillations in a Modular Attractor Memory Network
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Royal Institute of Technology, Sweden.
2010 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 6, no 6, e1000803- p.Article in journal (Refereed) Published
Abstract [en]

Attractor neural networks are thought to underlie working memory functions in the cerebral cortex. Several such models have been proposed that successfully reproduce firing properties of neurons recorded from monkeys performing working memory tasks. However, the regular temporal structure of spike trains in these models is often incompatible with experimental data. Here, we show that the in vivo observations of bistable activity with irregular firing at the single cell level can be achieved in a large-scale network model with a modular structure in terms of several connected hypercolumns. Despite high irregularity of individual spike trains, the model shows population oscillations in the beta and gamma band in ground and active states, respectively. Irregular firing typically emerges in a high-conductance regime of balanced excitation and inhibition. Population oscillations can produce such a regime, but in previous models only a non-coding ground state was oscillatory. Due to the modular structure of our network, the oscillatory and irregular firing was maintained also in the active state without fine-tuning. Our model provides a novel mechanistic view of how irregular firing emerges in cortical populations as they go from beta to gamma oscillations during memory retrieval.

Place, publisher, year, edition, pages
2010. Vol. 6, no 6, e1000803- p.
National Category
Computer and Information Science
URN: urn:nbn:se:su:diva-49898DOI: 10.1371/journal.pcbi.1000803ISI: 000279341000009OAI: diva2:380448

authorCount :3

Available from: 2010-12-21 Created: 2010-12-20 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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