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Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Royal Institute of Technology, Sweden.
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Royal Institute of Technology, Sweden.
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Royal Institute of Technology, Sweden.
2013 (English)In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 33, no 29, 11817-+ p.Article in journal (Refereed) Published
Abstract [en]

Spontaneous oscillations measured by local field potentials, electroencephalograms and magnetoencephalograms exhibit a pronounced peak in the alpha band (8-12 Hz) in humans and primates. Both instantaneous power and phase of these ongoing oscillations have commonly been observed to correlate with psychophysical performance in stimulus detection tasks. We use a novel model-based approach to study the effect of prestimulus oscillations on detection rate. A previously developed biophysically detailed attractor network exhibits spontaneous oscillations in the alpha range before a stimulus is presented and transiently switches to gamma-like oscillations on successful detection. We demonstrate that both phase and power of the ongoing alpha oscillations modulate the probability of such state transitions. The power can either positively or negatively correlate with the detection rate, in agreement with experimental findings, depending on the underlying neural mechanism modulating the oscillatory power. Furthermore, the spatially distributed alpha oscillators of the network can be synchronized by global nonspecific weak excitatory signals. These synchronization events lead to transient increases in alpha-band power and render the network sensitive to the exact timing of target stimuli, making the alpha cycle function as a temporal mask in line with recent experimental observations. Our results are relevant to several studies that attribute a modulatory role to prestimulus alpha dynamics.

Place, publisher, year, edition, pages
2013. Vol. 33, no 29, 11817-+ p.
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:su:diva-92923DOI: 10.1523/JNEUROSCI.5155-12.2013ISI: 000321893500010OAI: oai:DiVA.org:su-92923DiVA: diva2:644330
Funder
Swedish Research Council, VR-621-2009-3807VinnovaEU, FP7, Seventh Framework Programme, 269921
Note

AuthorCount:3;

Available from: 2013-08-30 Created: 2013-08-26 Last updated: 2017-12-06Bibliographically 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.
Keyword
Attractor networks, computational neuroscience, cortex
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
Identifiers
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)
Opponent
Supervisors
Note

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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