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Nested theta to gamma oscillations and precise spatiotemporal firing during memory retrieval in a simulated attractor network
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Royal Institute of Technology, Sweden.ORCID iD: 0000-0001-6553-823X
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: Brain Research, ISSN 0006-8993, E-ISSN 1872-6240, Vol. 1536, no S1, 68-87 p.Article in journal (Refereed) Published
Abstract [en]

Nested oscillations, where the phase of the underlying slow rhythm modulates the power of faster oscillations, have recently attracted considerable research attention as the increased phase-coupling of cross-frequency oscillations has been shown to relate to memory processes. Here we investigate the hypothesis that reactivations of memory patterns, induced by either external stimuli or internal dynamics, are manifested as distributed cell assemblies oscillating at gamma-like frequencies with life-times on a theta scale. For this purpose, we study the spatiotemporal oscillatory dynamics of a previously developed meso-scale attractor network model as a correlate of its memory function. The focus is on a hierarchical nested organization of neural oscillations in delta/theta (2–5 Hz) and gamma frequency bands (25–35 Hz), and in some conditions even in lower alpha band (8–12 Hz), which emerge in the synthesized field potentials during attractor memory retrieval. We also examine spiking behavior of the network in close relation to oscillations. Despite highly irregular firing during memory retrieval and random connectivity within each cell assembly, we observe precise spatiotemporal firing patterns that repeat across memory activations at a rate higher than expected from random firing. In contrast to earlier studies aimed at modeling neural oscillations, our attractor memory network allows us to elaborate on the functional context of emerging rhythms and discuss their relevance. We provide support for the hypothesis that the dynamics of coherent delta/theta oscillations constitute an important aspect of the formation and replay of neuronal assemblies.

Place, publisher, year, edition, pages
2013. Vol. 1536, no S1, 68-87 p.
Keyword [en]
Oscillation, Neuron firing pattern, Synchrony, Attractor model, Memory, Cortex
National Category
Bioinformatics and Systems Biology
URN: urn:nbn:se:su:diva-93604DOI: 10.1016/j.brainres.2013.08.002ISI: 000327830100007OAI: diva2:647081
10th International Workshop on Neural Coding (NC), Prague, Czech Republic, 02-07, 2012
Swedish Research Council, VR-621-2009-3807VinnovaEU, FP7, Seventh Framework Programme, EU-FP7-FET-269921
Available from: 2013-09-10 Created: 2013-09-10 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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Herman, Pawel AndrzejLansner, Anders
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