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Attractor dynamics in a modular network model of neocortex
Royal Institute of Technology, School of Numerical and Computer Science ((CSC).
Royal Institute of Technology, School of Numerical and Computer Science ((CSC).
Royal Institute of Technology, School of Numerical and Computer Science ((CSC).
Royal Institute of Technology, School of Numerical and Computer Science ((CSC).
2006 (English)In: Network, ISSN 0954-898X, E-ISSN 1361-6536, Vol. 17, no 3, 253-276 p.Article in journal (Refereed) Published
Abstract [en]

Starting from the hypothesis that the mammalian neocortex to a first approximation functions as an associative memory of the attractor network type, we formulate a quantitative computational model of neocortical layers 2/3. The model employs biophysically detailed multi-compartmental model neurons with conductance based synapses and includes pyramidal cells and two types of inhibitory interneurons, i.e., regular spiking non-pyramidal cells and basket cells. The simulated network has a minicolumnar as well as a hypercolumnar modular structure and we propose that minicolumns rather than single cells are the basic computational units in neocortex. The minicolumns are represented in full scale and synaptic input to the different types of model neurons is carefully matched to reproduce experimentally measured values and to allow a quantitative reproduction of single cell recordings. Several key phenomena seen experimentally in vitro and in vivo appear as emergent features of this model. It exhibits a robust and fast attractor dynamics with pattern completion and pattern rivalry and it suggests an explanation for the so-called attentional blink phenomenon. During assembly dynamics, the model faithfully reproduces several features of local UP states, as they have been experimentally observed in vitro, as well as oscillatory behavior similar to that observed in the neocortex.

Place, publisher, year, edition, pages
2006. Vol. 17, no 3, 253-276 p.
Keyword [en]
cortex, UP State, attentional blink, attractor dynamics, synchronization
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:su:diva-93425DOI: 10.1080/09548980600774619ISI: 000244140900003OAI: oai:DiVA.org:su-93425DiVA: diva2:646550
Available from: 2006-11-01 Created: 2013-09-09 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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Lundqvist, MikaelRehn, MartinDjurfeldt, MikaelLansner, Anders
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