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Attractor dynamics in a modular network model of neocortex
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-2358-7815
2006 (English)In: Network, ISSN 0954-898X, E-ISSN 1361-6536, Network: Computation in Neural Systems, 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:kth:diva-6310DOI: 10.1080/09548980600774619ISI: 000244140900003Scopus ID: 2-s2.0-33845421947OAI: oai:DiVA.org:kth-6310DiVA: diva2:10989
Note

QC 20150729

Available from: 2006-11-01 Created: 2006-11-01 Last updated: 2015-07-29Bibliographically approved
In thesis
1. Aspects of memory and representation in cortical computation
Open this publication in new window or tab >>Aspects of memory and representation in cortical computation
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [sv]

Denna avhandling i datalogi föreslår modeller för hur vissa beräkningsmässiga uppgifter kan utföras av hjärnbarken. Utgångspunkten är dels kända fakta om hur en area i hjärnbarken är uppbyggd och fungerar, dels etablerade modellklasser inom beräkningsneurobiologi, såsom attraktorminnen och system för gles kodning. Ett neuralt nätverk som producerar en effektiv gles kod i binär mening för sensoriska, särskilt visuella, intryck presenteras. Jag visar att detta nätverk, när det har tränats med naturliga bilder, reproducerar vissa egenskaper (receptiva fält) hos nervceller i lager IV i den primära synbarken och att de koder som det producerar är lämpliga för lagring i associativa minnesmodeller. Vidare visar jag hur ett enkelt autoassociativt minne kan modifieras till att fungera som ett generellt sekvenslärande system genom att utrustas med synapsdynamik. Jag undersöker hur ett abstrakt attraktorminnessystem kan implementeras i en detaljerad modell baserad på data om hjärnbarken. Denna modell kan sedan analyseras med verktyg som simulerar experiment som kan utföras på en riktig hjärnbark. Hypotesen att hjärnbarken till avsevärd del fungerar som ett attraktorminne undersöks och visar sig leda till prediktioner för dess kopplingsstruktur. Jag diskuterar också metodologiska aspekter på beräkningsneurobiologin idag.

Abstract [en]

In this thesis I take a modular approach to cortical function. I investigate how the cerebral cortex may realise a number of basic computational tasks, within the framework of its generic architecture. I present novel mechanisms for certain assumed computational capabilities of the cerebral cortex, building on the established notions of attractor memory and sparse coding. A sparse binary coding network for generating efficient representations of sensory input is presented. It is demonstrated that this network model well reproduces the simple cell receptive field shapes seen in the primary visual cortex and that its representations are efficient with respect to storage in associative memory. I show how an autoassociative memory, augmented with dynamical synapses, can function as a general sequence learning network. I demonstrate how an abstract attractor memory system may be realised on the microcircuit level -- and how it may be analysed using tools similar to those used experimentally. I outline some predictions from the hypothesis that the macroscopic connectivity of the cortex is optimised for attractor memory function. I also discuss methodological aspects of modelling in computational neuroscience.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. xiv, 99 p.
Series
Trita-NA, ISSN 0348-2952 ; 2006:17
Keyword
cerebral cortex, neural networks, attractor memory, sequence learning, biological vision, generative models, serial order, computational neuroscience, dynamical synapses
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-4161 (URN)91-7178-478-0 (ISBN)
Public defence
2006-11-13, F3, KTH, Lindstedtsvägen 26, Stockholm, 14:15
Opponent
Supervisors
Note
QC 20100916Available from: 2006-11-01 Created: 2006-11-01 Last updated: 2010-09-16Bibliographically approved
2. Large-scale simulation of neuronal systems
Open this publication in new window or tab >>Large-scale simulation of neuronal systems
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Biologically detailed computational models of large-scale neuronal networks have now become feasible due to the development of increasingly powerful massively parallel supercomputers. We report here about the methodology involved in simulation of very large neuronal networks. Using conductance-based multicompartmental model neurons based on Hodgkin-Huxley formalism, we simulate a neuronal network model of layers II/III of the neocortex. These simulations, the largest of this type ever performed, were made on the Blue Gene/L supercomputer and comprised up to 8 million neurons and 4 billion synapses. Such model sizes correspond to the cortex of a small mammal. After a series of optimization steps, performance measurements show linear scaling behavior both on the Blue Gene/L supercomputer and on a more conventional cluster computer. Results from the simulation of a model based on more abstract formalism, and of considerably larger size, also shows linear scaling behavior on both computer architectures.

Place, publisher, year, edition, pages
Stockholm: KTH, 2009. xii, 65 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2009:06
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-10616 (URN)978-91-7415-323-1 (ISBN)
Public defence
2009-06-09, Sal F2, KTH, Lindstedtsvägen 26, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20100722

Available from: 2009-06-03 Created: 2009-06-03 Last updated: 2013-04-08Bibliographically approved
3. Some computational aspects of attractor memory
Open this publication in new window or tab >>Some computational aspects of attractor memory
2005 (English)Licentiate thesis, comprehensive summary (Other scientific)
Abstract [en]

In this thesis I present novel mechanisms for certain computational capabilities of the cerebral cortex, building on the established notion of attractor memory. A sparse binary coding network for generating efficient representation of sensory input is presented. It is demonstrated that this network model well reproduces receptive field shapes seen in primary visual cortex and that its representations are efficient with respect to storage in associative memory. I show how an autoassociative memory, augmented with dynamical synapses, can function as a general sequence learning network. I demonstrate how an abstract attractor memory system may be realized on the microcircuit level -- and how it may be analyzed using similar tools as used experimentally. I demonstrate some predictions from the hypothesis that the macroscopic connectivity of the cortex is optimized for attractor memory function. I also discuss methodological aspects of modelling in computational neuroscience.

Place, publisher, year, edition, pages
Stockholm: KTH, 2005. viii, 76 p.
Series
Trita-NA, ISSN 0348-2952 ; 0509
Keyword
Datalogi, attractor memory, cerebral cortex, neural networks, Datalogi
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-249 (URN)91-7283-983-X (ISBN)
Presentation
2005-03-15, Sal E32, KTH, Lindstedtsvägen 3, Stockholm, 07:00
Opponent
Supervisors
Note
QC 20101220Available from: 2005-05-31 Created: 2005-05-31 Last updated: 2010-12-20Bibliographically approved

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