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Oscillations and spike statistics in biophysical attractor networks
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA).
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 [en]
Attractor networks, computational neuroscience, cortex
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:su:diva-93316ISBN: 978-91-7447-756-6 (print)OAI: oai:DiVA.org:su-93316DiVA: diva2:646173
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
List of papers
1. Attractor dynamics in a modular network model of neocortex
Open this publication in new window or tab >>Attractor dynamics in a modular network model of neocortex
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.

Keyword
cortex, UP State, attentional blink, attractor dynamics, synchronization
National Category
Neurosciences
Identifiers
urn:nbn:se:su:diva-93425 (URN)10.1080/09548980600774619 (DOI)000244140900003 ()
Available from: 2006-11-01 Created: 2013-09-09 Last updated: 2017-12-06Bibliographically approved
2. Brain-scale simulation of the neocortex on the IBM Blue Gene/L  supercomputer
Open this publication in new window or tab >>Brain-scale simulation of the neocortex on the IBM Blue Gene/L  supercomputer
Show others...
2008 (English)In: IBM Journal of Research and Development, ISSN 0018-8646, E-ISSN 2151-8556, Vol. 52, no 1-2, 31-41 p.Article in journal (Refereed) Published
Abstract [en]

Biologically detailed large-scale models of the brain can now be simulated thanks to increasingly powerful massively parallel supercomputers. We present an overview, for the general technical reader, of a neuronal network model of layers II/III of the neocortex built with biophysical model neurons. These simulations, carried out on an IBM Blue Gene/Le supercomputer, comprise up to 22 million neurons and 11 billion synapses, which makes them the largest simulations of this type ever performed. Such model sizes correspond to the cortex of a small mammal. The SPLIT library, used for these simulations, runs on single-processor as well as massively parallel machines. Performance measurements show good scaling behavior on the Blue Gene/L supercomputer up to 8,192 processors. Several key phenomena seen in the living brain appear as emergent phenomena in the simulations. We discuss the role of this kind of model in neuroscience and note that full-scale models may be necessary to preserve natural dynamics. We also discuss the need for software tools for the specification of models as well as for analysis and visualization of output data. Combining models that range from abstract connectionist type to biophysically detailed will help us unravel the basic principles underlying neocortical function.

National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:su:diva-93426 (URN)10.1147/rd.521.0031 (DOI)000253014700005 ()
Available from: 2009-05-19 Created: 2013-09-09 Last updated: 2017-12-06Bibliographically approved
3. Bistable, Irregular Firing and Population Oscillations in a Modular Attractor Memory Network
Open this publication in new window or tab >>Bistable, Irregular Firing and Population Oscillations in a Modular Attractor Memory Network
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.

National Category
Computer and Information Science
Identifiers
urn:nbn:se:su:diva-49898 (URN)10.1371/journal.pcbi.1000803 (DOI)000279341000009 ()
Note

authorCount :3

Available from: 2010-12-21 Created: 2010-12-20 Last updated: 2015-01-13Bibliographically approved
4. Theta and Gamma Power Increases and Alpha/Beta Power Decreases with Memory Load in an Attractor Network Model
Open this publication in new window or tab >>Theta and Gamma Power Increases and Alpha/Beta Power Decreases with Memory Load in an Attractor Network Model
2010 (English)In: Journal of cognitive neuroscience, ISSN 0898-929X, E-ISSN 1530-8898, Vol. 23, no 10, 3008-3020 p.Article in journal (Refereed) Published
Abstract [en]

Changes in oscillatory brain activity are strongly correlated with performance in cognitive tasks and modulations in specific frequency bands are associated with working memory tasks. Mesoscale network models allow the study of oscillations as an emergent feature of neuronal activity. Here we extend a previously developed attractor network model, shown to faithfully reproduce single-cell activity during retention and memory recall, with synaptic augmentation. This enables the network to function as a multi-item working memory by cyclic reactivation of up to six items. The reactivation happens at theta frequency, consistently with recent experimental findings, with increasing theta power for each additional item loaded in the network's memory. Furthermore, each memory reactivation is associated with gamma oscillations. Thus, single-cell spike trains as well as gamma oscillations in local groups are nested in the theta cycle. The network also exhibits an idling rhythm in the alpha/beta band associated with a noncoding global attractor. Put together, the resulting effect is increasing theta and gamma power and decreasing alpha/beta power with growing working memory load, rendering the network mechanisms involved a plausible explanation for this often reported behavior.

Keyword
LONG-TERM POTENTIATION, SINGLE-NEURON ACTIVITY, MEDIAL TEMPORAL-LOBE, WORKING-MEMORY, PHASE-LOCKING, INTRACELLULAR-RECORDINGS, PREFRONTAL CORTEX, PYRAMIDAL CELLS, VISUAL-CORTEX, OSCILLATIONS
National Category
Neurosciences
Identifiers
urn:nbn:se:su:diva-93429 (URN)10.1162/jocn_a_00029 (DOI)000294055600030 ()
Available from: 2011-09-12 Created: 2013-09-09 Last updated: 2017-12-06Bibliographically approved
5. Variability of spike firing during theta-coupled replay of memories in a simulated attractor network
Open this publication in new window or tab >>Variability of spike firing during theta-coupled replay of memories in a simulated attractor network
2012 (English)In: Brain Research, ISSN 0006-8993, E-ISSN 1872-6240, Vol. 1434, 152-161 p.Article in journal (Refereed) Published
Abstract [en]

Simulation work has recently shown that attractor networks can reproduce Poisson-like variability of single cell spiking, with coefficient of variation (Cv(2)) around unity, consistent with cortical data. However, the use of local variability (Lv) measures has revealed area- and layer-specific deviations from Poisson-like firing. In order to test these findings in silico we used a biophysically detailed attractor network model. We show that Lv well above 1, specifically found in superficial cortical layers and prefrontal areas, can indeed be reproduced in such networks and is consistent with periodic replay rather than persistent firing. The memory replay at the theta time scale provides a framework for a multi-item memory storage in the model. This article is part of a Special Issue entitled Neural Coding.

Keyword
Attractor model, Cortex, Oscillation, Spike statistics, Variability, Working memory
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:su:diva-93430 (URN)10.1016/j.brainres.2011.07.055 (DOI)000301559700015 ()
Available from: 2011-12-19 Created: 2013-09-09 Last updated: 2017-12-06Bibliographically approved
6. Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model
Open this publication in new window or tab >>Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model
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.

National Category
Neurosciences
Identifiers
urn:nbn:se:su:diva-92923 (URN)10.1523/JNEUROSCI.5155-12.2013 (DOI)000321893500010 ()
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
7. Nested theta to gamma oscillations and precise spatiotemporal firing during memory retrieval in a simulated attractor network
Open this publication in new window or tab >>Nested theta to gamma oscillations and precise spatiotemporal firing during memory retrieval in a simulated attractor network
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.

Keyword
Oscillation, Neuron firing pattern, Synchrony, Attractor model, Memory, Cortex
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:su:diva-93604 (URN)10.1016/j.brainres.2013.08.002 (DOI)000327830100007 ()
Conference
10th International Workshop on Neural Coding (NC), Prague, Czech Republic, 02-07, 2012
Funder
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: 2017-12-06Bibliographically approved

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