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The dynamic brain: from spiking neurons to neural masses and cortical fields.

Deco G, Jirsa VK, Robinson PA, Breakspear M, Friston K - PLoS Comput. Biol. (2008)

Bottom Line: Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data.This makes dynamic models critical in integrating theory and experiments.We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences.

View Article: PubMed Central - PubMed

Affiliation: Institució Catalana de Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Department of Technology, Computational Neuroscience, Barcelona, Spain. Gustavo.Deco@upf.edu

ABSTRACT
The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space-time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalogram (MEG). Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences.

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Cortical architecture of the model.The neural field is illustrated by the rectangular box showing theneural activityμ(x,t)composed of inhibitory and excitatory neurons. The inputs(x,t) isprovided at locations xi via theGaussian localization function  with width . The explicit model parameters used in thesimulations are given in [126].
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pcbi-1000092-g015: Cortical architecture of the model.The neural field is illustrated by the rectangular box showing theneural activityμ(x,t)composed of inhibitory and excitatory neurons. The inputs(x,t) isprovided at locations xi via theGaussian localization function with width . The explicit model parameters used in thesimulations are given in [126].

Mentions: The second network is not tonotopically organized, hence its spatialdimension is of no relevance, when we consider only the competition of twostreams. In fact, the ability to show multistable pattern formation is theonly relevant property of the network and can be realized in multiplenetwork architectures as discussed in previous sections. A simplemultistable subsystem with its scalar state variabley(t) is given by the equation(94)where ε is a constant that capturesall linear contributions. I0 contains allconstant contributions given rise to the rest state activity. The functionalI(t) is specified as(95)where Ω is a neural activity threshold. Equations93, 94, and 95 define the dynamics of a stream classification model in oneof its simplest forms. Figure15 illustrates the architecture of the model.


The dynamic brain: from spiking neurons to neural masses and cortical fields.

Deco G, Jirsa VK, Robinson PA, Breakspear M, Friston K - PLoS Comput. Biol. (2008)

Cortical architecture of the model.The neural field is illustrated by the rectangular box showing theneural activityμ(x,t)composed of inhibitory and excitatory neurons. The inputs(x,t) isprovided at locations xi via theGaussian localization function  with width . The explicit model parameters used in thesimulations are given in [126].
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2519166&req=5

pcbi-1000092-g015: Cortical architecture of the model.The neural field is illustrated by the rectangular box showing theneural activityμ(x,t)composed of inhibitory and excitatory neurons. The inputs(x,t) isprovided at locations xi via theGaussian localization function with width . The explicit model parameters used in thesimulations are given in [126].
Mentions: The second network is not tonotopically organized, hence its spatialdimension is of no relevance, when we consider only the competition of twostreams. In fact, the ability to show multistable pattern formation is theonly relevant property of the network and can be realized in multiplenetwork architectures as discussed in previous sections. A simplemultistable subsystem with its scalar state variabley(t) is given by the equation(94)where ε is a constant that capturesall linear contributions. I0 contains allconstant contributions given rise to the rest state activity. The functionalI(t) is specified as(95)where Ω is a neural activity threshold. Equations93, 94, and 95 define the dynamics of a stream classification model in oneof its simplest forms. Figure15 illustrates the architecture of the model.

Bottom Line: Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data.This makes dynamic models critical in integrating theory and experiments.We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences.

View Article: PubMed Central - PubMed

Affiliation: Institució Catalana de Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Department of Technology, Computational Neuroscience, Barcelona, Spain. Gustavo.Deco@upf.edu

ABSTRACT
The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space-time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalogram (MEG). Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences.

Show MeSH