Limits...
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.

Show MeSH
Summary of the stability changes of a neural field with mixed(local/global) connectivity.(Top) The relative size of stability area for different connectivitykernels. (Bottom) Illustration of change of stability as a functionof various factors. Gradient within the arrows indicates theincrease of the parameter indicated by each arrow. The direction ofthe arrow refers to the effect of the related factor on thestability change. The bold line separating stable and unstableregions indicates the course of the critical surface as the timedelay changes.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2519166&req=5

pcbi-1000092-g004: Summary of the stability changes of a neural field with mixed(local/global) connectivity.(Top) The relative size of stability area for different connectivitykernels. (Bottom) Illustration of change of stability as a functionof various factors. Gradient within the arrows indicates theincrease of the parameter indicated by each arrow. The direction ofthe arrow refers to the effect of the related factor on thestability change. The bold line separating stable and unstableregions indicates the course of the critical surface as the timedelay changes.

Mentions: A surprising result is that all changes of the extrinsic pathways have thesame qualitative effect on the stability of the network, independent of thelocal intrinsic architecture. This is not trivial, since despite the factthat extrinsic pathways are always excitatory the net effect on the networkdynamics could have been inhibitory, if the local architecture is dominatedby inhibition. Hence qualitatively different results on the total stabilitycould have been expected. Such is not the case, as we have shown here.Obviously the local architecture has quantitative effects on the overallnetwork stability, but not qualitatively differentiated effects. Purelyinhibitory local architectures are most stable, purely excitatoryarchitectures are the least stable. The biologically realistic andinteresting architectures, with mixed excitatory and inhibitorycontributions, play an intermediate role. When the stability of thenetwork's fixed point solution is lost, this loss may occur throughan oscillatory instability or a nonoscillatory solution. The loss ofstability for the nonoscillatory solution is never affected by thetransmission speeds, a direct physical consequence of its zero frequencyallowing time for all parts of the system to evolve in unison. The onlyroute to a non-oscillatory instability is through the increase of theheterogeneous connection strength. For oscillatory instabilities, thesituation is completely different. An increase of heterogeneous transmissionspeeds always causes a stabilization of the global network state. Theseresults are summarized in Figure 4.


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)

Summary of the stability changes of a neural field with mixed(local/global) connectivity.(Top) The relative size of stability area for different connectivitykernels. (Bottom) Illustration of change of stability as a functionof various factors. Gradient within the arrows indicates theincrease of the parameter indicated by each arrow. The direction ofthe arrow refers to the effect of the related factor on thestability change. The bold line separating stable and unstableregions indicates the course of the critical surface as the timedelay changes.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000092-g004: Summary of the stability changes of a neural field with mixed(local/global) connectivity.(Top) The relative size of stability area for different connectivitykernels. (Bottom) Illustration of change of stability as a functionof various factors. Gradient within the arrows indicates theincrease of the parameter indicated by each arrow. The direction ofthe arrow refers to the effect of the related factor on thestability change. The bold line separating stable and unstableregions indicates the course of the critical surface as the timedelay changes.
Mentions: A surprising result is that all changes of the extrinsic pathways have thesame qualitative effect on the stability of the network, independent of thelocal intrinsic architecture. This is not trivial, since despite the factthat extrinsic pathways are always excitatory the net effect on the networkdynamics could have been inhibitory, if the local architecture is dominatedby inhibition. Hence qualitatively different results on the total stabilitycould have been expected. Such is not the case, as we have shown here.Obviously the local architecture has quantitative effects on the overallnetwork stability, but not qualitatively differentiated effects. Purelyinhibitory local architectures are most stable, purely excitatoryarchitectures are the least stable. The biologically realistic andinteresting architectures, with mixed excitatory and inhibitorycontributions, play an intermediate role. When the stability of thenetwork's fixed point solution is lost, this loss may occur throughan oscillatory instability or a nonoscillatory solution. The loss ofstability for the nonoscillatory solution is never affected by thetransmission speeds, a direct physical consequence of its zero frequencyallowing time for all parts of the system to evolve in unison. The onlyroute to a non-oscillatory instability is through the increase of theheterogeneous connection strength. For oscillatory instabilities, thesituation is completely different. An increase of heterogeneous transmissionspeeds always causes a stabilization of the global network state. Theseresults are summarized in Figure 4.

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