Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1371/journal.pcbi.1000092 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
biomedical research,emergent behavior,empirical research,single unit recording,computer model,thermodynamics,data model,information model,computational biology,mean field,space time,emergent properties,local field potential,neuronal network,nonlinear dynamics,electrophysiology,complex system | Neuroscience,Functional magnetic resonance imaging,Computer science,Mathematical theory,Working memory,Computational model,Local field potential,Artificial intelligence,Artificial neural network,Genetics,Electroencephalography | Journal |
Volume | Issue | ISSN |
4 | 8 | 1553-734X |
Citations | PageRank | References |
151 | 7.86 | 28 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gustavo Deco | 1 | 1004 | 156.20 |
| Viktor K. Jirsa | 2 | 537 | 44.52 |
| Peter A Robinson | 3 | 154 | 8.99 |
| Michael Breakspear | 4 | 807 | 54.18 |
| Karl Friston | 5 | 776 | 49.34 |