The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields

By Gustavo Deco, Viktor Jirsa, Peter A. Robinson, Michael Breakspear, Karl J. Friston. PLoS Comput. Biol., 2008

1,094 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e518d831ff819019dd7f9de541983a19e6e79941

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Citations in the corpus

Functional and Effective Connectivity: A Review (2011, neutral): “These models are more sophisticated than the neuronal models for fMRI and are based on neural-mass or mean-ﬁeld models of interacting neuronal populations (see Deco et al., 2008).”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, positive): “A neural mass, that is, a group of neurons at a given physical location with similar characteristics and coordinated behavior (Deco et al., 2008), is the smallest operational (neural) unit of a brain network model.”
Free Energy, Value, and Attractors (2011, background): “order parameters) are just the average of each state over oscillators; this particular phase-function is known as a mean field: see [27] for a discussion of mean field treatments of neuronal dynamics.”
Dynamic causal modelling: A critical review of the biophysical and statistical foundations (2011, background): “Since the early work by Amari (Amari 1977), much effort has been invested in developing a neural field theory (e.g., see Deco et al. 2008 and references therein); incorporating these ideas into the DCM framework may prove fruitful.”
On the nature of seizure dynamics. (2014, methodology): “…and/or biophysical principles and laws, e.g. Nernst equation of electrochemical equilibrium, conservation of mass and/or energy, or mean fields (Deco et al., 2008), leading to neural population or ‘mass’ models (Wilson and Cowan, 1972; Nunez, 1974; Freeman, 1975; Jansen and Rit, 1995; Brunel…”
LFP and oscillations—what do they tell us? (2015, methodology): “gain control Neural fields treat neuronal signalling as a continuous process on the cortical sheet using partial differential equations [21,22].”
Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimer's Disease (2012, methodology): “[27] and Pons et al.”
Computational psychiatry as a bridge from neuroscience to clinical applications (2016, methodology): “Biophysically detailed models can also be reduced to extract the core nonlinear dynamical components 95 and make it amenable to detailed mathematical analysis using stability or perturbation analyses.”
The future of FMRI connectivity (2012, background): “At the lowest modelling level, there is an increasing amount of exciting neural-network simulation work, some of which simulates networks of individual neurons, but most of which shows/assumes that groups of neurons can be treated as single units (Deco et al., 2008).”
Effective connectivity: Influence, causality and biophysical modeling (2011, positive): “Finally the fourth development is the emergence of neural ﬁ eld models (Coombes, 2010; Deco et al., 2008), which not only poses much larger scale problems but also the use of integral equations, differential – integral equations, and partial differential equations which have yet to be exploited by…”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “These have been shown to produce neurobiologically plausible behaviors such as generalized synchronization, metastability, and multistability (Breakspear, 2017; Deco et al., 2008; Golos, Jirsa, & Dauce, 2015; Heitmann & Breakspear, 2018; Roberts et al., 2019).”
The free energy principle made simpler but not too simple (2022, neutral): “For example, the fast electrophysiological fluctuations of a neuron become, random fluctuations from the point of view of neuronal population dynamics and sensory motor coordination in the brain [177, 178, 179].”
Spatiotemporal dynamics of continuum neural fields (2012, background): “(i) There does not currently exist a multi-scale analysis of conductance-based neural networks that provides a rigorous derivation of neural field equations, although some progress has been made in this direction [65, 70–72].”