Dynamic models of large-scale brain activity

Abstract

Movement, cognition and perception arise from the collective activity of neurons within cortical circuits and across large-scale systems of the brain. While the causes of single neuron spikes have been understood for decades, the processes that support collective neural behavior in large-scale cortical systems are less clear and have been at times the subject of contention. Modeling large-scale brain activity with nonlinear dynamical systems theory allows the integration of experimental data from multiple modalities into a common framework that facilitates prediction, testing and possible refutation. This work reviews the core assumptions that underlie this computational approach, the methodological framework that fosters the translation of theory into the laboratory, and the emerging body of supporting evidence. While substantial challenges remain, evidence supports the view that collective, nonlinear dynamics are central to adaptive cortical activity. Likewise, aberrant dynamic processes appear to underlie a number of brain disorders.

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Figure 1: A dynamical system is defined by a differential equation dX/dt = f(X).
Figure 2: Principles of the neuronal ensemble reduction.
Figure 3: Models of large-scale brain dynamics.
Figure 4: Multistable large-scale brain rhythms.
Figure 5: Technical and conceptual framework for empirical testing of NMMs and NFMs.
Figure 6: Application of neural field model to human epilepsy11. (a) Human scalp EEG recording showing a characteristic 3 Hz absence seizure.

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Acknowledgements

The author would like to thank J. Roberts, L. Gollo and L. Cocchi for detailed comments on the manuscript and C. Schneider, V. Nguyen, A. Perry, S. Sonkusare and M. Flynn for assistance with the figures. This manuscript was supported by the National Health and Medical Research Council (118153, 10371296, 1095227) and the Australian Research Council (CE140100007).

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Breakspear, M. Dynamic models of large-scale brain activity. Nat Neurosci 20, 340–352 (2017). https://doi.org/10.1038/nn.4497

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