Mean-field Modeling and Theory

Mean-field Modeling and Theory

Copyright: Conceptual framework for scaling of neural network models (van Albada et al. PLoS Comput Biol 2015)

Mean-field models describing the average activity of populations of neurons provide a picture of brain dynamics complementary to network models resolving individual neurons and synapses. Such coarse-grained models can be analyzed more systematically due to their smaller number of parameters and higher speed with which the equations can be integrated. Furthermore, they can offer analytical approximations to spiking neural network dynamics. We use mean-field approaches to gain systematic insights into the links between the structure and dynamics of the brain, and to facilitate constraining large-scale spiking neural network models.

Publications

  • van Meegen A., van Albada SJ. (2021) Microscopic theory of intrinsic timescales in spiking neural networks. Physical Review Research 3(4), 043077.
    DOI: 10.1103/PhysRevResearch.3.043077

  • Müller EJ., van Albada SJ., Kim JW., Robinson PA. (2017) Unified neural field theory of brain dynamics underlying oscillations in Parkinson’s disease and generalized epilepsies. Journal of Theoretical Biology 428:132–146.
    DOI: 10.1016/j.jtbi.2017.06.016.

  • Schuecker J., Schmidt M., van Albada SJ., Diesmann M., Helias M. (2017) Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. PLOS Computational Biology 13:e1005179.
    DOI: 10.1371/journal.pcbi.1005179.

  • van Albada SJ., Helias M., Diesmann M. (2015) Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations. PLOS Computational Biology 11:e1004490.
    DOI: 10.1371/journal.pcbi.1004490.

  • van Albada SJ., Robinson PA. (2013) Relationships between electroencephalographic spectral peaks across frequency bands. Frontiers in Human Neuroscience 7:56.
    DOI: 10.3389/fnhum.2013.00056

  • Kerr CC., van Albada SJ., Neymotin SA., Chadderdon GL., Robinson PA., Lytton WW. (2013) Cortical information flow in Parkinson’s disease: a composite network/field model. Frontiers in Computational Neuroscience 7:39.
    DOI: 10.3389/fncom.2013.00039

  • Chiang AKI., Rennie CJ., Robinson PA., van Albada SJ., Kerr CC. (2011) Age trends and sex differences of alpha rhythms including split alpha peaks. Clinical Neurophysiology 122:1505–1517.
    DOI: 10.1016/j.clinph.2011.01.040.

  • van Albada SJ, Kerr CC, Chiang AK, Rennie CJ, Robinson PA (2010) Neurophysiological changes with age probed by inverse modeling of EEG spectra Clin Neurophysiol 121: 21-38
  • van Albada SJ, Robinson PA (2009) Mean-field modeling of the basal ganglia-thalamocortical system. II. Dynamics of parkinsonian oscillations. J Theor Biol 257: 664-688
  • van Albada SJ, Gray RT, Drysdale PM, Robinson PA (2009) Mean-field modeling of the basal ganglia-thalamocortical system. I. Firing rates in healthy and parkinsonian states. J Theor Biol 257: 642-663
  • van Albada SJ, Rennie CJ, Robinson PA (2007) Variability of model-free and model-based quantitative measures of EEG. J Integ Neurosci 6(2): 279-307

Software

Institute - internal contributors

  • Alexander van Meegen

  • Prof. Moritz Helias

Collaborators (external)

  • Prof. Peter Robinson, The University of Sydney, Australia

Funding

None

Last Modified: 16.05.2022