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Neural Fields pp 433–455Cite as

Neural Fields, Masses and Bayesian Modelling

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Abstract

This chapter considers the relationship between neural field and mass models and their application to modelling empirical data. Specifically, we consider neural masses as a special case of neural fields, when conduction times tend to zero and focus on two exemplar models of cortical microcircuitry; namely, the Jansen-Rit and the canonical microcircuit model . Both models incorporate parameters pertaining to important neurobiological attributes, such as synaptic rate constants and the extent of lateral connections. We describe these models and show how Bayesian inference can be used to assess the validity of their field and mass variants, given empirical data. Interestingly, we find greater evidence for neural field variants in analyses of LFP data but fail to find more evidence for such variants, relative to their neural mass counterparts, in MEG (virtual electrode) data. The key distinction between these data is that LFP data are sensitive to a wide range of spatial frequencies and the temporal fluctuations that these frequencies contain. In contrast, the lead fields, inherent in non-invasive electromagnetic recordings, are necessarily broader and suppress temporal dynamics that are expressed in high spatial frequencies. We present this as an example of how neuronal field and mass models (hypotheses) can be compared formally.

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Notes

  1. 1.

    http://www.fil.ion.ucl.ac.uk/spm/.

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Authors and Affiliations

  1. The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG, UK

    Dimitris A. Pinotsis & Karl J. Friston

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  1. Dimitris A. Pinotsis
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  2. Karl J. Friston
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Correspondence to Dimitris A. Pinotsis .

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Editors and Affiliations

  1. School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom

    Stephen Coombes

  2. Department of German Studies and Linguistics, Humboldt-Universität zu Berlin, Berlin, Germany

    Peter beim Graben

  3. Department of Mathematics, University of Reading, Reading, United Kingdom

    Roland Potthast

  4. School of Medicine, University of Auckland, Auckland, New Zealand

    James Wright

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Pinotsis, D.A., Friston, K.J. (2014). Neural Fields, Masses and Bayesian Modelling. In: Coombes, S., beim Graben, P., Potthast, R., Wright, J. (eds) Neural Fields. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54593-1_17

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