Neural Fields, Masses and Bayesian Modelling

Dimitris A. Pinotsis () and Karl J. Friston
Additional contact information
Dimitris A. Pinotsis: University College London, The Wellcome Trust Centre for Neuroimaging
Karl J. Friston: University College London, The Wellcome Trust Centre for Neuroimaging

Chapter Chapter 17 in Neural Fields, 2014, pp 433-455 from Springer

Abstract: 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 Jansen-Rit model and the canonical microcircuit model 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 Field potential local (LFP) data but fail to find more evidence for such variants, relative to their neural mass counterparts, in MEG Magnetoencephalogram (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.

Keywords: Hide State; Local Field Potential; Neural Field; Lead Field; Cortical Source (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-54593-1_17

Ordering information: This item can be ordered from
http://www.springer.com/9783642545931

DOI: 10.1007/978-3-642-54593-1_17

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().