Neural Fields, Masses and Bayesian Modelling
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
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Neural Fields, Masses and Bayesian Modelling Dimitris A. Pinotsis and Karl J. Friston
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 JansenRit 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.
17.1 Introduction This chapter reviews recent developments in the modelling of brain imaging data that exploits neural field theory. We focus on the Bayesian optimization of model parameters, called Dynamic Causal Modelling (DCM) and its application in the context of neural fields [18, 46]. This framework is part of the academic freeware
D.A. Pinotsis () • K.J. Friston The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG, UK e-mail: [email protected] S. Coombes et al. (eds.), Neural Fields, DOI 10.1007/978-3-642-54593-1__17, © Springer-Verlag Berlin Heidelberg 2014
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D.A Pinotsis and K.J. Friston
Statistical Parametric Mapping1 (SPM), which is a popular platform for analyzing neuroimaging data, used by several neuroscience communities worldwide. DCM allows for a formal (Bayesian) statistical analysis of cortical network connectivity, based upon realistic biophysical models of brain responses. It is this particular feature of DCM—the unique combination of generative models with optimization techniques based upon (variational) Bayesian principles—that furnishes a novel way to characterize brain organization. In particular, it provides answers to questions about how the brain is wired and how it responds in different situations. In this chapter, we first present the general framework with an emphasis on the role of neural fields. We then consider particular applications, in the context of LFP [39] and MEG data [56] and show how DCM allows one to adjudicate between alternative models of brain imaging data, such as neural masses and fields.
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