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Consistent spectral predictors for dynamic causal models of steady-state responses

Dynamic causal modelling (DCM) for steady-state responses (SSR) is a framework for inferring the mechanisms that underlie observed electrophysiological spectra, using biologically plausible generative models of neuronal dynamics. In this paper, we examine the dynamic repertoires of nonlinear conduct...

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Autores principales: Moran, Rosalyn J., Stephan, Klaas E., Dolan, Raymond J., Friston, Karl J.
Formato: Texto
Lenguaje:English
Publicado: Academic Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3093618/
https://www.ncbi.nlm.nih.gov/pubmed/21238593
http://dx.doi.org/10.1016/j.neuroimage.2011.01.012
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author Moran, Rosalyn J.
Stephan, Klaas E.
Dolan, Raymond J.
Friston, Karl J.
author_facet Moran, Rosalyn J.
Stephan, Klaas E.
Dolan, Raymond J.
Friston, Karl J.
author_sort Moran, Rosalyn J.
collection PubMed
description Dynamic causal modelling (DCM) for steady-state responses (SSR) is a framework for inferring the mechanisms that underlie observed electrophysiological spectra, using biologically plausible generative models of neuronal dynamics. In this paper, we examine the dynamic repertoires of nonlinear conductance-based neural population models and propose a generative model of their power spectra. Our model comprises an ensemble of interconnected excitatory and inhibitory cells, where synaptic currents are mediated by fast, glutamatergic and GABAergic receptors and slower voltage-gated NMDA receptors. We explore two formulations of how hidden neuronal states (depolarisation and conductances) interact: through their mean and variance (mean-field model) or through their mean alone (neural-mass model). Both rest on a nonlinear Fokker–Planck description of population dynamics, which can exhibit bifurcations (phase transitions). We first characterise these phase transitions numerically: by varying critical model parameters, we elicit both fixed points and quasiperiodic dynamics that reproduce the spectral characteristics (~ 2–100 Hz) of real electrophysiological data. We then introduce a predictor of spectral activity using centre manifold theory and linear stability analysis. This predictor is based on sampling the system's Jacobian over the orbits of hidden neuronal states. This predictor behaves consistently and smoothly in the region of phase transitions, which permits the use of gradient descent methods for model inversion. We demonstrate this by inverting generative models (DCMs) of SSRs, using simulated data that entails phase transitions.
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spelling pubmed-30936182011-07-12 Consistent spectral predictors for dynamic causal models of steady-state responses Moran, Rosalyn J. Stephan, Klaas E. Dolan, Raymond J. Friston, Karl J. Neuroimage Technical Note Dynamic causal modelling (DCM) for steady-state responses (SSR) is a framework for inferring the mechanisms that underlie observed electrophysiological spectra, using biologically plausible generative models of neuronal dynamics. In this paper, we examine the dynamic repertoires of nonlinear conductance-based neural population models and propose a generative model of their power spectra. Our model comprises an ensemble of interconnected excitatory and inhibitory cells, where synaptic currents are mediated by fast, glutamatergic and GABAergic receptors and slower voltage-gated NMDA receptors. We explore two formulations of how hidden neuronal states (depolarisation and conductances) interact: through their mean and variance (mean-field model) or through their mean alone (neural-mass model). Both rest on a nonlinear Fokker–Planck description of population dynamics, which can exhibit bifurcations (phase transitions). We first characterise these phase transitions numerically: by varying critical model parameters, we elicit both fixed points and quasiperiodic dynamics that reproduce the spectral characteristics (~ 2–100 Hz) of real electrophysiological data. We then introduce a predictor of spectral activity using centre manifold theory and linear stability analysis. This predictor is based on sampling the system's Jacobian over the orbits of hidden neuronal states. This predictor behaves consistently and smoothly in the region of phase transitions, which permits the use of gradient descent methods for model inversion. We demonstrate this by inverting generative models (DCMs) of SSRs, using simulated data that entails phase transitions. Academic Press 2011-04-15 /pmc/articles/PMC3093618/ /pubmed/21238593 http://dx.doi.org/10.1016/j.neuroimage.2011.01.012 Text en © 2011 Elsevier Inc. https://creativecommons.org/licenses/by/3.0/ Open Access under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/) license
spellingShingle Technical Note
Moran, Rosalyn J.
Stephan, Klaas E.
Dolan, Raymond J.
Friston, Karl J.
Consistent spectral predictors for dynamic causal models of steady-state responses
title Consistent spectral predictors for dynamic causal models of steady-state responses
title_full Consistent spectral predictors for dynamic causal models of steady-state responses
title_fullStr Consistent spectral predictors for dynamic causal models of steady-state responses
title_full_unstemmed Consistent spectral predictors for dynamic causal models of steady-state responses
title_short Consistent spectral predictors for dynamic causal models of steady-state responses
title_sort consistent spectral predictors for dynamic causal models of steady-state responses
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3093618/
https://www.ncbi.nlm.nih.gov/pubmed/21238593
http://dx.doi.org/10.1016/j.neuroimage.2011.01.012
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