System Models for Inference on Mechanisms of Neuronal Dynamics
Published Online: 15 MAY 2012
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Reviews in Cell Biology and Molecular Medicine
How to Cite
Stephan, K. E. and Friston, K. J. 2012. System Models for Inference on Mechanisms of Neuronal Dynamics. Reviews in Cell Biology and Molecular Medicine. .
- Published Online: 15 MAY 2012
Understanding processes that result from the interaction of multiple elements requires mathematical models of system dynamics. This increasingly important theme has led to the revitalization of “systems biology,” with a focus on molecular questions. However, system models have been used for much longer in computational neuroscience and neuroimaging to infer on causal mechanisms of neuronal dynamics, for example, effective connectivity. In this chapter, system models are reviewed for inferring effective connectivity from brain activity data obtained by functional magnetic resonance imaging or electroencephalography. Following an outline of general systems theory, a generic framework is provided for formalizing the description of systems; this guides the subsequent description of various models of effective connectivity. Attention is focused particularly on dynamic causal modeling (DCM), a Bayesian system identification approach which distinguishes between neuronal state equations and biophysical forward models, as well as methods for Bayesian model selection.
- Synaptic plasticity;
- Dynamic causal modeling (DCM);
- Granger causality;
- Psychophysiological interactions;
- Structural equation modeling;
- Bayesian model selection