Name
Abstract | ||
|---|---|---|
This paper describes a general model that subsumes many parametric models for continuous data. The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another. The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity. Special cases range from the general linear model for static data to generalised convolution models, with system noise, for nonlinear time-series analysis. Crucially, all of these models can be inverted using exactly the same scheme, namely, dynamic expectation maximization. This means that a single model and optimisation scheme can be used to invert a wide range of models. We present the model and a brief review of its inversion to disclose the relationships among, apparently, diverse generative models of empirical data. We then show that this inversion can be formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1371/journal.pcbi.1000211 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
optimization,kalman filter,neuronal plasticity,covariance,general linear model,hierarchical model,state space,algorithms,convolution,neural network,parametric model,dynamical systems,nonlinear dynamics,expectation maximization,motion,linear models,probability | Parametric model,Bayesian inference,Linear model,General linear model,Expectation–maximization algorithm,Computer science,Algorithm,Data type,Artificial intelligence,Artificial neural network,Genetics,Causal model | Journal |
Volume | Issue | ISSN |
4 | 11 | 1553-7358 |
Citations | PageRank | References |
106 | 5.87 | 14 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karl Friston | 1 | 776 | 49.34 |