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Hierarchical Models in the Brain.
- Source :
-
PLoS Computational Biology . Nov2008, Vol. 4 Issue 11, p1-24. 24p. 5 Diagrams, 1 Chart, 5 Graphs. - Publication Year :
- 2008
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1553734X
- Volume :
- 4
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- 45150086
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1000211