Hierarchical models in the brain. Export

@article{citeulike:3561412, 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.}, author = {Friston, Karl}, citeulike-article-id = {3561412}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000211}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18989391}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18989391}, day = {7}, doi = {10.1371/journal.pcbi.1000211}, issn = {1553-7358}, journal = {PLoS computational biology}, keywords = {brain, modelling}, month = {November}, number = {11}, pages = {e1000211+}, posted-at = {2009-03-10 10:57:56}, priority = {2}, publisher = {Public Library of Science}, title = {Hierarchical models in the brain.}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000211}, volume = {4}, year = {2008} }

TY - JOUR ID - citeulike:3561412 L3 - citeulike-article-id:3561412 N2 - 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. IS - 11 JF - PLoS computational biology SN - 1553-7358 TI - Hierarchical models in the brain. PB - Public Library of Science VL - 4 SP - e1000211 KW - brain KW - modelling AU - Friston, Karl PY - 2008/11/07/ UR - http://dx.doi.org/10.1371/journal.pcbi.1000211 ER -