Hierarchical models in the brain.

Hierarchical models in the brain.

Blog Article

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 bl2420pt 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 royal nomadic 5413 rug be formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain.