[Tyler]

Hierarchical Models in the Brain
     


The human brain has a unique ability to easily model all sorts of data,
allowing us to estimate things, predict things, identify things, and
learn things in
ways that would traditionally require the use of complex domain-specific
modeling algorithms if they were done by a computer or a scientist with
pencil and paper. A new paper, Hierarchical
Models in the Brain, by Karl
Friston of the Wellcome Trust Centre of Neuroimaging at the University
College of London, offers a possible explanation. He describes
hierarchical dynamic models and a generic method for their inversion. He
goes on to show that "the brain has evolved the necessary anatomical
and physiological equipment to implement this inversion, given sensory
data." This means the brain could use this single Bayesian mechanism
to implement a wide range of algorithms including models with
unknown parameters such as state-space models, probabilistic dynamic
models, static models, neural networks, nonlinear system identification,
general linear models, and identification of nonlinear dynamic systems;
as well as models with unknown states such as estimation with static
models, hierarchical linear observation model, covariance component
estimation, Gaussian process models, and deconvolution; and even models
with unknown states and parameters such as Principal Components Analysis
(PCA), factor analysis, probabilistic PCA, Independent component
analysis (ICA), sparse coding, and blind deconvolution. Obviously, this
paper has a lot of math in it, but even if you skip the math, it's an
interesting read.
   

http://robots.net/article/2689.html