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**These are excerpts and elaborations from my book "The Nature of Consciousness"**

This school of thought
merged with another one that was coming from a background of statistics and
neuroscience. The Swedish statistician Ulf Grenander (who in 1972 had
established the Brown University Pattern Theory Group) fostered a conceptual revolution
in the way a computer should describe knowledge of the world: not as concepts
but as patterns. His "general pattern theory" provided mathematical
tools for Identifying the hidden variables of a data set. Grenander's pupil
David Mumford studied the visual cortex and came up with a hierarchy of modules
in which inference is Bayesian and it is propagated both up and down ("On
the computational architecture of the neocortex II", 1992). a feedforward
chain of modules in successively higher The assumption was that
feedforward/feedback loops in the visual region integrate top-down expectations
and bottom-up observations via probabilistic inference. Basically, Mumford
applied hierarchical Bayesian inference to model how the brain works. Hinton's Helmholtz machine of 1995 was de facto an
implementation of those ideas: an unsupervised learning algorithm to discover
the hidden structure of a set of data based on Mumford's and Grenander's ideas.
The hierarchical Bayesian framework was later refined with Tai Sing Lee of
Carnegie Mellon University ("Hierarchical Bayesian inference in the visual
cortex", 2003). These studies were also the basis for the
widely-publicized "Hierarchical Temporal Memory" model of the startup
Numenta, founded in 2005 in Silicon Valley by Jeff Hawkins, Dileep George and Donna Dubinsky. It was another path to get to the
same paradigm: hierarchical Bayesian belief networks. Back to the beginning of the chapter "Connectionism and Neural Machines" | Back to the index of all chapters |