Title: Multi-layer DCA, Data Manifolds, Statistical Physics and Visual Processing
in the Brain
Redwood Neuroscience
Abstract:
I will discuss high-dimensional probability density
estimation from the point of view of Dependent Component Analysis (DCA). First
I will show the general structure of this algorithm. Then I will show results
of 'Subspace ICA' and 'Topographic ICA' (two DCA algorithms)on
natural images, showing, as Hyvarinen & Hoyer
did, how the results relate to the computation of area V1 of cortex. Next, I
will show how to turn Topographic DCA into a multi-layer algorithm, and
hopefully present results. Finally, I will address the (ever present) relations
with statistical physics and manifold interpolation and point to some
intriguing research directions.