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CATEGORIES:Computational Neuroscience
SUMMARY:Computational Neuroscience Journal Club - Richard
Turner (University of Cambridge)
DTSTART;TZID=Europe/London:20100223T160000
DTEND;TZID=Europe/London:20100223T170000
UID:TALK23108AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/23108
DESCRIPTION:Statistical modeling of photographic images E P Si
moncelli\, Handbook of Image and Video Processing\
,pages 431--441. Academic Press\, May 2005.\n\n\nO
ver the last 15 years it has been increasingly pop
ular to model the visual cortex as performing infe
rence in a latent variable model for image statist
ics. The basic idea is that visual scenes consist
of structural primatives\, like local edges at par
ticular orientations and scales. In any one image
only a relatively small number of these primitives
are assumed to be present\, and so these models e
mploy sparse latent variables that describe the pr
obability of occurrence. Cortex is then modelled a
s representing the posterior distribution over the
se latent variables\, given the retinal image.\n\n
In this journal club\, I will review this modellin
g work\, starting with independent component analy
sis\, and sparse coding\, which have been used to
model simple cells. I will then describe their gen
eralisation to Gaussian Scale Mixtures\, which hav
e been used to model both simple and complex cells
. Finally I hope to describe the most recent work
on these models by both Mike Lewicki and Eero Simo
ncelli. One of the main aims will be to assess thi
s modelling work whilst wearing a machine learning
hat.
LOCATION:Cambridge University Engineering Department\, Oatl
ey Seminar Room 2
CONTACT:Dr Jean-Pascal Pfister
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