Bernal S D_2010.pdf - University of Plymouth

J.I. OVERVIEW
retinal stimulation (e.g. Kanizsa figures), as reported in functional magcntic resonance imaging
(fMRIKMaertens el al. 2008). eleclroenceph;ilography (EEG) (Seghier and Vuilleumier 2006),
magnetoencephalography (MliG) (Halgren el al. 2003) and single-cell recording (Lee 2003, Lee
and Nguyen 2001) studies. The experiments show illusory contour-related activity emerging
firsl in Lateral Occipital Corlcx (LOC). then in V2 and finally in VI. strongly suggesting that
the response is driven by feedback (Lee and Nguyen 2(K)l, Murray et al. 2002).
While there is relative agreement that feedback connections play a role in integrating global
and l(K-al information from different cortical regions to generate an integrated percept (Bullier
2001, Lee 2003). several differing approaches have attempted to explain the underlying mech­
anisms. Generative models and the Bayesian brain hypothesis provide a framework thai can
quantitatively model the interaction between prior knowledge and sensory evidence, in order to
represent the physical and statistical properties of the environment. This framework provides
an elegant interpretation of how bottom-up and top-down inlbmiation across different cortical
regions can be combined to obtain an integrated percept.
Increasing evidence supports the proposal that Bayesian inference provides a theoretical frame­
work that maps well onto cortical connectivity, explains botli psychophysical and neurophysio-
logical results, and can be used to build biologically plausible models ofbrain function (Friston
and Kiebel 2009. Dayan et al. 1995. Knill and Richards 1996. Geislcr and Kerslen 2002, Ko-
rding and Wolperl 2004, Yuille and Kerslen 2006. Deneve 2008a). Within this framework,
Bayesian networks and belief propagation provide a rigorous mathematical implementation of
these principles. Belief propagation has been found to be particularly well-suited for neural im­
plementation, due to its hierarchical distributed organization and homogeneous internal struc­
ture and operations (George and Hawkins 2009, Lee and Mumford 2003. Rao 2006, l.itvak and
Ullman 2009, Steinier el al. 2009).
The present study explores the role of feedback in object perception, taking as a starting point
the HMAX model, a biologically inspired hierarchical model of object recognition (Riesenhu-
ber and Poggio 1999, Serre et al. 2007b). and extending il lo include feedback connectivity.
By replacing the classical deterministic view with a probabilistic interpretation, a Bayesian net-