Bernal S D_2010.pdf - University of Plymouth

3.4. EXISTING MODELS
such as mulliply-connected networks, and a shared dictionary of low-level features (Epshlein
et al. 2008); nr models exclusively higher level phenomena such as allemion, relying on non-
Bayesian object recognition models (Chikkerur et al. 2009).
The results of model simulations on real-world data arc still limiied. Some models remain
purely theoretical (Lee and Mumford 200.1), or provide simple toy examples (I'riston and Kiebel
2009, Lewicki and Sejnowski 1997, Hinton et al. 2006). Those that use bigger and more
complex input images fail to account for certain aspects of object perception, such a.s posi­
tion and scale invariance (Rao and Ballard 1997, Murray and Kreutz-Delgado 2(X)7, Chikkerur
el al. 2009), or feedback reconstruction (e.g. illusory contour completion) (llinton el al. 2006,
Epshtein et al. 2008); or are not implemeniing rigorous. Iheorelically-grounded generative mod­
els (George and Hawkins 2009).
Generative mcxiels have been described as the iicxi generation of neural networks (Hinlon et al.
2006). However, their application to visual perception using realistic data is still at a very early
stage. Much work needs to be done exploring the different approximate inference methods,
network .structures, learning meih(Kls and scalability of these networks, which allow them lo
deal with natural image statistics and capture the wide variety of perceptual phenomena, while
using realistic physiological parameters.
3.4.3 Cortical mapping of models
The homogeneous, local and distributed implementation of belief propagation in graphical mod­
els is reminiscent of the concept of u canonical local circuit that has been suggested to exist in
the mammalian cortex. These ubiquiiious circuits, shared by many species and cortical areas,
are repeated within ct)rtical columns of a few hundred microns, which contains neurons with
similar feature tuning properties. Several studies have focused on a theoretically precise map­
ping between the local structures of graphical mcxlels and the layered cortical structure within a
cortical column. These also describe the intercorllcal projections which lead to the larger scale
fundi onahty.
Two of the cornerstone studies thai have set the theoretical grounds for understanding cortical
compulation within the hierarchical Bayesian inference framework (Lee and Mumford 2003,
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