Bernal S D_2010.pdf - University of Plymouth

3.4. EXISTING MODELS
representations (suggested by experimental evidence), which are enforced during the learning
stage, and improve recognition performance.
A four-layer network with a 64x64 pixel input image simulates object recognition in the vi­
sual system. The network managed to correctly recognize, segment and reconstruct occluded
versions of the trained images, although no invariance to position and size transformations is
achieved. The study illustrates some interesting properties, such as the possibility of simulat­
ing imafiinalion by running the network generatively (i.e. top-down and not bottom-up input);
and expeclalion-driven segmentation, whereby the top-down input (e.g. prior expectations) im­
proves recognition in cluttered scenes. However, the model fails to provide mechanisms for
position and scale invariance during recognition. Furthermore, despite being based on a gener­
ative model, the resulting dynamic network derived from the simplified model is far from the
original belief propagation scheme.
3.4.2.4 Comparison and umclusions
This subsection has outlined some of the attempts to model visual perception in the brain using
the generative modelling approach, and in particular those employing algorithms similar to be­
lief propagation. Table ^.2 lists the models, comparing the type of network, inference algorithm
and results obtained in each case.
The complexity that emerges from the large-scale and intricate cortical connectivity means ex­
act inference methods are intractable, making it necessary to use approximate solutions such as
loopy belief propagation (George and Hawkins 2009), sampling methods (Hinton et al. 2006,
Lee and Mumford 2003. Lewicki and Sejnowski 1997) or variational methods (Murray and
Kreut/.-Dclgado 2007, Kao and Ballard 1999.1-riston 2010). Sampling methods typically main­
tain the probabilistic nature and structure of Bayesian networks, while variational approxima­
tion methods yield a hierarchical dynamic network which deals with the optimization problem
(minimizing the difference between the approximate and Uie true posterior distributions). Nev­
ertheless, in both cases the resulting dynamics lead lo local, recursive message-passing scheme.s
reminiscent of belief propagation.
Exact inference is only possible when the generative model avoids physiological constraints,
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