Bernal S D_2010.pdf - University of Plymouth

6.5. CONCLUSIONS AND SUMMARY Ut CONTRIBUTIONS
6.5 Conclusions and summary of contributions
It is important to highlight that ihe claim made in this thesis is not that the visual cortex works
exactly as a Bayesian network with belief propagation. However, ihe substantial body of evi­
dence presented and the model result.s suggest that, at a functional and structural level of de­
scription, there exist significant similarities between the visual cortex and Ihe proposed model.
Therefore, this thesis supports the notion that the role for feedback is not limited to attentional
mechanisms, but provides a substrate for the exchange of information across the visual system
leading to hierarchical perceptual inference. This thesis provides an explicit demonstration that
Bayesian networks and belief propagation can be used as tools to model large-scale perceptual
processes in the visual system. In this sense, it complements previous theoretical studies that
argued for this approach (Lee 2003, I'riston 2010) but did not provide an explicit implementa­
tion. Al the same lime, ii complements small-scale biologically plausible implementations of
belief propagation (Lilvak and Ullman 2009. Sleimer et al. 2009), by providing them with a
large-scale functional model which they can allempi to reproduce. The proposed model can be
used as a template to guide the design of large-scale biologically plausible implementations of
belief propagation that capture the venlral path functionality.
A list of the contributions of this thesis is included below;
• A review and analysis of the experimental evidence, theories and computational models
of the role of cortical high-level feedback in object perception, including the illusory and
occluded contours.
• A review and analysis of the experimental evidence, theories and computationaJ models
suggesting the visual cortex can be understood in terms of a generative model, Bayesian
networks and belief propagation, This includes a detailed comparison of existing func­
tional models, biologically plausible implementations and possible cortical mappings.
• A comprehensive and mathematically rigorous explanation of belief propagation in Bayesian
networks, including a novel, intuitive and illustrative example with numerical slep-hy-
step demonstrations of the different types of evidence propagation.
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