Bernal S D_2010.pdf - University of Plymouth

Chapter 3
Bayesian networks and belief propagation
As described in Chapter 2, ihe classical feedforward processing model fails lo capture many
observed neurophysiological phenomena, and thus is gradually being replaced by a more global
and integrative approach which relies on feedback connections. However, theoretical and com­
putational models stiil strive lo accommodate feedback connections and the dilTerenl observed
conlexlual effect.s within a single general theorelica! framework. The probabilistic inference
approach described in this chapter attempts to solve this problem. Results presented in this the­
sis are based on this methodological approach, and more specifically on belief propagation in
Bayesian networks. Thus, Section 3.1 offers an introduction lo Generative models and Bayesian
inference, providing the theoretical background and roots of this approach. Section 3.2 reviews
evidence that supports this framework as being a good candidate for modelling the visual cor­
tex. Section 3.3 dehnes and formulates mathematically both Bayesian networks and the belief
propagation algorithm, and includes an illustrative example. Finally, existing theoretical and
computational models based on belief propagation are described in Section 3.4.
3.1 The Bayesian brain hypothesis
3.1.1 Generative models
It has long been appreciated that information falling on the rclina cannot be mapped unambigu­
ously back onto the real-world; very different objects can give rise to similar retinal stimulation,
and the same object can give rise to very different retinal images. So how can the brain perceive
and understand the outside visual world based on these ambiguous two-dimensional retinal im­
ages? A possible explanation comes from the generative modelling approach, which has as
its goal Ihe mapping of external causes to sensory inputs. By building internal models of the
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