Bernal S D_2010.pdf - University of Plymouth

6.J. ANALYSIS OFRBSULTS
model could benefit from greater interactions among the lower level scale hands, which are
currently processed in parallel. Purthennore, the HMAX design doesn't take into account the
constraints of Bayesian networks, which may perform more efficiently using, for example, a
smaller number of stales per node.
Bayesian methods, such as the Expectation-Maximization algorithm, allow the optimum struc­
ture and parameters of a Bayesian network to he learned, given some data (Jordan and Weiss
2002. Lewicki and Sejnowski 1997, Murphy 2001), Although applying these methods from
scratch to such large scale mtxlels might be computationally inlraclable, these can be used to
shape the network given some initial structural constraints. The proposed model could poten­
tially be formulated in a more generic format, similar to the HTM model (George and Hawkins
2009), which could then be panicularized to specific scenarios with the aid of these Bayesian
learning methcxls. The proposed model can be understood as a particularization of the more
general model to the visual perception domain. However, the same generic model could be
particularized to other similarly structured domains such as the auditory system.
For example, one of the main properties embodied by the generic model would be the simple
and complex layer structure with complex layers grouping states in order to achieve invariance.
Many of the potential generic principles have been described in Chapter 4. but a more detailed
account and mathematical formulation of the generic framework is left as future work.
Several approximations and sampling methods, summarized in Section 4.6, have been imple­
mented to deal with the large number of nodes and connections in the model. These offer
solutions to the problem of multiplicativcly combining a large number of discrete probability
distributions with many stales. Previous models have proposed performing calculations in the
log domain to convert products into sums (Rao 2004, Lilvak and Ullman 2009). Here I propose
re-weighting distributions to establish a minimum value and sampling methods to keep only the
highest values of the distributions with highest variance.
A further novelty of the model is to use the weighted sum model proposed by Das (2004) to
approximate the CPT of nodes with multiple parents. Bayesian networks that try to model the
visual cortex will irremediably require multiple parent interactions as this arise as a consequence
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