Bernal S D_2010.pdf - University of Plymouth

6.1. ANALYSIS OF RESULTS
of overlapping receptive fields. Several melhods have been proposed lo approximate the expo­
nential number of paramelers of mulliple-parenl CPTs, the most common being the Noisy-OR
gate (Peari 1988, Diez 1993, Srinivas 1993, Onisko el al. 2001). This method however cannot
be applied to variables that are not graded, such as those coding ihe different features as states
of the variable, lor this reason, Ihe proposal by Das (2004). which has been justified from a
geometrical perspeciive and is not constrained to graded variables, offers a valuable allemative.
The model also deals with loops in the nclwork by implementing loopy belief propagation, a
mcihixl thai has only been proven to work empirically and conslitules an active field of research
in itself (Murphy et al. 1999. Weiss 2000). The proposed model explores different belief up­
dating methods and provides a comparison of the effects the,se have on the diffcrenl layers over
time. Additionally, to the best of my knowledge, this is the largest Bayesian network Chat imple­
ments loopy belief propagation and thus tests the limits and applicability of this approach. An
alternative and potentially more efficient belief update method, which could be tested in future
versions of the model, is asynchronous message-passing triggered by changes in the input to a
node.
All of the above proposed meth(Hls an: likely to be useful in the future for researchers modelling
similar large-scale scen;u-ios using Bayesian networks and belief propagation. However, it is
difficult lo evaluate the validity of these methods and their ability to approximale the exact
beliefs of ihe network. The only way to obtain the exact marginal probabilities in networks with
loops is lo apply the junction-tree algorithm (Murphy etal. 1999), which would incur prohibitive
computational costs. Thus, while these melhods remain to be tested more systematically, the
categorization performance and the feedback reconstruction capabilities of ihe model suggest
Ihe proposed melhods point in the right direction. Furthermore, resulls from the setup where the
square representation is fed from the top layer suggest lateral contextual interactions between
the boltom-up input and feedback activity are present in Ihe model.
On ihe other hand, ihe fact thai these contexlual lateral interactions are not clcariy showing up
in the results where feedback originates from S2 and C2, could suggest that the approxima­
tions and sampling methods used are discarding necessary infonnation. as previously argued.
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