Bernal S D_2010.pdf - University of Plymouth

6.t. ANALYSIS OF RESULTS
used 10 compare the results, which ;illows for a maximum of 50 object calegories. Ahhough
this means the lesl sets were different from those used for the rest of models, theoretically it
confers an advantage lo the HTM model as fewer categories facilitates the categorization task.
Nonetheless, the relatively low performance of the mode! might be a consequence of noi hav­
ing enough training images per category, as the Numenla Vision Toolkit recommends having at
least 20 training images per categor)', I^urthcnnore. the iniemal struclure of the HTM network
is unknown, which means it is possible that this was no! optimized for the type of images or
categorization task employed, and alternative HTM networks could improve the results. De­
spite this, the results are intended to illustrate dial it is not trivial ihal the task of feedforward
categorization has been performed by belief propagation models that incorporate feedback func­
tionality.
6.1.2 Feedback modulation and illusory contour completion
To test the effects of feedback in the network, the illusory contour completion paradigm was
chosen. Experimental evidence strongly suppons the involvement of high-level feedback in
lower-level illusory contour development (Halgren etal. 2003, Lee and Nguyen 2001, Maertens
et al. 2008). To try lo reproduce this phenomenon, the setup was typically chosen to be a
Kanizsa square as the inpul image lo Ihe network and the representaiion of a square fixed at some
higher layer. The square representaiion was fed back from increasingly higher layers, ranging
from CI to S3, This was done in order to study the effects of feedback systematically and
understand the particularities of each layer, although in las! instance feedback should arise from
the lop layer after the Kani/.sa image has been categorized as a square. Results are structured in
the same way. providing a progressive account of the network's recurrent dynamics.
6.1.2.1 Feedback from CI to SI
The first and most simple case (Figure 5.18) is that of feedback originating from the CI layer.
This example serves to clearly illustrate how boitom-up (A (SI)) and top-down (Jt(Sl)) evidence
are multiplicatively combined in the SI layer belief. Furthermore, it clarifies the correspondence
between ihe probability distributions of Ihe Bayesian nodes and the 2D graphical representations
used throughout the Results chapter. Note that only the lower scale band of each layer is plotted,
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