Bernal S D_2010.pdf - University of Plymouth

4.3. LEARNING
when all afferent SI nodes have a flat A distribution, as in blank regionsof the image, the parent
CI node will also show a flal distribution.
In summary, the CI layer becomes an iniermediale step ihai converts combinations of SI fea­
tures and spatial arrangements into the stales of a single CI node. The max operation only oc­
curs during the generation of the output A messages lo the S2 layers, which groups these states
via the learned weight matrices. This methixl also provides a way to feed back information from
complex to simple layers, where each complex feature corresponds to a specific arrangement of
simple features. The method is equivalent to that employed by Hierarchical Temporal Networks
(George and Hawkins 2009). where features in each node are combined into temporal groups
or Markov chains. The meihixl used here, however, preserves the Bayesian network simciure
by implementing the grouping olT'eatures in the weights of the CPTs.
4.3.3 C1-S2CPTS
To learn the selectivity weights between layers CI and S2. the minimum distance algorithm is
employed. This algorithm was also used to extract the most common spatial patterns (equiva­
lent to selectivity weights) in the Hierarchical Temporal Memory mixlel (George and Hawkins
2009). In the HMAX model, the selectivity weights, or the prototypes which serve as centres for
the Radial Basis Functions, were extracted at random from the CI maps generated by the train­
ing images. However, in our mtxlel. the minimum distance algorithm provides better results, as
it ensures the extracted prototypes maximize the Euclidean distance between each other. The
algorithm works as follows:
I. All features, potential S2 prototypes Ppoieniiiih ^re extracted by sampling from all the
locations and bands of the A (CI) response generated for each of the training images, i.e.
A(Cli,_jy - () V h.x.y. The number of elements for each prototype is ANsi x AA'M X
(Kci/Kc]group)< i.c- the S2 RF size times the number of CI states divided by the states
per group. To learn the S2 prototypes, it is more eflicient to obtain a single value for
each C1 group by summing over all the features belonging to that group, [n other words,
although each CI node is composed of 40 stales, only 4 values, corresponding to the sum
of each group, are used lo compute the S2 prototypes.
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