Bernal S D_2010.pdf - University of Plymouth

• The HTM paliems correspond lo the features coded HMAX simple units. We assume
simple units with a different relative location to the complex unit, represcnl a different
HTM pattern.
Thus, HTM nodes embody both the simple and complex features, which are called coincidence
patterns and groups (Markov chains), respectively. The inclusion of the groups within the node
makes HTM qualitatively different from a Bayesian network. Consequently, belief propagation
also becomes a qualitatively different algorithm that can be applied exclusively lo HTM nodes.
By combining simple and complex features within the same node, the authors avoid much of
the complexity inherent in a rigorous implementation of belief propagation, such as loops and
muliiple parents. The resulting HTM network can be compared lo the Bayesian network that
implements the same 3-level HMAX model (Figure 4.4) in order to obtain a better understand­
ing of the differences between HTM and the proposed model.
266