Bernal S D_2010.pdf - University of Plymouth

Appendix A
HMAX as a Hierarchical Temporal Memory net­
work
A Hierarchical Temporal Memory (HTM) network can be specirted mathemalically as a gener­
ative model and is dchned by llie following parameters:
• HTM n(xies N'-'. where L = level of the hierarchy, and i - index of the node within ihal
level.
• Each node contains a set of patterns C],...,CM and a set of groups/Markov chains gi, ...,gA',
each of which is defined over a subset of the coincidence patterns in that node.
• Connectivity between child and parent nodes which defines the structure boiiom-up mes­
sages A and top-down messages 7C of each node during belief propagation.
Figure A.l provides a schematic representation of how an HTM network could impiemenl the
3-level HMAX model (Serre el al. 2007c). The diagram define,s all the above parameters for an
HTM network that captures the structure and connectivity of the 3-level HMAX implemenia-
lion. The parameters of the HTM network are described as a function of the paramelers of the
HMAX model, using the same noialion as in Table 4.2. To summarize:
• Each HTM node corresponds to all the HMAX complex units at a specific location, and
all of its afferent simple units.
• The HTM groups correspond to each of the features coded by the HMAX complex units
al that location.
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