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fallen in and out of fashion, from Rosenblatt's early work on the perceptron (Rosen-
blatt
1962
), to Minsky and Papert's critique (Minsky and Papert
1969
), and to the
later successful development of non-linear models using backpropagation and self-
organisation.
A number of artificial neural network applications already noted showed only
limited success as either a fitness function or a standalone machine evaluation sys-
tem. It would be premature to conclude such use in has hit a permanent plateau.
But it would be glib to suggest that since the brain is a neural network that the
successful use of artificial neural networks for computational aesthetic evaluation
is inevitable. The brain's 10
15
neural connections and presently unknown glial cell
capacity presents a daunting quantitative advantage artificial systems will not match
any time soon.
Perhaps a better understanding of natural neurology and subsequent application
to connectionist technologies can help overcome what present artificial systems lack
in quantity. This is the approach Jeff Hawkins has taken in the development of hier-
archical temporal memory.
10.3.6 The Neocortex and Hierarchical Temporal Memory
Hawkins has proposed the
hierarchical temporal memory model
for the functional-
ity found in the neocortex of the brain. He proposes that this single mechanism is
used for all manner of higher brain function including perception, language, creativ-
ity, memory, cognition, association, and so on. He begins with a typical hierarchical
model where lower cortical levels aggregate inputs and pass the results up to higher
levels corresponding to increasing degrees of abstraction (Hawkins and Blakeslee
2004
).
Neurologists know that the neocortex consists of a repeating structure of six lay-
ers of cells. Hawkins has assigned each layer with functionality consistent with the
noted multi-level hierarchical structure. What Hawkins has added is that within a
given level higher layers constantly make local predictions as to what the next sig-
nals passed upward will be. This prediction is based on recent signals and local
synapse strength. Correct predictions strengthen connections within that level. Thus
the neocortex operates as a type of hierarchical associative memory system, and it
exploits the passage of time to create local feedback loops for constant training.
Artificial hierarchical temporal memory has been implemented as software called
NuPIC
. It has been successfully demonstrated in a number of computer vision ap-
plications where it can robustly identify and track moving objects, as well as extract
patterns in both physical transportation and website traffic (Numenta
2008
). To date
NuPIC seems to work best when applied to computer vision problems, but others
have adapted the hierarchical temporal memory model in software for temporal pat-
terns in music (Maxwell et al.
2009
).
