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standing them in light of a common set of basic princi-
ples. For example, our models of the learning of seman-
tic representations of word meanings and the encoding
of basic features of visual structure exploited virtually
identical principles and network structures. This link
opens up the possibility for empirical data in these two
very different domains to be related and provide mutual
understanding.
The coherent set of principles that we have exploited
in this topic and incorporated into the Leabra algorithm
have been around for a while, and have each been vali-
dated a number of times in different implementations.
However, in using a single algorithm for this entire
topic we are not implying that everyone in computa-
tional cognitive neuroscience should use one particu-
lar algorithm, or that this algorithm has some privileged
connection with the truth. Nevertheless, we do believe
that this collection of principles is important, and, when
combined together in an appropriate fashion, can help
us to understand a wide range of phenomena, from ion
channels to language processing to higher-level cogni-
tive skills like planning.
Undoubtedly, better performance in one way or an-
other could be obtained in specific cases by using a dif-
ferent implementation or a different set of principles.
However, we do not at this time know of any obvious
alternative algorithm or set of principles that could pro-
vide as good a fit to such a wide range of performance
criteria as the ones used in this topic. As such alterna-
tives emerge, we will gladly adopt them for subsequent
editions of this topic!
12.6
Exploring on Your Own
We hope that you are now filled with a sense of balanced
optimism and general excitement for the endeavor of
computational cognitive neuroscience! We close by en-
couraging you to continue in the exploration of com-
putational cognitive neuroscience by constructing your
own models. The following appendixes provide a start-
ing tutorial, and the many models in the various chap-
ters can be used as points of departure for exploring
other related phenomena. Enjoy!
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