Introduction and Overview - Computational Explorations in Cognitive Neuroscience - page 11

this algorithm plays an important role in our integrated
framework by allowing us to use the principle of back-
propagation learning without conflicting with the desire
to take the biology seriously.
Another long-standing theme in neural network mod-
els is the development of inhibitory competition mecha-
nisms (e.g., Kohonen, 1984; McClelland & Rumelhart,
1981; Rumelhart & Zipser, 1986; Grossberg, 1976).
Competition has a number of important functional ben-
efits emphasized in the GRAIN framework (which we
will explore in chapter 3) and is generally required for
the use of Hebbian learning mechanisms. It is tech-
nically challenging, however, to combine competition
with distributed representations in an effective manner,
because the two tend to work at cross purposes. Never-
theless, there are good reasons to believe that the kinds
of sparse distributed representations that should in prin-
ciple result from competition provide a particularly ef-
ficient means for representing the structure of the nat-
ural environment (e.g., Barlow, 1989; Field, 1994; Ol-
shausen & Field, 1996). Thus, an important part of our
framework is a mechanism of neural competition that
is compatible with powerful distributed representations
and can be combined with interactivity and learning in
a way that was not generally possible before (O'Reilly,
1998, 1996b).
The emphasis throughout the topic is on the facts
of the biology, the core computational principles just
described, which underlie most of the cognitive neu-
ral network models that have been developed to date,
and their interrelationship in the context of a range of
well-studied cognitive phenomena. To facilitate and
simplify the hands-on exploration of these ideas by the
student, we take advantage of a particular implementa-
tional framework that incorporates all of the core mech-
anistic principles called Leabra ( l ocal, e rror-driven and
a ssociative, b iologically r ealistic a lgorithm ). Leabra is
pronounced like the astrological sign Libra, which em-
phasizes the balance between many different objectives
that is achieved by the algorithm.
To the extent that we are able to understand a wide
range of cognitive phenomena using a consistent set of
biological and computational principles, one could con-
sider the framework presented in this topic to be a “first
draft” of a coherent framework for computational cog-

nitive neuroscience. This framework provides a useful
consolidation of existing ideas, and should help to iden-
tify the limitations and problems that will need to be
solved in the future.
Newell (1990) provided a number of arguments in fa-
vor of developing unified theories of cognition, many
of which apply to our approach of developing a co-
herent framework for computational cognitive neuro-
science. Newell argued that it is relatively easy (and
thus relatively uninformative) to construct specialized
theories of specific phenomena. In contrast, one en-
counters many more constraints by taking on a wider
range of data, and a theory that can account for this
data is thus much more likely to be true. Given that our
framework bears little resemblance to Newell's SOAR
architecture, it is clear that just the process of making
a unified architecture does not guarantee convergence
on some common set of principles. However, it is clear
that casting a wider net imposes many more constraints
on the modeling process, and the fact that the single set
of principles can be used to model the wide range of
phenomena covered in this topic lends some measure of
validity to the undertaking.
Chomsky (1965) and Seidenberg (1993) also dis-
cussed the value of developing explanatory theories that
explain phenomena in terms of a small set of indepen-
dently motivated principles, in contrast with descriptive
theories that essentially restate phenomena.

1.5 General Issues in Computational Modeling

The preceding discussion of the benefits of a unified
model raises a number of more general issues regarding
the benefits of computational modeling 1 as a method-
ology for cognitive neuroscience. Although we think
the benefits generally outweigh the disadvantages, it is
also important to be cognizant of the potential traps and
problems associated with this methodology. We will
just provide a brief summary of these advantages and
problems here.
1 We consider both models that are explicitly simulated on a com-
puter and more abstract mathematical models to be computational
models, in that both are focused on the computational processing of
information in the brain.