Introduction and Overview - Computational Explorations in Cognitive Neuroscience - page 8

sentations. Thus, the population can encode much more
information (e.g., many finer shades of meaning), and
is probably different in other important ways (e.g., it
might be more robust to the effects of noise). A visual
analogy for this kind of scaling is shown in figure 1.3,
where the sharp, high-resolution detail of the original
(panel a) is lost in the scaled-down version (panel b),
but the basic overall structure is preserved.
Finally, we believe that the brain has a fractal char-
acter for two reasons: First, it is likely that, at least in
the cortex, the effective properties of long-range con-
nectivity are similar to that of local, short-range con-
nectivity. For example, both short and long-range con-
nectivity produce a balance between excitation and in-
hibition by virtue of connecting to both excitatory and
inhibitory neurons (more on this in chapter 3). Thus, a
model based on the properties of short-range connectiv-
ity within a localized cortical area could also describe a
larger-scale model containing many such cortical areas
simulated at a coarser level. The second reason is basi-
cally the same as the one given earlier about averaging
over populations of neurons: if on average the popula-
tion behaves roughly the same as the individual neuron,
then the two levels of description are self-similar, which
is what it means to be fractal.
In short, these arguments provide a basis for opti-
mism that models based on neurobiological data can
provide useful accounts of cognitive phenomena, even
those that involve large, widely distributed areas of the
brain. The models described in this topic substanti-
ate some of this optimism, but certainly this issue re-
mains an open and important question for the compu-
tational cognitive neuroscience enterprise. The follow-
ing historical perspective on this enterprise provides an
overview of some of the other important issues that have
shaped the field.

contribution to this field. Thus, the entire space of this
topic could be devoted to an adequate account of the
relevant history of the field. This section is instead in-
tended to merely provide a brief overview of some of
the particularly relevant historical context and motiva-
tion behind our approach. Specifically, we focus on the
advances in understanding how networks of simulated
neurons can lead to interesting cognitive phenomena,
which occurred initially in the 1960s and then again in
the period from the late '70s to the present day. These
advances form the main heritage of our approach be-
cause, as should be clear from what has been said ear-
lier, the neural network modeling approach provides a
crucial link between networks of neurons and human
cognition.
The field of cognitive psychology began in the late
1950s and early '60s, following the domination of the
behaviorists. Key advances associated with this new
field included its emphasis on internal mechanisms for
mediating cognition, and in particular the use of explicit
computational models for simulating cognition on com-
puters (e.g., problem solving and mathematical reason-
ing; Newell & Simon, 1972). The dominant approach
was based on the computer metaphor , which held that
human cognition is much like processing in a standard
serial computer.
In such systems, which we will refer to as “tra-
ditional” or “symbolic,” the basic operations involve
symbol manipulation (e.g., manipulating logical state-
ments expressed using dynamically-bound variables
and operators), and processing consists of a sequence
of serial , rule-governed steps. Production systems
became the dominant framework for cognitive model-
ing within this approach. Productions are essentially
elaborate if-then constructs that are activated when their
if-conditions are met, and they then produce actions that
enable the firing of subsequent productions. Thus, these
productions control the sequential flow of processing.
As we will see, these traditional, symbolic models serve
as an important contrast to the neural-network frame-
work, and the two have been in a state of competition
from the earliest days of their existence.
Even though the computer metaphor was dominant,
there was also considerable interest in neuronlike pro-
cessing during this time, with advances like: (a) the

1.3 Historical Context

Although the field of computational cognitive neuro-
science is relatively young, its boundaries are easily
blurred into a large number of related disciplines, some
of which have been around for quite some time. In-
deed, research in any aspect of cognition, neuroscience,
or computation has the potential to make an important