Information Technology Reference
In-Depth Information
Chapter 1
Introduction and Overview
color images. These amazing images, together with the
results from many other important techniques, have ad-
vanced our understanding of the neural bases of cogni-
tion considerably. We can consolidate these various dif-
ferent approaches under the umbrella discipline of
cog-
nitive neuroscience
, which has as its goal answering
this most important of scientific questions.
Cognitive neuroscience will remain a frontier for
many years to come, because both thoughts and brains
are incredibly complex and difficult to understand. Se-
quences of images of the brain thinking reveal a vast
network of glowing regions that interact in complex
ways with changing patterns of thought. Each picture
is worth a thousand words — indeed, language often
fails us in the attempt to capture the richness and sub-
tlety of it all.
Computational
models based on bio-
logical properties of the brain can provide an impor-
tant tool for understanding all of this complexity. Such
models can capture the flow of information from your
eyes recording these letters and words, up to the parts of
your brain activated by the different word meanings, re-
sulting in an integrated comprehension of this text. Al-
though our understanding of such phenomena is still in-
complete, these models enable us to explore their under-
lying mechanisms, which we can implement on a com-
puter and manipulate, test, and ultimately understand.
This topic provides an introduction to this emerging
subdiscipline known as
computational cognitive neu-
roscience
: simulating human cognition using biologi-
cally based networks of neuronlike units (
neural net-
works
). We provide a textbook-style treatment of the
central ideas in this field, integrated with computer sim-
Contents
1.1 ComputationalCognitiveNeuroscience......
1
1.2 Basic Motivations for Computational Cognitive
Neuroscience .................... 2
1.2.1 PhysicalReductionism ........... 2
1.2.2 Reconstructionism.............. 3
1.2.3 LevelsofAnalysis.............. 4
1.2.4 ScalingIssues................ 6
1.3 HistoricalContext ................. 8
1.4 OverviewofOurApproach ............ 10
1.5 General Issues in Computational Modeling .... 11
1.6 Motivating Cognitive Phenomena and Their Bi-
ologicalBases.................... 14
1.6.1 Parallelism ................. 15
1.6.2 Gradedness ................. 15
1.6.3 Interactivity ................. 17
1.6.4 Competition ................. 17
1.6.5 Learning................... 18
1.7 OrganizationoftheBook ............. 19
1.8 FurtherReading ..................
20
1.1
Computational Cognitive Neuroscience
How does the brain think? This is one of the most
challenging unsolved questions in science. Armed with
new methods, data, and ideas, researchers in a variety
of fields bring us closer to fully answering this question
each day. We can even watch the brain as it thinks, using
modern neuroimaging machines that record the biolog-
ical shadows of thought and transform them into vivid
1
