COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 173
FIGURE 5.12 Temporal and spatial scales of neuroscience research. SOURCE: Courtesy of Christof Koch, Caltech.
perception, and imaging. At this level, computational analysis of nervous system networks and
(connectionist) modeling of psychological processes is the primary focus.
Computational neuroscience provides the basis for testing models of the nervous system’s func-
tional processes and their mechanisms, and computational modeling at several levels of detail is impor-
tant, depending on the purposes of a given effort. Box 5.16 describes simulators that operate at different
levels of detail for different purposes.
5.4.5.2 Large-scale Neural Modeling^98
To better understand a system as complex as the human brain, it is necessary to develop techniques
and tools for supporting large-scale, similarly complex simulations. Recent advances in understanding
how single neurons represent the world,^99 how large populations of neurons cooperate to build more
complex representations,^100 and how neurobiological systems compute functions over their representa-
tions make large-scale neural modeling a highly anticipated next step.
(^98) Section 5.4.5.2 is based largely on material supplied by Chris Eliasmith, University of Waterloo, September 7, 2004.
(^99) F. Rieke, D. Warland, R. de Ruyter van Steveninick, and W. Bialek, Spikes: Exploring the Neural Code, MIT Press, Cambridge,
MA, 1997; D. Warland, M. Landolfa, J. Miller, and W. Bialek, “Reading Between the Spikes in the Cercal Filiform Hair Receptors
of the Cricket,” Analysis and Modeling of Neural Systems, F. Eeckman, ed., Kluwer Academic Publishers, Boston, MA, 1992.
(^100) L. Abbott and T. Sejnowski, Neural Codes and Distributed Representations: Foundations of Neural Computation, MIT Press,
Cambridge, MA, 1999; R.S. Zemel, P. Dayan, and A. Pouget, “Probabilistic Interpretation of Population Codes,” Neural Computa-
tion 10, 1998.