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This paper describes fundamental methodological challenges faced by computational neuroscience. Modeling strategies intended for consolidating extensive knowledge and data into single predictive models are inappropriate for much of neuroscience at this stage in its evolution. Instead, models designed to explore the implications of guesses or suppositions are more likely to have utility in supporting experimental studies, either by deriving added insight from available data or by suggesting experiments that otherwise might not be performed. Such computational experiments, if they are to be useful, must be based upon models with a careful balance between solid data and supposition. The implications of this requirement for two major modeling strategies, simulation modeling and parametric data modeling, are examined.

Originally published in: American Zoologist, v. 33, no. 1, 1993, pp. 8-15.

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