Abstract
Theoretical studies of the human brain require mathematical models. Unfortunately, mathematical analysis of brain models is of limited value, since the results can depend on particulars of an underlying model; that is, various models of the same brain structure could produce different results. This could discourage biologists from using mathematics and/or mathematicians. A reasonable way to circumvent this problem is to derive results that are largely independent of the model and that can be observed in a broad class of models. For example, if one modifies a model by adding more parameters and variables, similar results should hold.
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© 1997 Springer Science+Business Media New York
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Hoppensteadt, F.C., Izhikevich, E.M. (1997). Introduction to Canonical Models. In: Weakly Connected Neural Networks. Applied Mathematical Sciences, vol 126. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1828-9_4
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DOI: https://doi.org/10.1007/978-1-4612-1828-9_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-7302-8
Online ISBN: 978-1-4612-1828-9
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