The name is absent

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namics (Stefanescu and Jirsa, 2008). Although they make certain assumptions on
distributions of relevant parameters and the network architecture in order to make
the analysis tractable, they are able to recover a broad range of population behaviors
with a very small reduced system. While this is an important step, our model reduc-
tion goal is to be able to quantitatively reproduce the dynamics of individual cells in
the network.

In contrast to these approaches, which deal with simple models of single cells and
attempt to reduce the dimensionality of the network itself, the techniques in this
thesis can be applied to realistic single cell models. These reduced single cells can
then be simulated in a network in order to obtain the full network dynamics without
sacrificing properties of the individual neurons or of the input patterns, and thus this
thesis provides a new perspective on how to tackle the problem of model reduction
of networks.

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