  For constructing neuronal network models computational neuroscientists have
access to wide-ranging anatomical data that nevertheless tend to cover only a
fraction of the parameters to be determined. Finding and interpreting the most
relevant data, estimating missing values, and combining the data and estimates
from various sources into a coherent whole is a daunting task. With this
chapter we aim to provide guidance to modelers by describing the main types of
anatomical data that may be useful for informing neuronal network models. We
further discuss aspects of the underlying experimental techniques relevant to
the interpretation of the data, list particularly comprehensive data sets, and
describe methods for filling in the gaps in the experimental data. Such methods
of `predictive connectomics' estimate connectivity where the data are lacking
based on statistical relationships with known quantities. It is instructive,
and in certain cases necessary, to use organizational principles that link the
plethora of data within a unifying framework where regularities of brain
structure can be exploited to inform computational models. In addition, we
touch upon the most prominent features of brain organization that are likely to
influence predicted neuronal network dynamics, with a focus on the mammalian
cerebral cortex. Given the still existing need for modelers to navigate a
complex data landscape full of holes and stumbling blocks, it is vital that the
field of neuroanatomy is moving toward increasingly systematic data collection,
representation, and publication.
