For constructing neuronal network models computational neuroscientists have access to a wealth of 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 give modelers a helping hand by describing the main types of anatomical data that may be useful for informing neuronal network models , aspects of the underlying experimental techniques relevant to their interpretation , particularly comprehensive datasets, and methods for filling in the gaps in the data. Estimating connectivity where the data are lacking generally relies on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of URLanization that are likely to influence predicted neuronal network dynamics, with a focus on  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.
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 URLanizational 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 URLanization 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.
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