Before revision:  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.
After revision:  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 experimental data. Such methods of `predictive connectomics' estimate 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.