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Developmental programs that guide neurons and their neurites into specific subvolumes of the mammalian neocortex give rise to lifelong constraints for the formation of synaptic connections. To what degree do these constraints affect cortical wiring diagrams? Here we introduce an inverse modeling approach to show how cortical networks would appear if they were solely due to the spatial distributions of neurons and neurites. We find that neurite packing density and morphological diversity will inevitably translate into non-random pairwise and higher-order connectivity statistics. More importantly, we show that these non-random wiring properties are not arbitrary, but instead reflect the specific structural organization of the underlying neuropil. Our predictions are consistent with the empirically observed wiring specificity from subcellular to network scales. Thus, independent from learning and genetically encoded wiring rules, many of the properties that define the neocortex’ characteristic network architecture may emerge as a result of neuron and neurite development.

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Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neural networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a single rule to fit the empirical data, SBI considers many parametrizations of a wiring rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rules and relies on machine learning methods to estimate a probability distribution (the `posterior distribution over rule parameters conditioned on the data') that characterizes all data-compatible rules. We demonstrate how to apply SBI in connectomics by inferring the parameters of wiring rules in anin silicomodel of the rat barrel cortex, givenin vivoconnectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.

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