Combined topological and spatial constraints are required to capture the structure of neural connectomes

TL;DR

This paper demonstrates that combining topological and spatial constraints in maximum entropy models is essential to accurately capture the structure of neural connectomes across different organisms, revealing shared principles and improving predictive modeling.

Contribution

It introduces scalable maximum entropy models that incorporate both spatial and topological constraints to better understand neural connectome structures.

Findings

Physical constraints significantly influence connectome structure.

Spatial constraints alone cannot reproduce network topology.

Combined constraints improve predictive accuracy of neural network models.

Abstract

Volumetric brain reconstructions provide an unprecedented opportunity to gain insights into the complex connectivity patterns of neurons in an increasing number of organisms. Here, we model and quantify the complexity of the resulting neural connectomes in the fruit fly, mouse, and human and unveil a simple set of shared organizing principles across these organisms. To put the connectomes in a physical context, we also construct contactomes, the network of neurons in physical contact in each organism. With these, we establish that physical constraints -- either given by pairwise distances or the contactome -- play a crucial role in shaping the network structure. For example, neuron positions are highly optimal in terms of distance from their neighbors. However, spatial constraints alone cannot capture the network topology, including the broad degree distribution. Conversely, the degree sequence alone is insufficient to recover the spatial structure. We resolve this apparent conflict by formulating scalable maximum entropy models, incorporating both types of constraints. The resulting generative models have predictive power beyond the input data, as they capture several additional biological and network characteristics, like synaptic weights and graphlet statistics.