Tackling the subsampling problem to infer collective properties from limited data

TL;DR

This paper reviews the challenges of spatial subsampling in complex systems research, especially in neuroscience, and discusses recent mathematical approaches to accurately infer collective properties from limited data, highlighting ongoing open problems.

Contribution

It provides a comprehensive overview of subsampling issues and reviews recent methods developed to correct biases in inferring system-wide properties from partial observations.

Findings

Recent approaches enable correct assessment of network structures from limited data

Subsampling biases can significantly distort inference of collective dynamics

Open challenges remain in generalizing solutions to all complex systems

Abstract

Complex systems are fascinating because their rich macroscopic properties emerge from the interaction of many simple parts. Understanding the building principles of these emergent phenomena in nature requires assessing natural complex systems experimentally. However, despite the development of large-scale data-acquisition techniques, experimental observations are often limited to a tiny fraction of the system. This spatial subsampling is particularly severe in neuroscience, where only a tiny fraction of millions or even billions of neurons can be individually recorded. Spatial subsampling may lead to significant systematic biases when inferring the collective properties of the entire system naively from a subsampled part. To overcome such biases, powerful mathematical tools have been developed in the past. In this perspective, we overview some issues arising from subsampling and review recently developed approaches to tackle the subsampling problem. These approaches enable one to assess, e.g., graph structures, collective dynamics of animals, neural network activity, or the spread of disease correctly from observing only a tiny fraction of the system. However, our current approaches are still far from having solved the subsampling problem in general, and hence we conclude by outlining what we believe are the main open challenges. Solving these challenges alongside the development of large-scale recording techniques will enable further fundamental insights into the working of complex and living systems.