Inferring network connectivity from event timing patterns

TL;DR

This paper introduces a novel, model-independent method to infer network connectivity solely from event timing data, enabling the reconstruction of complex networks without access to their full continuous dynamics.

Contribution

It presents a new theoretical framework that reconstructs network connections from event time series, applicable to neural, social, and engineering systems.

Findings

Successfully infers synaptic connections and their nature in neural circuits.

Scalable to large networks and applicable across disciplines.

Reveals direct influence between units based on event timing patterns.

Abstract

Reconstructing network connectivity from the collective dynamics of a system typically requires access to its complete continuous-time evolution although these are often experimentally inaccessible. Here we propose a theory for revealing physical connectivity of networked systems only from the event time series their intrinsic collective dynamics generate. Representing the patterns of event timings in an event space spanned by inter-event and cross-event intervals, we reveal which other units directly influence the inter-event times of any given unit. For illustration, we linearize an event space mapping constructed from the spiking patterns in model neural circuits to reveal the presence or absence of synapses between any pair of neurons as well as whether the coupling acts in an inhibiting or activating (excitatory) manner. The proposed model-independent reconstruction theory is scalable to larger networks and may thus play an important role in the reconstruction of networks from biology to social science and engineering.