Locally embedded presages of global network bursts

TL;DR

This paper introduces a novel state-space reconstruction method that uses high-resolution neural recordings to identify early deterministic signatures predicting global network bursts, revealing insights into neural synchronization mechanisms.

Contribution

The study develops a new method combining state-space reconstruction with neural recordings to predict network bursts from local neuron dynamics, advancing understanding of neural synchronization.

Findings

Local neuron dynamics can predict global bursts more effectively than mean field activity.

Inter-cell variability in predictability reflects underlying network structure.

Deterministic signatures serve as early warnings of network state transitions.

Abstract

Spontaneous, synchronous bursting of neural population is a widely observed phenomenon in nervous networks, which is considered important for functions and dysfunctions of the brain. However, how the global synchrony across a large number of neurons emerges from an initially non-bursting network state is not fully understood. In this study, we develop a new state-space reconstruction method combined with high-resolution recordings of cultured neurons. This method extracts deterministic signatures of upcoming global bursts in "local" dynamics of individual neurons during non-bursting periods. We find that local information within a single-cell time series can compare with or even outperform the global mean field activity for predicting future global bursts. Moreover, the inter-cell variability in the burst predictability is found to reflect the network structure realized in the non-bursting periods. These findings demonstrate the deterministic mechanisms underlying the locally concentrated early-warnings of the global state transition in self-organized networks.