# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1703.04176