Quantifying Synchronization in a Biologically Inspired Neural Network

TL;DR

This paper introduces SyncBox, a MATLAB toolkit for objectively measuring phase synchronization in neural mass models simulating visual thalamic activity, aiding understanding of neural dynamics in response to periodic stimuli.

Contribution

The paper presents a comprehensive set of algorithms, implemented as SyncBox, for quantifying synchronization in brain time-series data, specifically applied to neural mass models of the visual thalamus.

Findings

SyncBox effectively measures phase synchronization in neural models.

Application to visual thalamus models demonstrates its utility.

Tool is being used for further neural dynamics research.

Abstract

We present a collated set of algorithms to obtain objective measures of synchronisation in brain time-series data. The algorithms are implemented in MATLAB; we refer to our collated set of 'tools' as SyncBox. Our motivation for SyncBox is to understand the underlying dynamics in an existing population neural network, commonly referred to as neural mass models, that mimic Local Field Potentials of the visual thalamic tissue. Specifically, we aim to measure the phase synchronisation objectively in the model response to periodic stimuli; this is to mimic the condition of Steady-state-visually-evoked-potentials (SSVEP), which are scalp Electroencephalograph (EEG) corresponding to periodic stimuli. We showcase the use of SyncBox on our existing neural mass model of the visual thalamus. Following our successful testing of SyncBox, it is currently being used for further research on understanding the underlying dynamics in enhanced neural networks of the visual pathway