Quantifying synchronization in a biologically inspired neural network

Conference item

We present a collated set of algorithms to obtain objective measures of synchronization 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 synchronization objectively in the network 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. Link to SyncBox: https://github.com/PranavMahajan25/SyncBox

Authors: Mahajan, P; Rane, A; Sasi, S; Bhattacharya, BS

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2021 International Joint Conference on Neural Networks (IJCNN)
• Pages: 1-6
• Publication date: 2021-09-20
• Event title: International Joint Conference on Neural Networks (IJCNN 2021)
• Event location: Shenzhen, China
• Event website: https://neural.memberclicks.net/ijcnn-2021-sessions
• Event start date: 2021-07-18
• Event end date: 2021-07-22
• DOI: 10.1109/ijcnn52387.2021.9533414
• ISSN: 2161-4393
• Language: English
• Keywords: neurological; neurosciences; bioengineering