Mode-locking behavior of Izhikevich neurons under periodic external forcing

Farokhniaee, AmirAli; Large, Edward W.

doi:10.1186/1471-2202-16-s1-p140

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…Synchronization between spiking time-series output of different neuronal populations or between the spike trains and an external input depends on both amplitude and frequency of the input. Thus, one can obtain regions of phaselocking on an amplitude-frequency plane, and these are commonly referred to as Arnold Tongue, due to the tongue like shape chalked out by the high synchrony regions [2].…”

Section: Phase Locking Valuementioning

confidence: 99%

“…Parkinsonian tremor [19]). Thus, neuronal dynamics underlying rhythmic periodicity has been the subject of several research [3,10,2,19]. Of particular interest is the neuronal dynamics corresponding to Steady-statevisually-evoked-potentials (SSVEP), that refer to Electroencephalogram (EEG) recorded from humans and animals when subjected to rhythmic visual stimuli.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Synchronization in a Biologically Inspired Neural Network

Mahajan¹,

Rane²,

Sasi³

et al. 2020

Preprint

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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.

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Section: Phase Locking Valuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying Synchronization in a Biologically Inspired Neural Network

Mahajan¹,

Rane²,

Sasi³

et al. 2020

Preprint

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“…Neural activity synchronization is crucial to neural function and cognitive processes [ 16 – 19 ]. Synchronization of different single neuron classes to periodic external stimuli [ 20 , 21 ] and coupled neurons [ 22 – 26 ] has been analyzed extensively, and can be measured both locally or over a global scale [ 27 – 30 ]. Brain networks are not the only complex systems that exhibit this phenomenon, social communications and financial markets are also affected.…”

Section: Introductionmentioning

confidence: 99%

Criticality and partial synchronization analysis in Wilson-Cowan and Jansen-Rit neural mass models

Kazemi,

Farokhniaee,

Jamali

2024

PLoS ONE

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Synchronization is a phenomenon observed in neuronal networks involved in diverse brain activities. Neural mass models such as Wilson-Cowan (WC) and Jansen-Rit (JR) manifest synchronized states. Despite extensive research on these models over the past several decades, their potential of manifesting second-order phase transitions (SOPT) and criticality has not been sufficiently acknowledged. In this study, two networks of coupled WC and JR nodes with small-world topologies were constructed and Kuramoto order parameter (KOP) was used to quantify the amount of synchronization. In addition, we investigated the presence of SOPT using the synchronization coefficient of variation. Both networks reached high synchrony by changing the coupling weight between their nodes. Moreover, they exhibited abrupt changes in the synchronization at certain values of the control parameter not necessarily related to a phase transition. While SOPT was observed only in JR model, neither WC nor JR model showed power-law behavior. Our study further investigated the global synchronization phenomenon that is known to exist in pathological brain states, such as seizure. JR model showed global synchronization, while WC model seemed to be more suitable in producing partially synchronized patterns.

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“…Neural activity synchronization is crucial to neural function and cognitive processes [16][17][18][19]. Synchronization of different single neuron classes to periodic external stimuli [20,21] and coupled neurons [22][23][24][25] has been analyzed extensively, and can be measured both locally or over a global scale [26][27][28][29]. Synchronization, asynchronization, and partial synchronization are three cases of dynamical behavior in systems of coupled oscillators that manifest interesting and neurophysiologically relevant results.…”

Section: Introductionmentioning

confidence: 99%

Criticality and partial synchronization analysis in Wilson-Cowan and Jansen-Rit neural mass models

Kazemi,

Farokhniaee,

Jamali

2023

Preprint

Self Cite

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Synchronization is a phenomenon observed in neuronal networks involved in diverse brain activities. Neural mass models such as Wilson-Cowan (WC) and Jansen-Rit (JR) manifest synchronized states. Although they have been studied for decades, their ability to demonstrate second-order phase transition (SOPT) and criticality has not received enough attention, which serves as candidates for the development of healthy brain networks. In this study, two networks of coupled WC and JR nodes with small-world topologies were constructed and Kuramoto order parameter (KOP) was used to quantify the amount of synchronization. In addition, we investigated the presence of SOPT using the synchronization coefficient of variation. Both networks reached high synchrony by changing the coupling weight between their nodes. Moreover, they exhibited abrupt changes in the synchronization at certain values of the control parameter not necessarily related to a phase transition. While SOPT was observed only in JR model, neither WC nor JR model showed power-law behavior. Our study further investigated the global synchronization phenomenon that is known to exist in pathological brain states, such as seizure. JR model showed global synchronization, while WC model seemed to be more suitable in producing partially synchronized patterns.Corresponding author summaryYousef Jamali received his M.Sc. degree in physics from Sharif University, Tehran, Iran, in 2004, and his Ph.D. degree in physics from Sharif University, Tehran, Iran, in 2009. After finishing his Ph.D. thesis, in 2009, he got two years postdoctoral research associate position at the University of California at Berkeley, Department of Bioengineering. He is currently an Associate Professor in the Applied Mathematics Department (Biomathematics division), at Tarbiat Modares University, Tehran, Iran. His current research interests include the interdisciplinary area of the complex system especially on the modeling of brain. In fact, his interests are how the brain works at the critical state and how this complex system synchronized under different conditions.

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Mode-locking behavior of Izhikevich neurons under periodic external forcing

Cited by 6 publications

References 3 publications

Quantifying Synchronization in a Biologically Inspired Neural Network

Quantifying Synchronization in a Biologically Inspired Neural Network

Criticality and partial synchronization analysis in Wilson-Cowan and Jansen-Rit neural mass models

Criticality and partial synchronization analysis in Wilson-Cowan and Jansen-Rit neural mass models

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