A Neural Mass Computational Framework to Study Synaptic Mechanisms Underlying Alpha and Theta Rhythms

Bhattacharya, Basabdatta Sen; Durrant, Simon J.

doi:10.1007/978-3-319-49959-8_14

Cited by 7 publications

(9 citation statements)

References 68 publications

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“…3, we present a comparative study of synchronization in the model with a high and a low signal-to-noise ratio (amplitude of the input impulse as 5 and 10 respectively). As observed in [1], we note that PLVs between TCR and TRN time-series responses are high between frequencies 5 -20. The NSE and LSI also confirm this observation.…”

Section: Resultssupporting

confidence: 75%

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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: Resultssupporting

confidence: 75%

“…We test our toolbox on an existing neural mass model of the visual thalamus Lateral Geniculate Nucleus (LGN) [1]. We present the model with periodic input at varied frequency and at varying input strength.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Synchronization in a Biologically Inspired Neural Network

Mahajan¹,

Rane²,

Sasi³

et al. 2020

Preprint

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“…Neural field models [74,75], which are conceptually similar to neural mass models but have additional spatio-temporal characteristics using partial differential equations [76], are used to simulate and understand brain states such as anesthesia [77], sleep-wake cycles [60], as well as to make testable predictions on brain stimulation [78]. We have been working with neural mass in silico models to understand slowing of alpha rhythms as a definitive marker in the EEG of Alzheimer disease, as well as to understand the neuronal dynamics underpinning awake resting state alpha rhythms [34]. The in silico model used in this study is an extension of our previous works.…”

Section: Discussionmentioning

confidence: 99%

“…Table 1: Synaptic connectivity parameters for cortical and thalamic populations as derived from [49] and [34] The nomenclature for the cortical layer populations adopted in this work are as follows: L4 populations are Py4, SS4, B4; L6 populations are Py6, B6; sources of intrinsic noisy input from other cortical and subcortical areas to L4 (L6) are Asy4 (Asy6) (asymmetric: excitatory) and Sy4 (Sy6) (symmetric: inhibitory). The network outputs are the time series responses of the TCR, Py4 and Py6 populations, all of which are known to communicate neuronal information over long distances due to their physical attributes, as opposed to the interneurons that are known to communicate locally.…”

Section: Structure and Layoutmentioning

confidence: 99%

“…Subsequently, we have used an adapted version of the traditional neural mass model [32] by introducing kinetic equations of neuro-transmission and reception [33]. We have been using this version of the neural mass model to understand the population dynamics underlying alpha rhythms in the visual thalamus, known as the Lateral Geniculate Nucleus (LGN) of the thalamic structure [34]. We have shown, albeit briefly, the phase locking behaviour in the LGN output time-series corresponding to periodic flickering stimuli as observed in human SSVEP [35].…”

Section: Introductionmentioning

confidence: 99%

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Phase Entrainment by Periodic Stimuli In Silico: A Quantitative Study

Sasi,

Bhattacharya

2021

Preprint

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We present a quantitative study of phase entrainment by periodic visual stimuli in a biologically inspired neural network. The objective is to understand the neuronal population dynamics that underlie phase entrainment. Phase and frequency entrainment of brain oscillations by external stimuli are used as therapeutic treatment in neurological disorders, for example in Parkinsonian tremor. And yet, the neuronal dynamics underpinning such entrainment is not fully understood. Rhythmic sensory stimulation is one way of studying phase synchronisation in the brain. A recent experimental study has shown phase entrainment of brain oscillations during steady state visually evoked potentials (SSVEP), which are scalp electroencephalogram corresponding to periodic rhythmic stimuli. We have simulated SSVEP-like signals corresponding to periodic pulse input to our in silico model. We have used phase locking values, normalised Shannon entropy and conditional probability as synchronisation indices showing relative phase synchrony between the neuronal populations as well with the input. The phase synchronisation disappears with jitter in the input inter-pulse intervals. This would not have been the case if the output signal were to be the superposition of the responses to the different input signals. Thus, it may be inferred that the phase synchronisation implies entrainment of the network response by the periodic input. Overall, our study shows the plausibility of using biologically inspired in silico models, validated with experimental works, to understand and make testable predictions on brain entrainment as a therapeutic treatment in specific neurological disorders.

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In Silico Study of Single Synapse Dynamics Using a Three-State Kinetic Model

Sasi,

Sen Bhattacharya

2023

Lecture Notes in Computer Science

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A Neural Mass Computational Framework to Study Synaptic Mechanisms Underlying Alpha and Theta Rhythms

Cited by 7 publications

References 68 publications

Quantifying Synchronization in a Biologically Inspired Neural Network

Quantifying Synchronization in a Biologically Inspired Neural Network

Phase Entrainment by Periodic Stimuli In Silico: A Quantitative Study

In Silico Study of Single Synapse Dynamics Using a Three-State Kinetic Model

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