ORCID Profile
0000-0002-1669-1083
Current Organisation
Indian Institute of Technology
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: IOP Publishing
Date: 07-2019
Abstract: Several studies have proposed constraints under which a low-dimensional representation can be derived from large-scale real-world networks exhibiting complex nonlinear dynamics. Typically, these representations are formulated under certain assumptions, such as when solutions converge to attractor states using linear stability analysis or using projections of large-scale dynamical data into a set of lower dimensional modes that are selected heuristically. Here, we propose a generative framework for selection of lower dimensional modes onto which the entire network dynamics can be projected based on the symmetry of the input distribution for a large-scale network driven by external inputs, thus relaxing the heuristic selection of modes made in the earlier reduction approaches. The proposed mode reduction technique is tractable analytically and applied to different kinds of real-world large-scale network scenarios with nodes comprising of (a) Van der Pol oscillators (b) Hindmarsh–Rose neurons. These two demonstrations elucidate how order parameter is conserved at original and reduced descriptions thus validating our proposition.
Publisher: Frontiers Media SA
Date: 13-10-2016
Publisher: Mary Ann Liebert Inc
Date: 12-2014
Abstract: Spontaneous brain activity, that is, activity in the absence of controlled stimulus input or an explicit active task, is topologically organized in multiple functional networks (FNs) maintaining a high degree of coherence. These "resting state networks" are constrained by the underlying anatomical connectivity between brain areas. They are also influenced by the history of task-related activation. The precise rules that link plastic changes and ongoing dynamics of resting-state functional connectivity (rs-FC) remain unclear. Using the framework of the open source neuroinformatics platform "The Virtual Brain," we identify potential computational mechanisms that alter the dynamical landscape, leading to reconfigurations of FNs. Using a spiking neuron model, we first demonstrate that network activity in the absence of plasticity is characterized by irregular oscillations between low- litude asynchronous states and high- litude synchronous states. We then demonstrate the capability of spike-timing-dependent plasticity (STDP) combined with intrinsic alpha (8-12 Hz) oscillations to efficiently influence learning. Further, we show how alpha-state-dependent STDP alters the local area dynamics from an irregular to a highly periodic alpha-like state. This is an important finding, as the cortical input from the thalamus is at the rate of alpha. We demonstrate how resulting rhythmic cortical output in this frequency range acts as a neuronal tuner and, hence, leads to synchronization or de-synchronization between brain areas. Finally, we demonstrate that locally restricted structural connectivity changes influence local as well as global dynamics and lead to altered rs-FC.
Publisher: Springer Singapore
Date: 2018
Publisher: Wiley
Date: 29-06-2020
DOI: 10.1111/EJN.14747
Location: India
Location: Germany
Location: United States of America
No related grants have been discovered for Dipanjan Roy.