Dibakar Ghosh

Dr. Dibakar Ghosh

Associate Professor of Applied Mathematics, Physics and Applied Mathematics Unit, Indian Statistical, 203 B. T. Road, Kolkata 700108, India

Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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23 March 2022 in Journal of The Royal Society Interface
Verified
Journal Article
Open access
SMSoumen Majhi
Soumen Majhi
Matjaz PercMatjaz Perc
Matjaz Perc
DGDibakar Ghosh
Dibakar Ghosh
Network science has evolved into an indispensable platform for studying complex systems. But recent research has identified limits of classical networks, where links connect pairs of nodes, to comprehensively describe group interactions. Higher-order networks, where a link can connect more than two nodes, have therefore emerged as a new frontier in network science. Since group interactions are common in social, biological and technological systems, higher-order networks have recently led to important new discoveries across many fields of research. Here, we review these works, focusing in particular on the novel aspects of the dynamics that emerges on higher-order networks. We cover a variety of dynamical processes that have thus far been studied, including different synchronization phenomena, contagion processes, the evolution of cooperation and consensus formation. We also outline open challenges and promising directions for future research.

1 March 2019 in Physics of Life Reviews
Verified
Journal Article
Closed access
SMSoumen Majhi
Soumen Majhi
BBBidesh K. Bera
Bidesh K. Bera
DGDibakar Ghosh
Dibakar Ghosh
Matjaz PercMatjaz Perc
Matjaz Perc

11 January 2016 in Physical Review E
Verified
Journal Article
Closed access
PDF file
BBBidesh K. Bera
Bidesh K. Bera
DGDibakar Ghosh
Dibakar Ghosh
MLMuthusamy Lakshmanan
Muthusamy Lakshmanan
We study the existence of chimera states in pulse-coupled networks of bursting Hindmarsh-Rose neurons with nonlocal, global, and local (nearest neighbor) couplings. Through a linear stability analysis, we discuss the behavior of the stability function in the incoherent (i.e., disorder), coherent, chimera, and multichimera states. Surprisingly, we find that chimera and multichimera states occur even using local nearest neighbor interaction in a network of identical bursting neurons alone. This is in contrast with the existence of chimera states in populations of nonlocally or globally coupled oscillators. A chemical synaptic coupling function is used which plays a key role in the emergence of chimera states in bursting neurons. The existence of chimera, multichimera, coherent, and disordered states is confirmed by means of the recently introduced statistical measures and mean phase velocity.

5 January 2022 in Physics Reports
Verified
Journal Article
Closed access
DGDibakar Ghosh
Dibakar Ghosh
MFMattia Frasca
Mattia Frasca
ARAlessandro Rizzo
Alessandro Rizzo
SMSoumen Majhi
Soumen Majhi
SRSarbendu Rakshit
Sarbendu Rakshit
SBStefano Boccaletti
Stefano Boccaletti

3 May 2022 in Physics Reports
Verified
Journal Article
Closed access
SCSayantan Nag Chowdhury
Sayantan Nag Chowdhury
ARArnob Ray
Arnob Ray
SDSyamal K. Dana
Syamal K. Dana
DGDibakar Ghosh
Dibakar Ghosh

14 July 2016 in Physical Review E
Verified
Journal Article
Closed access
PDF file
BBBidesh K. Bera
Bidesh K. Bera
DGDibakar Ghosh
Dibakar Ghosh
TBTanmoy Banerjee
Tanmoy Banerjee
In this paper, we report the occurrence of chimera patterns in a network of neuronal oscillators, which are coupled through local, synaptic gradient coupling. We discover a new chimera pattern, namely the imperfect traveling chimera state, where the incoherent traveling domain spreads into the coherent domain of the network. Remarkably, we also find that chimera states arise even for one-way local coupling, which is in contrast to the earlier belief that only nonlocal, global, or nearest-neighbor local coupling can give rise to chimera state; this find further relaxes the essential connectivity requirement of getting a chimera state. We choose a network of identical bursting Hindmarsh-Rose neuronal oscillators, and we show that depending upon the relative strength of the synaptic and gradient coupling, several chimera patterns emerge. We map all the spatiotemporal behaviors in parameter space and identify the transitions among several chimera patterns, an in-phase synchronized state, and a global amplitude death state.