Chimeras

Cui

et al. 2021

Preprint

Periodic visual stimulation can induce stable steady-state visual evoked potentials (SSVEPs) distributed in multiple brain regions and has potential applications in both neural engineering and cognitive neuroscience. However, the underlying dynamic mechanisms of SSVEPs at the whole-brain level are still not completely understood. Here, we addressed this issue by simulating the rich dynamics of SSVEPs with a large-scale brain model designed with constraints of neuroimaging data acquired from the human brain. By eliciting activity of the occipital areas using an external periodic stimulus, our model was capable of replicating both the spatial distributions and response features of SSVEPs that were observed in experiments. In particular, we confirmed that alpha-band (8-12 Hz) stimulation could evoke stronger SSVEP responses; this frequency sensitivity was due to nonlinear resonance and could be modulated by endogenous factors in the brain. Interestingly, the stimulus-evoked brain networks also exhibited significant superiority in topological properties near this frequency-sensitivity range, and stronger SSVEP responses were demonstrated to be supported by more efficient functional connectivity at the neural activity level. These findings not only provide insights into the mechanistic understanding of SSVEPs at the whole-brain level but also indicate a bright future for large-scale brain modeling in characterizing the complicated dynamics and functions of the brain.

Section: Model and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational exploration of dynamic mechanisms of steady state visual evoked potentials at the whole brain level

Cui

et al. 2021

Preprint

“…e emergence of collective behaviors is an important characteristic of complex networks [13]. Some examples of collective behaviors are synchronization [14], chimera state [15], and solitary state [16]. Synchronization is an important phenomenon in many applications [17].…”

Section: Introductionmentioning

confidence: 99%

Chimera State in the Network of Fractional‐Order FitzHugh–Nagumo Neurons

et al. 2021

Self Cite

The fractional calculus in the neuronal models provides the memory properties. In the fractional-order neuronal model, the dynamics of the neuron depends on the derivative order, which can produce various types of memory-dependent dynamics. In this paper, the behaviors of the coupled fractional-order FitzHugh–Nagumo neurons are investigated. The effects of the coupling strength and the derivative order are under consideration. It is revealed that the level of the synchronization is decreased by decreasing the derivative order, and the chimera state emerges for stronger couplings. Furthermore, the patterns of the formed chimeras rely on the order of the derivatives.

“…Chimera states (Abrams and Strogatz, 2004;Sethia et al, 2008;Laing, 2009;Hagerstrom et al, 2012;Martens et al, 2013;Omelchenko et al, 2013;Gopal et al, 2014;Schöll, 2016;Hart et al, 2019;Majhi et al, 2019;Parastesh et al, 2020;Wang and Liu, 2020) became a paradigm of collective phenomena in dynamical systems that started with the first report by Kuramoto and Battogtokh (2002) of two coexisting synchronous and asynchronous groups of phase oscillators arranged in a ring and coupled in a non-local fashion. The main question was how the symmetry in a completely synchronous ensemble of identical oscillators breaks into two clusters, one synchronous group and another asynchronous group.…”

Section: Introductionmentioning

confidence: 99%

“…Later, it has been established that chimera states are possible stable states (Pecora et al, 2014;Omel'chenko, 2018;Laing, 2019) in an ensemble of identical oscillators and with symmetry in the connectivity matrix or the topology of a network. By this time, this phenomenon has been widely explored in single-layer networks (Abrams and Strogatz, 2004;Sethia et al, 2008;Laing, 2009;Hagerstrom et al, 2012;Martens et al, 2013;Omelchenko et al, 2013;Gopal et al, 2014;Hart et al, 2019;Majhi et al, 2019;Parastesh et al, 2020;Wang and Liu, 2020), multilayer networks (Ghosh and Jalan, 2016;Maksimenko et al, 2016;Sawicki et al, 2018;Ruzzene et al, 2020), and 3D networks (Maistrenko et al, 2015;Kasimatis et al, 2018;Kundu et al, 2019) with different forms of chimeras such as traveling chimera (Bera et al, 2016a;Omel'chenko, 2019;Dudkowski et al, 2019;Alvarez-Socorro et al, 2021) and spiral chimera (Martens et al, 2010;Gu et al, 2013). Various dynamical models (Hizanidis et al, 2015;Banerjee et al, 2016;Bera et al, 2016b;Saha et al, 2019) with different coupling schemes (Meena et al, 2016;Bera et al, 2017) and global coupling (Sethia and Sen, 2014;Yeldesbay et al, 2014;Hens et al, 2015a;Mishra et al, 2015) have been used for observing chimera patterns.…”

Section: Introductionmentioning

confidence: 99%

Smallest Chimeras Under Repulsive Interactions

Saha¹,

Dana²

2021

Front. Netw. Physiol.

We present an exemplary system of three identical oscillators in a ring interacting repulsively to show up chimera patterns. The dynamics of individual oscillators is governed by the superconducting Josephson junction. Surprisingly, the repulsive interactions can only establish a symmetry of complete synchrony in the ring, which is broken with increasing repulsive interactions when the junctions pass through serials of asynchronous states (periodic and chaotic) but finally emerge into chimera states. The chimera pattern first appears in chaotic rotational motion of the three junctions when two junctions evolve coherently, while the third junction is incoherent. For larger repulsive coupling, the junctions evolve into another chimera pattern in a periodic state when two junctions remain coherent in rotational motion and one junction transits to incoherent librational motion. This chimera pattern is sensitive to initial conditions in the sense that the chimera state flips to another pattern when two junctions switch to coherent librational motion and the third junction remains in rotational motion, but incoherent. The chimera patterns are detected by using partial and global error functions of the junctions, while the librational and rotational motions are identified by a libration index. All the collective states, complete synchrony, desynchronization, and two chimera patterns are delineated in a parameter plane of the ring of junctions, where the boundaries of complete synchrony are demarcated by using the master stability function.