Difference Between Intermittent Chaotic Bursting and Spiking of Neural Firing Patterns

Gu, Huaguang; Xiao, Weiwei

doi:10.1142/s0218127414500825

Cited by 39 publications

(17 citation statements)

References 49 publications

(79 reference statements)

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“…As V shift K 2 is decreased from −0.023 to −0.025, the number of spikes per burst of bursting pattern is increased and changes to spiking pattern at V shift K 2 ≈ −0.024828 (critical value labeled as V LC ), as shown in the bottom part of Fig. 1c (similar bifurcations of neural firing patterns have been observed in the experiments on a neural pacemaker [41][42][43][44][45]). Therefore, spiking and bursting patterns coexist (between two dash lines in Fig.…”

Section: Coexistence Of Spiking and Burstingsupporting

confidence: 72%

The dependence of synchronization transition processes of coupled neurons with coexisting spiking and bursting on the control parameter, initial value, and attraction domain

Pan

2015

Nonlinear Dyn

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As the value of a control parameter decreases, monostable bursting changes to monostable spiking via the coexistence of spiking and bursting in a Leech neuron model, and the attraction domain of the bursting decreases, while that of the coexisting spiking increases for coexisting behaviors. As the coupling strength increases, the two coupled Leech neurons with the coexisting spiking and bursting manifest transitions from non-synchronization to complete synchronization (CS) through a complex process, which is dependent of the values of the control parameter, cases of configurations (one neuron spiking and the other bursting, both bursting, and both spiking), and the initial values of the two neurons. The transition processes involve various spiking and bursting or chaos synchronization states for the first two cases given here, but contain only spiking patterns for the last case. Corresponding with the attraction domain, the probability of initial values that can lead to bursting patterns of CS decreases, but the probability of initial values that can induce spiking of CS increases for the former two cases, as the control parameter is decreased. This phenomenon can also be interpreted by the structure and volume of the attraction domain with which all initial values can only induce CS of spiking for case 3. The results may improve understanding of the synchronization dynamics of the coupled neurons with coexisting behaviors.

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Section: Coexistence Of Spiking and Burstingsupporting

confidence: 72%

The dependence of synchronization transition processes of coupled neurons with coexisting spiking and bursting on the control parameter, initial value, and attraction domain

Pan

2015

Nonlinear Dyn

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“…ously reported models designed to recapitulate scale-free and multifractal time series, we developed hypotheses for molecular and system-level mechanisms underlying the multifractal nature of animal behaviors, and discuss how PKG may be involved in the mechanism. First, the intermittent bursts of a single neuron can be reproduced in chaos dynamical systems [62][63][64][65][66][67][68] ; owing to their non-Gaussian fluctuation with intermittency, such systems may be associated with multifractality in animal behaviors. Chaotic dynamics reproduced in these models are generated by interactions between fast inflows and slow outflows of ions to/from a neuron.…”

Section: Molecular and System-level Mechanisms For Multifractal Episomentioning

confidence: 99%

C. elegans episodic swimming is driven by multifractal kinetics

Ikeda

Jurica

Kimura

et al. 2020

Sci Rep

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Fractal scaling is a common property of temporal change in various modes of animal behavior. The molecular mechanisms of fractal scaling in animal behaviors remain largely unexplored. The nematode C. elegans alternates between swimming and resting states in a liquid solution. Here, we report that C. elegans episodic swimming is characterized by scale-free kinetics with long-range temporal correlation and local temporal clusterization, namely consistent with multifractal kinetics. Residence times in actively-moving and inactive states were distributed in a power law-based scale-free manner. Multifractal analysis showed that temporal correlation and temporal clusterization were distinct between the actively-moving state and the inactive state. These results indicate that C. elegans episodic swimming is driven by transition between two behavioral states, in which each of two transition kinetics follows distinct multifractal kinetics. We found that a conserved behavioral modulator, cyclic GMP dependent kinase (PKG) may regulate the multifractal kinetics underlying an animal behavior. Our combinatorial analysis approach involving molecular genetics and kinetics provides a platform for the molecular dissection of the fractal nature of physiological and behavioral phenomena.

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“…The existence of a ‘small’ limit cycle next to others or fixed points can be directly used to describe bursting behavior by fast events, for instance, in repetitive spiking sequences. Other dynamics associated with bursting behavior are quasi-periodicity, deterministic chaos and intermittency [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Fast–Slow Bursters in the Unfolding of a High Codimension Singularity and the Ultra-slow Transitions of Classes

et al. 2017

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Bursting is a phenomenon found in a variety of physical and biological systems. For example, in neuroscience, bursting is believed to play a key role in the way information is transferred in the nervous system. In this work, we propose a model that, appropriately tuned, can display several types of bursting behaviors. The model contains two subsystems acting at different time scales. For the fast subsystem we use the planar unfolding of a high codimension singularity. In its bifurcation diagram, we locate paths that underlie the right sequence of bifurcations necessary for bursting. The slow subsystem steers the fast one back and forth along these paths leading to bursting behavior. The model is able to produce almost all the classes of bursting predicted for systems with a planar fast subsystem. Transitions between classes can be obtained through an ultra-slow modulation of the model’s parameters. A detailed exploration of the parameter space allows predicting possible transitions. This provides a single framework to understand the coexistence of diverse bursting patterns in physical and biological systems or in models.

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Difference Between Intermittent Chaotic Bursting and Spiking of Neural Firing Patterns

Cited by 39 publications

References 49 publications

The dependence of synchronization transition processes of coupled neurons with coexisting spiking and bursting on the control parameter, initial value, and attraction domain

The dependence of synchronization transition processes of coupled neurons with coexisting spiking and bursting on the control parameter, initial value, and attraction domain

C. elegans episodic swimming is driven by multifractal kinetics

Fast–Slow Bursters in the Unfolding of a High Codimension Singularity and the Ultra-slow Transitions of Classes

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