Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses

Protachevicz, Paulo R.; Iarosz, Kelly C.; Caldas, Iberê L.; Antonopoulos, Chris G.; Batista, Antônio M.; Kurths, Jürgen

doi:10.3389/fnsys.2020.604563

Cited by 28 publications

(12 citation statements)

References 106 publications

(119 reference statements)

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“…The form of feedback we incorporate has a form akin to that of an autapse, which is a chemical synapse from a neuron to itself. Autaptic connections have previously been shown to be important in establishing and maintaining rhythmic activity in mathematical models of small [ 32 ] and large [ 33 ] neural networks. Here, we investigate the interplay between autaptic processing and excitability dynamics in a single cell.…”

Section: Introductionmentioning

confidence: 99%

Robust spike timing in an excitable cell with delayed feedback

Wedgwood

Słowiński

Manson

et al. 2021

J. R. Soc. Interface.

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The initiation and regeneration of pulsatile activity is a ubiquitous feature observed in excitable systems with delayed feedback. Here, we demonstrate this phenomenon in a real biological cell. We establish a critical role of the delay resulting from the finite propagation speed of electrical impulses in the emergence of persistent multiple-spike patterns. We predict the coexistence of a number of such patterns in a mathematical model and use a biological cell subject to dynamic clamp to confirm our predictions in a living mammalian system. Given the general nature of our mathematical model and experimental system, we believe that our results capture key hallmarks of physiological excitability that are fundamental to information processing.

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Section: Introductionmentioning

confidence: 99%

Robust spike timing in an excitable cell with delayed feedback

Wedgwood

Słowiński

Manson

et al. 2021

J. R. Soc. Interface.

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show abstract

“…Then, the asymmetric time-delay coupling is discussed, and the phase difference is defined by Poincaré mapping [ 13 ]. The results show that the firing pattern and synchronous transition of chaotic coupled PBC neurons are robust to large time delays, and the robustness is more obvious with the increase of time delays, that is, the asymmetric time delays always show the phase synchronization of chaotic firing in a large range.…”

Section: Discussionmentioning

confidence: 99%

“…When a large number of conducted neuron models are considered, the number of coupled differential equations can be often a problem for computer simulations. Therefore, some models that are simpler but keep some of the dynamical features are considered, such as a discrete-time two-dimensional map proposed by Rulkov, and the integrate-and-fire neural models or hybrid neuron models [ 13 , 14 ]. Protachevicz et al investigated how the excitatory and inhibitory connectivities from one brain area to another influence the phase angle and neuronal synchronization, in which the neuron dynamics is given by the adaptive exponential integrate-and-fire model [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network

Yuan

2021

Computational Intelligence and Neuroscience

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Pre-Bötzinger complex (PBC) is a necessary condition for the generation of respiratory rhythm. Due to the existence of synaptic gaps, delay plays a key role in the synchronous operation of coupled neurons. In this study, the relationship between synchronization and correlation degree is established for the first time by using ISI bifurcation and correlation coefficient, and the relationship between synchronization and correlation degree is discussed under the conditions of no delay, symmetric delay, and asymmetric delay. The results show that the phase synchronization of two coupling PBCs is closely related to the weak correlation, that is, the weak phase synchronization may occur under the condition of incomplete synchronization. Moreover, the time delay and coupling strength are controlled in the modified PBC network model, which not only reveals the law of PBC firing transition but also reveals the complex synchronization behavior in the coupled chaotic neurons. Especially, when the two coupled neurons are nonidentical, the complete synchronization will disappear. These results fully reveal the dynamic behavior of the PBC neural system, which is helpful to explore the signal transmission and coding of PBC neurons and provide theoretical value for further understanding respiratory rhythm.

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“…Last but not the least, the model we consider focus on connection given by excitatory synapses. However, there are also inhibitory synapses [18,20] that inhibit the next neuron to generate the action potential. It would be interesting to extend our model with inclusion of both excitatory and inhibitory synapses to examine the influence of initial membrane potentials on the phase locking as well as spiking hierarchy of neurons.…”

Section: Discussionmentioning

confidence: 99%

Spiking hierarchy in an adaptive exponential integrate-and-fire network synchronization

Xiao-yue¹,

lin²,

Zhang³

2021

Preprint

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Neuronal network synchronization has received wide interests. Network connection structure is known to play a key role in its synchronization. In the present manuscript, we study the influence of initial membrane potentials together with network topology on bursting synchronization, in particular the sequential spiking order in stabilized inter bursts. We find a hierarchical phenomenon on spiking order. We grade neurons into different layers: primary neurons are those initiate the spike and lower-layer neurons are then determined via network connections successively. This constructs a directed graph to indicate spiking propagation among different layers of neurons. Neurons in upper layers spike earlier than those in lower layers. More interestingly, we find that among the same layer, neuron spiking order is mainly associated with stimuli they receive from upper layer via network connections; more stimuli leads to earlier spiking. Furthermore, we find that with effectively the same stimuli from upper layer, neurons with less connections spike earlier.

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Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses

Cited by 28 publications

References 106 publications

Robust spike timing in an excitable cell with delayed feedback

Robust spike timing in an excitable cell with delayed feedback

Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network

Spiking hierarchy in an adaptive exponential integrate-and-fire network synchronization

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