Discrete hybrid Izhikevich neuron model: Nodal and network behaviours considering electromagnetic flux coupling

Muni, Sishu Shankar; Rajagopal, Karthikeyan; Karthikeyan, Anitha; Arun, Sundaram

doi:10.1016/j.chaos.2021.111759

Cited by 39 publications

(12 citation statements)

References 16 publications

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“…In Figs. 4-9, 1. the first plot indicates the phase portrait of all the N nodes with the transients removed, 2. the second plot refers to the Γ 2,m values for all the N nodes, 3. the third plot denotes the spatiotemporal dynamics of the nodes with time, 4. the fourth plot portrays the last instance of all the units in space after a sufficient number of iterations n with the transient dynamics removed, and 5. the last plot corresponds to the recurrence plot [49] comparing the distance between the final position of each node with all the other nodes in space after n iterations. x SE = 0.041 Fig.…”

Section: Phase Portraits Spatiotemporal Patterns and Recurrence Plotsmentioning

confidence: 99%

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On the analysis of a heterogeneous coupled network of memristive Chialvo neurons.

Ghosh

Muni

Fatoyinbo

2023

Preprint

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We perform a numerical study on the application of electromagnetic flux on a heterogeneous network of Chialvo neurons represented by a ring-star topology. Heterogeneities are realized by introducing additive noise modulations on both the central-peripheral and the peripheral-peripheral coupling links in the topology not only varying in space but also in time. The variation in time is understood by two coupling probabilities, one for the central-peripheral connections and the other for the peripheral-peripheral connections respectively, that update the network topology with each iteration in time. We have further reported the rich spatiotemporal patterns like two-cluster states, chimera states, coherent, and asynchronized states that arise throughout the network dynamics. We have also investigated the appearance of a special kind of asynchronization behavior called "solitary nodes" that have a wide range of applications pertaining to real-world nervous systems. In order to characterize the behavior of the nodes under the influence of these heterogeneities, we have studied two different metrics called the "cross-correlation coefficient" and the "synchronization error". Additionally, to capture the statistical property of the network, for example, how complex the system behaves, we have also studied a measure called "sample entropy". Various two-dimensional color-coded plots are presented in the study to exhibit how these metrics/measures behave with the variation of parameters.

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Section: Phase Portraits Spatiotemporal Patterns and Recurrence Plotsmentioning

confidence: 99%

“…Empirical evidence says that the functioning of neurons is affected by many external factors such as temperature [46], pressure [47] light [48], electromagnetic radiations [49][50][51] and many more. For example, the signature of growth in embryo neuronal cells was observed with the application of electromagnetic flux [52].…”

Section: Introductionmentioning

confidence: 99%

On the analysis of a heterogeneous coupled network of memristive Chialvo neurons.

Ghosh

Muni

Fatoyinbo

2023

Preprint

Self Cite

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“…[44] However, it is currently unclear how noise affects the information processing mechanism of neurons, so the impact of noise on the firing activities of neurons cannot be ignored. [55][56][57] In the present study, we propose a new discrete locally active memristor and use it to connect the discretized Izhikevich and Chialvo neurons. Thus, a heterogeneous discrete neuron network model coupled by the discrete locally active memristor is constructed.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Yan 晏,

Li 李,

Li 李

2024

Chinese Phys. B

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Research on discrete memristor-based neural networks has received much attention. However, current research mainly focuses on memristor–based discrete homogeneous neuron networks, while memristor-coupled discrete heterogeneous neuron networks are rarely reported. In this study, a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V-I diagram. Based on two-dimensional discrete Izhikevich neuron and two-dimensional discrete Chialvo neuron, a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons. Considering the coupling strength as the control parameter, chaotic firing, periodic firing and hyperchaotic firing patterns are revealed. In particular, multiple coexisting firing patterns are observed, which are induced by different initial values of the memristor. Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength. Furthermore, the effect of Gaussian white noise on synchronization behaviors is also explored. We demonstrate that the presence of noise not only leads to the transition of firing patterns, but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

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“…Neuron models formed by nonlinear map are called discrete neuron models. [15][16][17][18][19][20][21] The discrete neuron model has the advantage of faster speed of simulations and the ease of understanding complex phenomena. [22][23][24] Map-based discrete neuron models have become most popular in recent years.…”

Section: Introductionmentioning

confidence: 99%

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

XiaoHua

Yang

et al. 2023

Chinese Phys. B

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At present, many neuron models have been proposed, which can be divided into discrete neuron models and continuous neuron models. Discrete neuron models have the advantage of faster simulation speed and the ease of understanding complex dynamic phenomena. Due to the properties of memorability, nonvolatility and local activity, locally active discrete memristors (LADMs) are also suitable for simulating synapses. In this paper, we use a LADM to mimic synapses and establish a Rulkov neural network model. It is found that the change of coupling strength and the initial state of the LADM leads to multiple firing patterns of the neural network. In addition, considering the influence of neural network parameters and the initial state of the LADM, numerical analysis methods such as phase diagram and timing diagram are used to study the phase synchronization. As the system parameters and the initial states of the LADM change, the LADM coupled Rulkov neural network exhibits synchronization transition and synchronization coexistence.

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Discrete hybrid Izhikevich neuron model: Nodal and network behaviours considering electromagnetic flux coupling

Cited by 39 publications

References 16 publications

On the analysis of a heterogeneous coupled network of memristive Chialvo neurons.

On the analysis of a heterogeneous coupled network of memristive Chialvo neurons.

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

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