Coherence resonance in a memristive map neuron and adaptive energy regulation DOI
Yixuan Chen, Feifei Yang, Chunni Wang

и другие.

Modern Physics Letters B, Год журнала: 2024, Номер unknown

Опубликована: Сен. 18, 2024

Involvement of memristive term and additive physical variables including magnetic flux charge can enhance the description biophysical neurons. Neural circuits coupled with memristors be built tamed to mimic intrinsic characteristics dynamical properties biological neurons, these oscillator models are effective in predicting mode transition neural activities self-organization collective electric behaviors networks. Any proposal map neurons requires reliable description. For example, energy definition self-adaptive working mechanism crucial verify reliability maps. A capacitive variable is useful describe membrane potential, while complexity ion channels careful evaluation by using inductive relative electromagnetic field. In this work, a charge-controlled memristor connected an inductor series for building hybrid channel, then capacitor nonlinear resistor combined couple channel. As result, simple circuit designed discern inner effect field synchronously. The function defined verified theoretical proof. Furthermore, linear transformation applied convert neuron into exact description, which its dynamics will controlled adaptive law when beyond threshold. Additive noise imposed induce coherence resonance, detected statistical analysis average value Hamilton during changes intensity. This scheme provides guidance maps regulation explained from aspects.

Язык: Английский

Effect of the electromagnetic induction on a modified memristive neural map model DOI Creative Commons
Prasina Alexander, Fatemeh Parastesh,

Ibrahim Ismael Hamarash

и другие.

Mathematical Biosciences & Engineering, Год журнала: 2023, Номер 20(10), С. 17849 - 17865

Опубликована: Янв. 1, 2023

The significance of discrete neural models lies in their mathematical simplicity and computational ease. This research focuses on enhancing a map model by incorporating hyperbolic tangent-based memristor. study extensively explores the impact magnetic induction strength model's dynamics, analyzing bifurcation diagrams presence multistability. Moreover, investigation extends to collective behavior coupled memristive maps with electrical, chemical, connections. synchronization these is examined, revealing that chemical coupling exhibits broader area. Additionally, diverse chimera states cluster synchronized are identified discussed.

Язык: Английский

Процитировано

4

Hypergraph of Functional Connectivity Based on Event-Related Coherence: MEG Data Analysis DOI Open Access
Natalia Peña Serrano, R. Jaimes-Reátegui, Alexander N. Pisarchik

и другие.

Опубликована: Янв. 26, 2024

We construct hypergraphs to analyze functional brain connectivity, leveraging event-related coherence in magnetoencephalography (MEG) data during the visual perception of a flickering image. Principal network characteristics are computed for delta, theta, alpha, beta, and gamma frequency ranges. Employing measure, statistical estimate correlation between signal pairs across frequencies, we generate edge time series, depicting how an evolves over time. This forms basis constructing edge-to-edge connectivity network. Emphasizing hyperedges as connected components absolute-valued network, focus on exploring these context individual variability. Our coherence-based hypergraph construction specifically addresses among four lobes: frontal, parietal, temporal, occipital. approach enables nuanced exploration differences within diverse bands, providing insights into dynamical nature tasks. The results furnish compelling evidence supporting hypothesis cortico-cortical interactions occurring varying scales. derived illustrates robust activation patterns specific regions, indicative their engagement cognitive contexts different bands. findings suggest potential integration or multifunctionality examined lobes, contributing valuable perspectives our understanding dynamics perception.

Язык: Английский

Процитировано

1

Hypergraph of Functional Connectivity Based on Event-Related Coherence: Magnetoencephalography Data Analysis DOI Creative Commons
Natalia Peña Serrano, R. Jaimes-Reátegui, Alexander N. Pisarchik

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(6), С. 2343 - 2343

Опубликована: Март 11, 2024

We construct hypergraphs to analyze functional brain connectivity, leveraging event-related coherence in magnetoencephalography (MEG) data during the visual perception of a flickering image. Principal network characteristics are computed for delta, theta, alpha, beta, and gamma frequency ranges. Employing measure, statistical estimate correlation between signal pairs across frequencies, we generate an edge time series, depicting how evolves over time. This forms basis constructing edge-to-edge connectivity network. emphasize hyperedges as connected components absolute-valued Our coherence-based hypergraph construction specifically addresses among four lobes both hemispheres: frontal, parietal, temporal, occipital. approach enables nuanced exploration individual differences within diverse bands, providing insights into dynamic nature tasks. The results furnish compelling evidence supporting hypothesis cortico–cortical interactions occurring varying scales. derived illustrates robust activation patterns specific regions, indicative their engagement cognitive contexts different bands. findings suggest potential integration or multifunctionality examined lobes, contributing valuable perspectives our understanding dynamics perception.

Язык: Английский

Процитировано

1

Reduced-order dynamics of coupled self-induced stochastic resonance quasiperiodic oscillators driven by varying noise intensities DOI
Jinjie Zhu, Feng Zhao,

Xianbin Liu

и другие.

Nonlinear Dynamics, Год журнала: 2024, Номер 112(20), С. 17671 - 17681

Опубликована: Июнь 24, 2024

Язык: Английский

Процитировано

1

Coherence resonance in a memristive map neuron and adaptive energy regulation DOI
Yixuan Chen, Feifei Yang, Chunni Wang

и другие.

Modern Physics Letters B, Год журнала: 2024, Номер unknown

Опубликована: Сен. 18, 2024

Involvement of memristive term and additive physical variables including magnetic flux charge can enhance the description biophysical neurons. Neural circuits coupled with memristors be built tamed to mimic intrinsic characteristics dynamical properties biological neurons, these oscillator models are effective in predicting mode transition neural activities self-organization collective electric behaviors networks. Any proposal map neurons requires reliable description. For example, energy definition self-adaptive working mechanism crucial verify reliability maps. A capacitive variable is useful describe membrane potential, while complexity ion channels careful evaluation by using inductive relative electromagnetic field. In this work, a charge-controlled memristor connected an inductor series for building hybrid channel, then capacitor nonlinear resistor combined couple channel. As result, simple circuit designed discern inner effect field synchronously. The function defined verified theoretical proof. Furthermore, linear transformation applied convert neuron into exact description, which its dynamics will controlled adaptive law when beyond threshold. Additive noise imposed induce coherence resonance, detected statistical analysis average value Hamilton during changes intensity. This scheme provides guidance maps regulation explained from aspects.

Язык: Английский

Процитировано

1