Understanding how external stimuli propagate in neural systems is an important challenge in the fields of neuroscience and nonlinear dynamics. Despite extensive studies over several decades, this problem remains poorly understood. In this work, we examine a simple "toy model" of an excitable medium, a linear chain of diffusely coupled FitzHugh-Nagumo neurons, and analyze the transmission of a sinusoidal signal injected into one of the neurons at the ends of the chain. We measure to what extent the propagation of the wave reaching the opposite end is affected by the frequency and amplitude of the signal, the number of neurons in the chain, and the strength of their mutual diffusive coupling. To quantify these effects, we measure the cross correlation between the time series of the membrane potentials of the end neurons. This measure allows us to detect the values of the parameters that delimit different propagation regimes.
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Chaos
March 2025
Department of Physics, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225-22451-900 Gávea, Rio de Janeiro, RJ, Brazil.
Understanding how external stimuli propagate in neural systems is an important challenge in the fields of neuroscience and nonlinear dynamics. Despite extensive studies over several decades, this problem remains poorly understood. In this work, we examine a simple "toy model" of an excitable medium, a linear chain of diffusely coupled FitzHugh-Nagumo neurons, and analyze the transmission of a sinusoidal signal injected into one of the neurons at the ends of the chain.
View Article and Find Full Text PDFPhys Rev E
December 2024
Central China Normal University, Department of Physics and Institute of Biophysics, Wuhan 430079, China.
When nodes in excitable system are stimulated, the system tends to form traveling waves or self-organized spiral waves, such as electrical signals in the heart and the spread of epidemics. Networks composed of these nodes can be influenced by higher-order interactions. We utilized the FitzHugh-Nagumo (FHN) model for nodes to construct a three-layer lattice network, incorporating higher-order interactions applicable to neuronal models.
View Article and Find Full Text PDFChaos
February 2025
Nonlinear Dynamics, Chaos and Complex Systems Group, Departamento de Física, Universidad Rey Juan Carlos, Tulipán s/n, Móstoles, 28933 Madrid, Spain.
We propose a nonlinear FitzHugh-Nagumo neuronal model with an asymmetric potential driven by both a high-frequency signal and a low-frequency signal. Our numerical analysis focuses on the influence of a state-dependent time delay on vibrational resonance and delay-induced resonance phenomena. The response amplitude at the low-frequency signal is explored to characterize the vibrational resonance and delay-induced resonance.
View Article and Find Full Text PDFPhys Life Rev
March 2025
Community Healthcare Center Dr. Adolf Drolc Maribor, Ulica talcev 9, 2000 Maribor, Slovenia; Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia; Complexity Science Hub, Metternichgasse 8, 1080 Vienna, Austria; Department of Physics, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea. Electronic address:
Synchrony in neuronal networks is crucial for cognitive functions, motor coordination, and various neurological disorders. While traditional research has focused on pairwise interactions between neurons, recent studies highlight the importance of higher-order interactions involving multiple neurons. Both types of interactions lead to complex synchronous spatiotemporal patterns, including the fascinating phenomenon of chimera states, where synchronized and desynchronized neuronal activity coexist.
View Article and Find Full Text PDFCogn Neurodyn
December 2024
Center for Research, SRM Institute of Science and Technology-Ramapuram, Chennai, India.
In this study, we investigate the impact of first and second-order coupling strengths on the stability of a synchronization manifold in a Discrete FitzHugh-Nagumo (DFHN) neuron model with memristor coupling. Master Stability Function (MSF) is used to estimate the stability of the synchronized manifold. The MSF of the DFHN model exhibits two zero crossings as we vary the coupling strengths, which is categorized as class .
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