Firing activities analysis of a novel small heterogeneous coupled network through a memristive synapse

Wang, Mengjiao; Peng, Jianhua; Zhang, Xinan; Iu, Herbert Ho-Ching; Li, Zhijun

doi:10.1007/s11071-023-08626-0

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…At present, there are two main categories to accomplish the circuit implementation of nonlinear dynamical system. One is the use of operational amplifiers, capacitors and resistors and other discrete components to form each independent functional module connected to form the analog circuit hardware implementation [61,62], or the use of super-large scale Field Programmable Gate Array (FPGA), Microcontroller Unit (MCU) and other digital processing chips through the D/A conversion output to form the circuit digital implementation [63,64]. During this part, the process of analog circuit design and simulation and digital circuit design and implementation of the suggested memristive neuron model are explained at length, and the experimental consequences are exhibited using an oscilloscope.…”

Section: Circuit Design and Implementationmentioning

confidence: 99%

Multiple firing patterns, energy conversion and hardware implementation within Hindmarsh-Rose-improved neuron model

Yan,

Jiang,

Zhang

et al. 2024

Phys. Scr.

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The transmission of information between neurons is accomplished in living organisms through synapses. The memristor is an electronic component that simulates the tunability of the strength of biological synaptic connections in artificial neural networks. This article constructs a novel type of locally active memristor and verifies by nonlinear theoretical analysis, locally active analysis and circuit simulation. The designed memristor is simulated as a biological autapse of Hindmarsh-Rose(HR) neuron to obtain the improved HR neuron model of memristive autapse, and the Hamilton energy is obtained according to Helmholtz theorem. By varying the external forcing current and the memristive autapse strength, this article analyses the changes of the Hamilton energy and explores its self-excited and hidden firing behavior. The analog circuit simulation and digital circuit implementation of the HR model confirm the consistency between the mathematical model and the actual behavior, which can advance the field of neuroscience and artificial intelligence.

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Section: Circuit Design and Implementationmentioning

confidence: 99%

Multiple firing patterns, energy conversion and hardware implementation within Hindmarsh-Rose-improved neuron model

Yan,

Jiang,

Zhang

et al. 2024

Phys. Scr.

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“…Due to their unique biomimetic properties, such as nanoscale size, low power consumption, and non-volatility, continuous memristors are considered the optimal choice for simulating synapses in analog form. [25][26][27][28][29][30] Compared to conventional electronic synapses, neural networks based on memristive synapses can more effectively mimic the complex firing activity of biological neural networks. For instance, memristor-coupled neural networks can generate grid multiscroll attractors, [31][32][33][34] diverse chaotic attractors that are employed in image encryption.…”

Section: Introductionmentioning

confidence: 99%

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Ma 马,

Xiong 熊,

Li 李

et al. 2024

Chinese Phys. B

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Synaptic crosstalk is a prevalent phenomenon among neuronal synapses, playing a crucial role in the transmission of neural signals. Therefore, considering synaptic crosstalk behavior and investigating the dynamical behavior of discrete neural networks is highly necessary. In this paper, we propose a heterogeneous discrete neural network (HDNN) consisting of a three-dimensional KTz discrete neuron and a Chialvo discrete neuron. These two neurons are coupled mutually by two discrete memristors and the synaptic crosstalk is considered. The impact of crosstalk strength on the firing behavior of the HDNN is explored through bifurcation diagrams and Lyapunov exponents. It is observed that the HDNN exhibits different coexisting attractors under varying crosstalk strengths. Furthermore, the influence of different crosstalk strengths on the synchronized firing of the HDNN is investigated, revealing a gradual attainment of phase synchronization between the two discrete neurons as the crosstalk strength decreases.

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“…To further understand the operating mechanism of the brain, it is essential and meaningful to study the dynamic behavior of neuron networks constructed with heterogeneous neurons distributed in different brain regions. [52][53][54] In addition, in the biological nervous systems, a large number of neurons work together, which inevitably leads to changes in the surrounding physiological environment. Therefore, noise is ubiquitous in the biological nervous systems.…”

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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Firing activities analysis of a novel small heterogeneous coupled network through a memristive synapse

Cited by 13 publications

References 39 publications

Multiple firing patterns, energy conversion and hardware implementation within Hindmarsh-Rose-improved neuron model

Multiple firing patterns, energy conversion and hardware implementation within Hindmarsh-Rose-improved neuron model

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

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

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