Dynamical analysis of an improved FitzHugh-Nagumo neuron model with multiplier-free implementation

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

“…With other parameters constant, equation (10) will have different solutions as the external current I, memristive autapse strength α, and the degree of flux leakage β are varied, and the system will get different equilibrium points S = (x, y, z, w).…”

“…The multistability of a system means that when the system parameters are unchanged and different initial states are chosen, the system can generate three or more different coexisting attractors or coexisting firing patterns. When the external current is I = 4 and the memristive autapse strength is α = 2, the calculation of equation (10) shows that the proposed LAMHR neuron model has no equilibrium point, and so this model may have specific hidden attractors or hidden firing patterns. When the initial value of the memristive autaptic variable w(0) varies in the interval [−5, 5], the bifurcation diagram and the corresponding Lyapunov exponent are depicted in figure 14(a).…”

“…The relationship between the electromagnetic fields produced near the membrane and the ion flows is crucial for understanding how neurons process information [6]. To explore the irregular firing activities in the nervous system, neuroscientists have proposed artificial neuronal models to simulate and analyze the firing patterns of neurons, such as the HR model [7,8], FitzHugh-Nagumo (FHN) model [9][10][11], Hopfield Neural Network (HNN) model [12][13][14] and others [15,16]. The HR neuron model has attracted much attention, which has simple mathematical expression and rich characterization of electrical activity, and thus has become one of the paradigms of artificial neuron modeling.…”

Coexistence behavior of asymmetric attractors in hyperbolic-type memristive Hopfield neural network and its application in image encryption

“…The nervous system, comprising a vast network of interconnected neurons, [1,2] is one of the most complex systems in the human body. Neurons communicate and transmit electrochemical signals to accomplish various physiological functions, including perception, cognition, and motor control.…”

Editorial: Advances in non-linear systems and networks

“…Non-linear systems and networks have broad application prospects in the engineering fields of the Internet of Things, medical care, intelligent systems [1][2][3][4][5], etc. With the development of science and technology, according to the current research Frontier, it is not difficult to find that the research fields of non-linear systems and networks are also expanding, including chaotic systems and circuits [6][7][8][9], non-linear device models [10][11][12], memristors [13][14][15][16], neural networks [17][18][19][20][21], neural circuits [22][23][24], synchronous control [25-27] and application research in related fields [28][29][30][31].…”