.png){kind=icon}
.png){kind=icon}
Return
Alternative discrete Rulkov neuron models: nonlinear function approximation, dynamical analysis, and circuit implementation
.png){kind=icon}
PRE
AI
DOI：10.1140/epjp/s13360-026-07960-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Constructing a simple and biologically interpretable neuron model is crucial for understanding the firing mechanism of biological neurons and supporting the integration and engineering applications of neuromorphic circuits. However, the rational nonlinear term in the conventional Rulkov neuron model leads to high complexity in hardware implementation, which limits its practical applications. To solve this problem, this paper uses an exponential nonlinear function to approximate and simplify the key rational nonlinear term of the original Rulkov model, and proposes a simplified discrete Rulkov neuron model. The proposed model not only retains the typical dynamic characteristics and firing patterns of the original model, but also demonstrates rich bifurcation and complex dynamic behaviors. Parameter-dependent dynamical analysis, including bifurcation diagrams, Lyapunov exponent spectra, and dynamical maps, is carried out to reveal its dynamic properties systematically. Furthermore, an equivalent hardware circuit is designed and verified by PSIM simulations. The numerical and circuit simulation results are highly consistent, which verifies the effectiveness of the simplified model, the correctness of dynamical analysis, and the feasibility of hardware implementation. The model has potential application value in neuromorphic computing, information encryption, and chaotic secure communication.
Journal
T
IF:
0
Papers:
490
Citations:
0