Cover Image

Adjusting Dynamics of Hopfield Neural Network via Time-variant Stimulus

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Peng, Xuenan, Li, Chengqing, Zeng, Yicheng, Li, Chun-Lai
Format:	Preprint
Published:	2024
Subjects:	Neural and Evolutionary Computing Chaotic Dynamics 68T07
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Integer-State Dynamics of Quantized Spiking Neural Networks for Efficient Hardware Acceleration
by: Zhang, Lei
Published: (2026)

Reconfigurable Nonlinear Photonic Networks for In-Situ Learning and Memory Formation via Driven-Dissipative Dynamics
by: Yorke, Isaac
Published: (2026)

Pointer Networks Trained Better via Evolutionary Algorithms
by: Zhong, Muyao, et al.
Published: (2023)

Asymmetrically connected reservoir networks learn better
by: Rathor, Shailendra K., et al.
Published: (2024)

Jump-diffusion models of parametric volume-price distributions
by: Budhathoki, Anup, et al.
Published: (2025)

Identification of fixations and saccades in eye-tracking data using adaptive threshold-based method
by: Orioma, Charles, et al.
Published: (2025)

Learning Continuous Chaotic Attractors with a Reservoir Computer
by: Smith, Lindsay M., et al.
Published: (2021)

Panda: A pretrained forecast model for chaotic dynamics
by: Lai, Jeffrey, et al.
Published: (2025)

Chaotic Oscillator Networks for Classification Tasks
by: Ivas, Toni, et al.
Published: (2026)

Fractional Artificial Neural Networks for Growth Models
by: Najera-Tinoco, Juan Carlos, et al.
Published: (2025)

Reconstruction of dynamic systems using genetic algorithms with dynamic search limits
by: Rodríguez-Abreo, Omar, et al.
Published: (2024)

Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing
by: Sedehi, Omid, et al.
Published: (2025)

Machine Learning of Nonlinear Dynamical Systems with Control Parameters Using Feedforward Neural Networks
by: Sakaguchi, Hidetsugu
Published: (2024)

The boundary of neural network trainability is fractal
by: Sohl-Dickstein, Jascha
Published: (2024)

Harnessing and modulating chaos to sample from neural generative models
by: Chaudhuri, Rishidev, et al.
Published: (2024)

Analysis of chaotic dynamical systems with autoencoders
by: Almazova, N., et al.
Published: (2021)

Reservoir computing with logistic map
by: Arun, R., et al.
Published: (2024)

Designing Chaotic Attractors: A Semi-supervised Approach
by: Kabayama, Tempei, et al.
Published: (2024)

Attractor-merging Crises and Intermittency in Reservoir Computing
by: Kabayama, Tempei, et al.
Published: (2025)

Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime
by: Abdel-Ghani, Hend, et al.
Published: (2025)

Implementing Online Reinforcement Learning with Clustering Neural Networks
by: Smith, James E.
Published: (2024)

Reconstructing shared dynamics with a deep neural network
by: Benkő, Zsigmond, et al.
Published: (2021)

Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement Learning
by: Munn, Humphrey, et al.
Published: (2022)

Prediction performance of random reservoirs with different topology for nonlinear dynamical systems with different number of degrees of freedom
by: Rathor, Shailendra K., et al.
Published: (2025)

Contraction, Criticality, and Capacity: A Dynamical-Systems Perspective on Echo-State Networks
by: Singh, Pradeep, et al.
Published: (2025)

Persistence and Doubling of Chaotic Attractors in Coupled 3-Cell Hopfield Neural Networks
by: Fen, Mehmet Onur, et al.
Published: (2023)

Freezing chaos without synaptic plasticity
by: Huang, Weizhong, et al.
Published: (2025)

How high dimensional neural dynamics are confined in phase space
by: Wang, Shishe, et al.
Published: (2024)

Controlling Chaotic Maps using Next-Generation Reservoir Computing
by: Kent, Robert M., et al.
Published: (2023)

Proposed modified computational model for the amoeba-inspired combinatorial optimization machine
by: Miyajima, Yusuke, et al.
Published: (2024)

Revisiting time-variant complex conjugate matrix equations with their corresponding real field time-variant large-scale linear equations, neural hypercomplex numbers space compressive approximation approach
by: He, Jiakuang, et al.
Published: (2024)

Efficient Approximation to Analytic and $L^p$ functions by Height-Augmented ReLU Networks
by: Li, ZeYu, et al.
Published: (2026)

P1-KAN: an effective Kolmogorov-Arnold network with application to hydraulic valley optimization
by: Warin, Xavier
Published: (2024)

One-Spike SNN: Single-Spike Phase Coding with Base Manipulation for ANN-to-SNN Conversion Loss Minimization
by: Hwang, Sangwoo, et al.
Published: (2024)

A parametric activation function based on Wendland RBF
by: Darehmiraki, Majid
Published: (2025)

Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data
by: Maksymov, Ivan S.
Published: (2024)

Neural Networks Trained by Weight Permutation are Universal Approximators
by: Cai, Yongqiang, et al.
Published: (2024)

Enhancing Computational Efficiency in Multiscale Systems Using Deep Learning of Coordinates and Flow Maps
by: Hamid, Asif, et al.
Published: (2024)

Hierarchy of chaotic dynamics in random modular networks
by: Kuśmierz, Łukasz, et al.
Published: (2024)

Game-Theoretic Gradient Control for Robust Neural Network Training
by: Zaitseva, Maria, et al.
Published: (2025)