Cover Image

Bio-Inspired Adaptive Neurons for Dynamic Weighting in Artificial Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Islam, Ashhadul, Bouzerdoum, Abdesselam, Belhaouari, Samir Brahim
Format:	Preprint
Published:	2024
Subjects:	Neural and Evolutionary Computing Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

CHNNet: An Artificial Neural Network With Connected Hidden Neurons
by: Shahir, Rafiad Sadat, et al.
Published: (2023)

A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs
by: Somvanshi, Shriyank, et al.
Published: (2025)

Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks
by: Bej, Saptarshi, et al.
Published: (2025)

A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks
by: Meng, Qingyan, et al.
Published: (2025)

No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks
by: Fan, Feng-Lei, et al.
Published: (2024)

Dynamically Weighted Momentum with Adaptive Step Sizes for Efficient Deep Network Training
by: Wang, Zhifeng, et al.
Published: (2025)

Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks
by: Brenner, Rorry, et al.
Published: (2025)

A Neural Network Training Method Based on Neuron Connection Coefficient Adjustments
by: Jiang, Kun
Published: (2025)

Dataset Optimization for Chronic Disease Prediction with Bio-Inspired Feature Selection
by: Dyoub, Abeer, et al.
Published: (2023)

The Evolution of Learning Algorithms for Artificial Neural Networks
by: Baxter, Jonathan
Published: (2025)

Gated Recurrent Neural Networks with Weighted Time-Delay Feedback
by: Erichson, N. Benjamin, et al.
Published: (2022)

Learning from Emergence: A Study on Proactively Inhibiting the Monosemantic Neurons of Artificial Neural Networks
by: Wang, Jiachuan, et al.
Published: (2023)

Bio-Inspired Neuron Synapse Optimization for Adaptive Learning and Smart Decision-Making
by: Singh, Sreeja, et al.
Published: (2025)

Walking the Weight Manifold: a Topological Approach to Conditioning Inspired by Neuromodulation
by: Benjamin, Ari S., et al.
Published: (2025)

Synaptic Activation and Dual Liquid Dynamics for Interpretable Bio-Inspired Models
by: Farsang, Mónika, et al.
Published: (2026)

Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network
by: Oh, Hyunseok, et al.
Published: (2024)

Linearly Constrained Weights: Reducing Activation Shift for Faster Training of Neural Networks
by: Kutsuna, Takuro
Published: (2024)

Brain-Inspired Spike Echo State Network Dynamics for Aero-Engine Intelligent Fault Prediction
by: Liu, Mo-Ran, et al.
Published: (2024)

Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators
by: Yeung, Alexander, et al.
Published: (2025)

Exploiting Chaotic Dynamics as Deep Neural Networks
by: Liu, Shuhong, et al.
Published: (2024)

Learning to Forget: Continual Learning with Adaptive Weight Decay
by: Ramesh, Aditya A., et al.
Published: (2026)

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks
by: Moshruba, Ayana, et al.
Published: (2025)

Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks
by: Fofanah, Abdul Joseph, et al.
Published: (2026)

Self-Motivated Growing Neural Network for Adaptive Architecture via Local Structural Plasticity
by: Jia, Yiyang, et al.
Published: (2025)

Energy-Aware FPGA Implementation of Spiking Neural Network with LIF Neurons
by: Ali, Asmer Hamid, et al.
Published: (2024)

On Accelerating Deep Neural Network Mutation Analysis by Neuron and Mutant Clustering
by: Lyons, Lauren, et al.
Published: (2025)

Balanced Resonate-and-Fire Neurons
by: Higuchi, Saya, et al.
Published: (2024)

LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization
by: Apolinario, Marco Paul E., et al.
Published: (2024)

Structure of Artificial Neural Networks -- Empirical Investigations
by: Stier, Julian
Published: (2024)

Understanding the Convergence in Balanced Resonate-and-Fire Neurons
by: Higuchi, Saya, et al.
Published: (2024)

Cyclic Neural Network
by: Yang, Liangwei, et al.
Published: (2024)

Airborne Neural Network
by: Ranjan, Paritosh, et al.
Published: (2025)

Expressivity of Neural Networks with Random Weights and Learned Biases
by: Williams, Ezekiel, et al.
Published: (2024)

Should Under-parameterized Student Networks Copy or Average Teacher Weights?
by: Şimşek, Berfin, et al.
Published: (2023)

Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients
by: Zhou, Feifan, et al.
Published: (2026)

Spiking Neural Networks with Random Network Architecture
by: Dai, Zihan, et al.
Published: (2025)

Understanding Vector-Valued Neural Networks and Their Relationship with Real and Hypercomplex-Valued Neural Networks
by: Valle, Marcos Eduardo
Published: (2023)

Input Invex Neural Network
by: Sapkota, Suman, et al.
Published: (2021)

Physics-Modeled Neural Networks
by: Felipe-Sosa, Raul, et al.
Published: (2026)

Set-based Neural Network Encoding Without Weight Tying
by: Andreis, Bruno, et al.
Published: (2023)