Image de couverture de livre

Eventprop training for efficient neuromorphic applications

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Shoesmith, Thomas, Knight, James C., Mészáros, Balázs, Timcheck, Jonathan, Nowotny, Thomas
Format:	Preprint
Publié:	2025
Sujets:	Neural and Evolutionary Computing Emerging Technologies I.2.6; I.5.1; C.1.3
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks
par: Nowotny, Thomas, et autres
Publié: (2022)

Enhanced Protein Intrinsic Disorder Prediction Through Dual-View Multiscale Features and Multi-objective Evolutionary Algorithm
par: Wang, Shaokuan, et autres
Publié: (2026)

Spiking Neural Network Architecture Search: A Survey
par: Svoboda, Kama, et autres
Publié: (2025)

Theoretical Analysis of the Advantage of Deepening Neural Networks
par: Esaki, Yasushi, et autres
Publié: (2020)

Energy-Efficient Neuromorphic Computing for Edge AI: A Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimization
par: Imanov, Olaf Yunus Laitinen, et autres
Publié: (2026)

Expanding continual few-shot learning benchmarks to include recognition of specific instances
par: Kowadlo, Gideon, et autres
Publié: (2022)

Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data
par: Geglio, Anthony, et autres
Publié: (2022)

Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience
par: Gayler, Ross W.
Publié: (2004)

BMR and BWR: Two simple metaphor-free optimization algorithms for solving real-life non-convex constrained and unconstrained problems
par: Rao, Ravipudi Venkata, et autres
Publié: (2024)

K-Way Energy Probes for Metacognition Reduce to Softmax in Discriminative Predictive Coding Networks
par: Cacioli, Jon-Paul
Publié: (2026)

Collapse or Preserve: Data-Dependent Temporal Aggregation for Spiking Neural Network Acceleration
par: Qin, Jiahao
Publié: (2026)

Pendulum Model of Spiking Neurons
par: Bose, Joy
Publié: (2025)

Asynchronous Stochastic Gradient Descent with Decoupled Backpropagation and Layer-Wise Updates
par: Fokam, Cabrel Teguemne, et autres
Publié: (2024)

Neural Attention: A Novel Mechanism for Enhanced Expressive Power in Transformer Models
par: DiGiugno, Andrew, et autres
Publié: (2025)

Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
par: Baik, Hyeongmeen, et autres
Publié: (2026)

Pulse-Driven Neural Architecture: Learnable Oscillatory Dynamics for Robust Continuous-Time Sequence Processing
par: Sharma, Paras
Publié: (2026)

Deep learning in a bilateral brain with hemispheric specialization
par: Rajagopalan, Chandramouli, et autres
Publié: (2022)

PolyGLU: State-Conditional Activation Routing in Transformer Feed-Forward Networks
par: Medeiros, Daniel Nobrega
Publié: (2026)

Scalable Nested Optimization for Deep Learning
par: Lorraine, Jonathan
Publié: (2024)

Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm
par: Sharifi, Zaniar, et autres
Publié: (2024)

On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers
par: Štrupl, Miroslav, et autres
Publié: (2025)

Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networks
par: Zuo, Lin, et autres
Publié: (2024)

Decoding Phone Pairs from MEG Signals Across Speech Modalities
par: de Zuazo, Xabier, et autres
Publié: (2025)

Perforated Neural Networks for Keyword Spotting
par: Gopal, Vishy, et autres
Publié: (2026)

mlx-snn: Spiking Neural Networks on Apple Silicon via MLX
par: Qin, Jiahao
Publié: (2026)

Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agents
par: Malik, Raaghav, et autres
Publié: (2025)

Tensor Logic: The Language of AI
par: Domingos, Pedro
Publié: (2025)

The Hidden Linear Structure in Score-Based Models and its Application
par: Wang, Binxu, et autres
Publié: (2023)

Mamba base PKD for efficient knowledge compression
par: Medina, José, et autres
Publié: (2025)

Toward Thermodynamic Reservoir Computing: Exploring SHA-256 ASICs as Potential Physical Substrates
par: de Lafuente, Francisco Angulo, et autres
Publié: (2026)

Timing-Based Backpropagation in Spiking Neural Networks Without Single-Spike Restrictions
par: Yamamoto, Kakei, et autres
Publié: (2022)

Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks
par: Kunc, Vladimír, et autres
Publié: (2024)

Exploring the Relationship: Transformative Adaptive Activation Functions in Comparison to Other Activation Functions
par: Kunc, Vladimír
Publié: (2024)

Spiking Sequence Machines and Transformers
par: Bose, Joy
Publié: (2026)

Isotropic Activation Functions Enable Deindividuated Neurons and Adaptive Topologies
par: Bird, George
Publié: (2026)

Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image Classification
par: Hoang, Anh T., et autres
Publié: (2023)

The GAIN Model: A Nature-Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model
par: Hooper, Gage K. R.
Publié: (2025)

The Spotlight Resonance Method: Resolving the Alignment of Embedded Activations
par: Bird, George
Publié: (2025)

AGOP-IxG: A Gradient Covariance Filter for Local Feature Attribution on Tabular Data, with a Controlled Benchmark
par: Katakam, Raj Kiran Gupta
Publié: (2026)

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies
par: Lachapelle, Sébastien, et autres
Publié: (2024)