Image de couverture de livre

Counter-Current Learning: A Biologically Plausible Dual Network Approach for Deep Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kao, Chia-Hsiang, Hariharan, Bharath
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence Neural and Evolutionary Computing
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Memory Networks: Towards Fully Biologically Plausible Learning
par: Ruiz, Jacobo, et autres
Publié: (2024)

Multiscale Astrocyte Network Calcium Dynamics for Biologically Plausible Intelligence in Anomaly Detection
par: Iskar, Berk, et autres
Publié: (2025)

Towards Physical Plausibility in Neuroevolution Systems
par: Cortês, Gabriel, et autres
Publié: (2024)

Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
par: Holliday, Andrew, et autres
Publié: (2024)

CLASSP: a Biologically-Inspired Approach to Continual Learning through Adjustment Suppression and Sparsity Promotion
par: Ludwig, Oswaldo
Publié: (2024)

Meta-Learning Loss Functions for Deep Neural Networks
par: Raymond, Christian
Publié: (2024)

The Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks
par: Ishikawa, Takehiro
Publié: (2025)

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

Biologically Plausible Training of Deep Neural Networks Using a Top-down Credit Assignment Network
par: Chen, Jian-Hui, et autres
Publié: (2022)

Towards White Box Deep Learning
par: Satkiewicz, Maciej
Publié: (2024)

Automated Deep Learning for Load Forecasting
par: Keisler, Julie, et autres
Publié: (2024)

Understanding Deep Learning via Notions of Rank
par: Razin, Noam
Publié: (2024)

Deep Reinforcement Learning with Spiking Q-learning
par: Chen, Ding, et autres
Publié: (2022)

On Non-Linear operators for Geometric Deep Learning
par: Sergeant-Perthuis, Grégoire, et autres
Publié: (2022)

A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning
par: Qin, Lang, et autres
Publié: (2022)

Massive Dimensions Reduction and Hybridization with Meta-heuristics in Deep Learning
par: Khosrowshahli, Rasa, et autres
Publié: (2024)

Deep ARTMAP: Generalized Hierarchical Learning with Adaptive Resonance Theory
par: Melton, Niklas M., et autres
Publié: (2025)

Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks
par: Xiao, Mingqing, et autres
Publié: (2024)

SpikeRL: A Scalable and Energy-efficient Framework for Deep Spiking Reinforcement Learning
par: Tahmid, Tokey, et autres
Publié: (2025)

Deep Oscillatory Neural Network
par: Rohan, Nurani Rajagopal, et autres
Publié: (2024)

Half-Space Feature Learning in Neural Networks
par: Yadav, Mahesh Lorik, et autres
Publié: (2024)

Mechanistic Neural Networks for Scientific Machine Learning
par: Pervez, Adeel, et autres
Publié: (2024)

Introduction to Predictive Coding Networks for Machine Learning
par: Stenlund, Mikko
Publié: (2025)

A Learn-to-Optimize Approach for Coordinate-Wise Step Sizes for Quasi-Newton Methods
par: Lin, Wei, et autres
Publié: (2024)

t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making
par: Yue, William, et autres
Publié: (2024)

Learning Sequence Attractors in Recurrent Networks with Hidden Neurons
par: Lu, Yao, et autres
Publié: (2024)

Training Neural Networks with Optimal Double-Bayesian Learning
par: Bui, Vy, et autres
Publié: (2026)

Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances
par: Hart, Emma, et autres
Publié: (2024)

An Experimental Study on Decomposition-Based Deep Ensemble Learning for Traffic Flow Forecasting
par: Zhu, Qiyuan, et autres
Publié: (2024)

A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
par: Salvatori, Tommaso, et autres
Publié: (2022)

TACOS: Task Agnostic Continual Learning in Spiking Neural Networks
par: Soures, Nicholas, et autres
Publié: (2024)

Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning
par: Lala, Aarav, et autres
Publié: (2025)

TESS: A Scalable Temporally and Spatially Local Learning Rule for Spiking Neural Networks
par: Apolinario, Marco Paul E., et autres
Publié: (2025)

Toward Efficient Deep Spiking Neuron Networks:A Survey On Compression
par: Xie, Hui, et autres
Publié: (2024)

The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning
par: Nguyen, Manh V., et autres
Publié: (2024)

Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learning
par: Chen, Ding, et autres
Publié: (2024)

Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks
par: Miao, Jiaxing, et autres
Publié: (2024)

Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
par: Yin, Huifeng, et autres
Publié: (2024)

Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation
par: Tang, Bo, et autres
Publié: (2026)

SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning
par: Chen, Dong, et autres
Publié: (2024)