Hebbian Learning

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Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints
Jul 17, 2026Patrick Inoue, Florian Röhrbein, Andreas KnoblauchHebbian Learning Synaptic Plasticity
KEIM Institute, Albstadt-Sigmaringen University, Germany · Department of Computer Science, Chemnitz University of Technology, Germany
Nonlinear Bandit
Jul 8, 2026Tianshuo Zheng, Ting Wu, Zhi-Hua Zhou +1Optimal Regret Loss Function
School of Mathematics, Nanjing University, Nanjing, 210093, China · School of Artificial Intelligence, Nanjing University, National Key Laboratory for Novel Software Technology, Nanjing, 210023, China · School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China
Rank-Order N-of-M Codes for Sparse Distributed Memory: Disentangling Representation and Learning Effects in Noise Robustness Against Contemporary Neuromorphic Architectures
Jul 3, 2026Joy BoseNeuromorphic Computing Hebbian Learning
Independent Researcher Bengaluru, India
Supervised Hebbian learning in Deep Counterstream Associative Networks
Jun 28, 2026Andreas KnoblauchHebbian Learning Backpropagation
Albstadt-Sigmaringen University, KEIM Institute, Poststrasse 6, 72458 Albstadt-Ebingen, Germany
Adaptive Hebbian Memory Routing in Vision Transformers for Few-Shot Learning
Jun 23, 2026Mohammed Yusuf Mujawar, Noorbakhsh Amiri GolilarzFew-Shot Learning Vision Transformer
Department of Computer Science The University of Alabama, Tuscaloosa, AL, USA
Local Pheromone Network: Sparse Local Learning with Multi-Scale Synaptic Trails, Consolidation, and Replay
Jun 22, 2026Xingcheng Fu, Xianjun Chen, Zhihao LiHebbian Learning Synaptic Plasticity
STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning
Jun 7, 2026Sumin Park, Noseong ParkMixture-Of-Experts Large Language Model Routing
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Closed-form predictive coding via hierarchical Gaussian filters
May 19, 2026Aleksandrs Baskakovs, Sylvain Estebe, Kenneth Enevoldsen +3Predictive Coding Hebbian Learning
Center for Humanities Computing Aarhus University Aarhus, Denmark · Interacting Minds Center Aarhus University Aarhus, Denmark
Benchmarking local Hebbian learning rules for memory storage and prototype extraction
May 1, 2026Anders Lansner, Andreas Knoblauch, Naresh B Ravichandran +1Hebbian Learning Neuroscience
1KTH Royal Institute of Technology, EECS, Stockholm · 2Stockholm University, Dept. of Mathematics, Stockholm · 3KEIM-Institute, Albstadt-Sigmaringen University, Germany +1
Incremental learning for audio classification with Hebbian Deep Neural Networks
Apr 20, 2026Riccardo Casciotti, Francesco De Santis, Alberto Antonietti +1Hebbian Learning Class-Incremental Learning
Signal Processing Research Centre, Tampere University, Tampere, Finland · Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Italy
HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents
Apr 18, 2026Jinchang Zhu, Jindong Li, Cheng Zhang +2Episodic Memory Large Language Model Agents
The Hong Kong University of Science and Technology (Guangzhou) · Jilin University · The Chinese University of Hong Kong
Building Supervision into Hebbian Plasticity through Spike Agreement
Jan 13, 2026Gouri Lakshmi S, Athira Chandrasekharan, Harshit Kumar +3Spiking Neural Networks Hebbian Learning
Indian Institute of Science Education and Research Thiruvananthapuram, Vithura, Thiruvananthapuram, Kerala 695551, India
Formalized Hopfield Networks and Boltzmann Machines
Dec 8, 2025Matteo Cipollina, Michail Karatarakis, Freek WiedijkHopfield Networks Hebbian Learning
Axiomatic AI, Barcelona, Spain · Radboud University, Nijmegen, The Netherlands
Chameleon: An Adaptive AI-Driven Honeypot Architecture Using Threat-Calibrated Particle Swarm Optimization and Semantic Deception Rapidly-Exploring Random Trees
Date pendingRohit Swami, Tushar Singh, Akash Warde +1Honeypots Threat Detection
Dept. of AI and Data Science, SIES Graduate School of Technology, Mumbai, India

Jul 17, 2026, cs.LGJ/K move · Enter open · S save

Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints

Patrick Inoue, Florian Röhrbein, Andreas Knoblauch

KEIM Institute, Albstadt-Sigmaringen University, Germany · Department of Computer Science, Chemnitz University of Technology, Germany

Hebbian Learning Synaptic Plasticity Representational Capacity Structural Constraints

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Introduction: Biological systems face anatomical and metabolic constraints, including costly synaptic maintenance and limited connectivity. These constraints favor neural codes that compress behaviorally relevant information into low-redundancy patterns. We test whether an excitatory competitive Hebbian rule can support synaptic resource allocation under such constraints and whether the resulting representations occupy a more favorable cost-performance regime than reference learning rules. Methods: Representational cost is quantified using mutual-information-based measures derived from the Variational Information Bottleneck. Experiments use fixed audiovisual embeddings from three audiovisual benchmarks (AVE, Kinetics-Sounds, VGGSound100) to isolate downstream associative plasticity. Hebbian learning is compared with Dense Difference Target Propagation (DDTP) and backpropagation (BP) under matched sparsity and architectural constraints. Results: Hebbian learning achieves lower task-information cost (CTI) than sparse BP and DDTP in the main compressed comparisons, while reaching CTI values comparable to shallow BP with nonnegative weights. Rather than uniformly improving classification performance, Hebbian learning shifts the trade-off between task-relevant information and representational cost, yielding lower CTI at comparable functional performance in several settings. Discussion: The results indicate a cost-performance trade-off rather than uniform accuracy gains. For a given level of task-relevant information, Hebbian representations retain less input information while preserving functional performance, although accuracy is slightly reduced on some datasets. These findings support interpreting Hebbian learning as a mechanism for synaptic resource allocation rather than as a general strategy for maximizing audiovisual classification accuracy.

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