Biologically Plausible Learning

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Overview

Biologically plausible learning asks how networks can acquire useful representations and skills without relying on learning mechanisms that conflict with neural physiology. Approaches include local Hebbian and three-factor plasticity, dendritic and recurrent learning, feedback alignment and error diffusion for credit assignment, and structural plasticity, homeostasis, and replay for memory and continual learning. Researchers test these methods on tasks such as image classification, sequence learning, causal discovery, and lifelong learning, while examining constraints including Dale’s law and comparisons with brain representations. A central tension is whether biologically grounded rules can match the performance and scale of backpropagation while avoiding its global, gradient-derived error signals.

Latest papers

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27 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

  1. On the stability of learning

    Oct 26, 2026John J. VastolaNeural Network StabilityNeural Network Training Dynamics

    Department of Neurobiology, Harvard Medical School · Department of Psychology and Center for Brain Science, Harvard University · Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University

  2. Engram Neuron Inspired Adaptive Memorization for Lifelong Learning

    Oct 10, 2026Suiyang Jiang, Bomin Wang, Hangqi Zhou +2Continual LearningMemory-Augmented Neural Networks

    School of Data Science, Fudan University, 220 Handan Road, Shanghai, 200433, China · Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Shanghai, 200433, China

  3. Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1

    Sep 10, 2026Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado +2Compute-in-MemoryBayesian Inference

    CEA-List, Grenoble, France

  4. A Bio-Plausible Visual Neural Network for Locust-Inspired Collision Perception

    Sep 9, 2026Qinbing Fu, Jiani Li, Jiajun Huang +1Visual NeuroscienceSpiking Neural Networks

    School of Mathematics and Information Science, Guangzhou University, China

  5. Formation of structural attractors in neuromorphic systems

    Sep 6, 2026Yurii Parzhyn, Alexander Schwarzmann, Mykyta Lapin +1Neural Network Training DynamicsNeuromorphic Computing

    Augusta University, Augusta, USA · National Technical University "Kharkiv Polytechnic Institute", Kharkiv, Ukraine

  6. Biologically Inspired Mechanisms for Facilitating Grokking in Multilayer Perceptrons

    Aug 28, 2026Florin LeonGrokkingNeural Network Generalization

    Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Ias,i Bd. Mangeron 27, 700050 Ias,i, Romania

  7. The Forward-Backward Disconnect: State Dynamics, Credit Assignment, and Biological Grounding in Neural Computation

    Aug 20, 2026Hadi Al Mubasher, Mariette AwadCredit AssignmentNeural Network Training Dynamics

    Department of Electrical and Computer Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Lebanon

  8. In Two Minds about Lifelong Learning: Exploring Hemispheric Redundancy and Specialisation in Neural Models

    Aug 20, 2026Benjamin Smith, Levin Kuhlmann, Kaushik Roy +1Online Continual LearningBiologically Plausible Learning

    Data Science and AI, Monash University, Wellington Road, Clayton, 3800, Victoria, Australia · CSIRO Robotics, CSIRO, Australia · Cerenaut, Victoria, Australia

  9. Evaluation Resolution Confounds Learning-Rule Comparisons in Model-Brain RSA of Early Visual Cortex

    Aug 11, 2026Nils LeuteneggerRepresentational Similarity AnalysisVisual Neuroscience

    Independent Researcher, Switzerland

  10. Learning in Deep Networks under Dale's Constraint

    Aug 7, 2026Roy Abel, Shimon UllmanBackpropagationBiologically Plausible Learning

    Weizmann Institute of Science

  11. Effective pruning of task-trained recurrent neural networks using noisy fluctuations and connection rescaling

    Aug 5, 2026Sanjith Senthil, Rishidev ChaudhuriRecurrent Neural NetworksBiologically Plausible Learning

    1The Harker School, San Jose, CA 95129, USA · Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis CA 95616 · Department of Mathematics, University of California, Davis, Davis CA 95616 +1

  12. Diffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks

    Jun 30, 2026Yutaro Yamada, Luca Grillotti, Rujikorn Charakorn +3Credit Assignment in RLBiologically Plausible Learning

    Sakana AI

  13. A Programmer's Guide to Cascaded Adaptive Combiners: Online Learning by Biologically Accurate Models of Multilayer Neuron Networks

    Jun 12, 2026Martin Nilsson, Denis KleykoBiologically Plausible LearningAdaptive Learning

    RISE Research Institutes of Sweden, Kista, Sweden · Örebro University, Örebro, Sweden

  14. Score Broadcast and Decorrelation: A General Framework for Broadcast-Based Credit Assignment

    May 28, 2026Mustafa Uzun, Mete Erdogan, Cengiz Pehlevan +1Credit AssignmentBiologically Plausible Learning

    KUIS AI Center, Koc University, Turkey · Electrical and Electronics Engineering, Koc University, Turkey · Department of Electrical Engineering, Stanford University, USA +3

  15. Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning Rules

    May 28, 2026Nils LeuteneggerNeural Representation GeometryPredictive Coding

    Independent Researcher, Switzerland

  16. Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data

    May 18, 2026Ariane Delrocq, Wu S. Zihan, Guillaume Bellec +1Self-Supervised LearningHierarchical Representation Learning

    School of Life Science and School of Computer and Communications Sciences, EPFL, Lausanne, Switzerland · Machine Learning Research Unit, TU Wien, Vienna, Austria

  17. Biological Plausibility and Representational Alignment of Feedback Alignment in Convolutional Networks

    May 8, 2026Jake Lance, Larry KieuRepresentational Similarity AnalysisBackpropagation

    University of Toronto

  18. Causal Learning with Neural Assemblies

    Apr 29, 2026Evangelia Kopadi, Dimitris KallesNeural ProcessesCausal Discovery

    Hellenic Open University Greece

  19. Mistake gating leads to energy and memory efficient continual learning

    Apr 15, 2026Aaron Pache, Mark CW van RossumEnergy-Efficient MLContinual Learning

    School of Mathematical Sciences University of Nottingham, Nottingham NG7 2RD, United Kingdom · School of Psychology University of Nottingham, Nottingham NG7 2RD, United Kingdom

  20. Frame Theoretical Derivation of Three Factor Learning Rule for Oja's Subspace Rule

    Apr 3, 2026Taiki YamadaHebbian LearningPrincipal Component Analysis

  21. Inhibitory normalization of error signals improves learning in neural circuits

    Mar 18, 2026Roy Henha Eyono, Daniel Levenstein, Arna Ghosh +2Biologically Plausible Learning

    Mila-Quebec AI Institute · School of Computer Science, McGill University · Yale University +5

  22. Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations

    Mar 3, 2026Patrick Inoue, Florian Röhrbein, Andreas KnoblauchBiologically Plausible LearningSparse Neural Networks

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

  23. Heterogeneous Time Constants Improve Stability in Equilibrium Propagation

    Mar 3, 2026Yoshimasa Kubo, Suhani Pragnesh Modi, Smit PatelEquilibrium PropagationNeural Network Training Dynamics

    Department of Computer Science, Lakehead University

  24. Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning

    Feb 28, 2026Patrick Stricker, Florian Röhrbein, Andreas KnoblauchEnergy-Efficient MLBiologically Plausible Learning

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

  25. How Biological Synapses Self-Assemble Gradient Learning

    Dec 28, 2024Qianli Liao, Liu Ziyin, Yulu Gan +3Biologically Plausible LearningNeural Processes

    Center for Brains, Minds, and Machines, MIT · McGovern Institute, MIT · Research Laboratory of Electronics, MIT +3

  26. ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks

    Feb 26, 2024Laura Kriener, Ben von Hünerbein, Kristin Spicher +5Neural ProcessesRecurrent Neural Networks

    Department of Physiology, University of Bern, 3012 Bern, Switzerland. · Institute of Neuroinformatics, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland. · Catlab Engineering GmbH, 82284 Grafrath, Germany.

  27. One-shot learning of paired association navigation with biologically plausible schemas

    Jun 7, 2021M Ganesh Kumar, Cheston Tan, Camilo Libedinsky +2Few-Shot LearningKnowledge Representation

    Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore · The N.1 Institute for Health, National University of Singapore · Engineering Design and Innovation Centre, College of Design and Engineering, National University of Singapore +8