Biologically Plausible Learning

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1
Sep 12, 2026Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado +2Compute-in-Memory Bayesian Inference
CEA-List, Grenoble, France
A Bio-Plausible Visual Neural Network for Locust-Inspired Collision Perception
Sep 9, 2026Qinbing Fu, Jiani Li, Jiajun Huang +1Visual Neuroscience Spiking Neural Networks
School of Mathematics and Information Science, Guangzhou University, China
Evaluation Resolution Confounds Learning-Rule Comparisons in Model-Brain RSA of Early Visual Cortex
Aug 11, 2026Nils LeuteneggerRepresentational Similarity Analysis Visual Neuroscience
Independent Researcher, Switzerland
Learning in Deep Networks under Dale's Constraint
Aug 7, 2026Roy Abel, Shimon UllmanBackpropagation Biologically Plausible Learning
Weizmann Institute of Science
Effective pruning of task-trained recurrent neural networks using noisy fluctuations and connection rescaling
Aug 5, 2026Sanjith Senthil, Rishidev ChaudhuriRecurrent Neural Networks Biologically 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
Diffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks
Jun 30, 2026Yutaro Yamada, Luca Grillotti, Rujikorn Charakorn +3Credit Assignment in RL Biologically Plausible Learning
Sakana AI
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 Learning Adaptive Learning
RISE Research Institutes of Sweden, Kista, Sweden · Örebro University, Örebro, Sweden
Score Broadcast and Decorrelation: A General Framework for Broadcast-Based Credit Assignment
May 28, 2026Mustafa Uzun, Mete Erdogan, Cengiz Pehlevan +1Credit Assignment Biologically Plausible Learning
KUIS AI Center, Koc University, Turkey · Electrical and Electronics Engineering, Koc University, Turkey · Department of Electrical Engineering, Stanford University, USA +3
Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning Rules
May 28, 2026Nils LeuteneggerNeural Representation Geometry Predictive Coding
Independent Researcher, Switzerland
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 Learning Hierarchical Representation Learning
School of Life Science and School of Computer and Communications Sciences, EPFL, Lausanne, Switzerland · Machine Learning Research Unit, TU Wien, Vienna, Austria
Biological Plausibility and Representational Alignment of Feedback Alignment in Convolutional Networks
May 8, 2026Jake Lance, Larry KieuRepresentational Similarity Analysis Backpropagation
University of Toronto
Causal Learning with Neural Assemblies
Apr 29, 2026Evangelia Kopadi, Dimitris KallesNeural Processes Causal Discovery
Hellenic Open University Greece
Mistake gating leads to energy and memory efficient continual learning
Apr 15, 2026Aaron Pache, Mark CW van RossumEnergy-Efficient ML Continual Learning
School of Mathematical Sciences University of Nottingham, Nottingham NG7 2RD, United Kingdom · School of Psychology University of Nottingham, Nottingham NG7 2RD, United Kingdom
ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks
Feb 26, 2024Laura Kriener, Ben von Hünerbein, Kristin Spicher +5Neural Processes Recurrent 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.

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

Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado, Devendra Vyas+1

CEA-List, Grenoble, France

Compute-in-Memory Bayesian Inference Neuromorphic Computing Biologically Plausible Learning

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Learning and decision-making in animals are often modeled as Bayesian processes, where sensory evidence is integrated with prior beliefs to guide behavior in the face of uncertainty. But what are the inherent neural dynamics that give rise to this ability, and how could they be replicated in computing systems? This abstract discusses a biologically grounded framework in which noisy neural and synaptic dynamics perform inference and learning via stochastic sampling from an internal energy function, capturing uncertainty over latent states and model parameters through neural and synaptic variability, respectively. This enables approaches such as predictive coding networks to account for epistemic uncertainty via Markov chain Monte Carlo sampling. Drawing a parallel between intrinsic noise in biological systems and electrical noise in emerging probabilistic analogue memory technologies, we highlight how analogue in-memory computing hardware naturally emerges as the solution for massively scalable and energy-efficient probabilistic inference.

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