Recurrent Neural Networks

Also known as RNN

Recurrent neural networks study how a model can process ordered inputs by repeatedly updating an internal state, which is useful for streaming data, long-term memory, and decisions under partial observability. Common approaches include gated LSTMs and GRUs, linear and state-space recurrences, continuous-time and spiking networks, and weight-shared loops for iterative reasoning; training ranges from backpropagation through time to online learning and parallel scans. Researchers test these models on state-tracking and recall tasks, language modeling, time-series forecasting, control, and physical-system prediction. A central tension is how to preserve useful information and stable learning over long sequences while keeping computation efficient and matching the accuracy and flexibility of Transformers.

Latest papers

303 papers47 papers in the last four weeks, up 15% on the four weeks before. 0.3% of all new papers.

Von Economo neurons enable reliable social skill acquisition in recurrent spiking neural networks: a computational account with clinical predictions
May 17, 2026Esila KeskinNeural Processes Recurrent Neural Networks
School of Computing and Creative Technologies, University of the West of England, Bristol, UK
Parallel Recursive LSTM
May 16, 2026Tristan Gaudreault, Yongyi MaoRecurrent Neural Networks Language Modeling
School of Electrical Engineering and Computer Science University of Ottawa Ottawa, ON
Rethinking the State Update Gate for Long-Sequence Recurrent 3D Reconstruction
May 16, 2026Kejun Ren, Lei Jin, Tianxin Huang +2Streaming 3D Reconstruction 3D Reconstruction
Beijing University of Posts and Telecommunications, Beijing, China · School of Computing and Data Science, The University of Hong Kong
PDRNN: Modular Data-driven Pedestrian Dead Reckoning on Loosely Coupled Radio- and Inertial-Signalstreams
May 14, 2026Peter Bauer, Andreas Porada, Felix Ott +2Recurrent Neural Networks Robot Localization
Fraunhofer Institute for Integrated Circuits IIS, 90411 Nürnberg, Germany · Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers
May 14, 2026Lukas Schelenz, Shobha Rajanna, Denis Gosalci +6Graph Neural Networks Multi-Agent Trajectory Prediction
Fraunhofer Institute for Integrated Circuits IIS, 90411 Nürnberg, Germany · Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning
May 14, 2026Luca Marzari, Enrico MarchesiniNeural Network Verification Partially Observable RL
1TU Wien, Vienna, Austria · 2Massachusetts Institute of Technology, Cambridge (MA), USA
Spontaneous symmetry breaking and Goldstone modes for deep information propagation
May 14, 2026Nabil Iqbal, T. Anderson Keller, Yue Song +2Symmetry Breaking Equivariant Neural Networks
Dept. of Mathematical Sciences, Durham University · Kempner Institute, Harvard University · College of AI, Tsinghua University +2
Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo
May 13, 2026Ejaaz Merali, Mohamed Hibat-Allah, Mohammad Kohandel +2Neural Quantum States Variational Monte Carlo
Department of Physics and Astronomy, University of California, Davis, California 95616, USA · Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada · Vector Institute, Toronto, Ontario, M5G 0C6, Canada +1
Phasor Memory Networks: Stable Backpropagation Through Time for Scalable Explicit Memory
May 13, 2026Sungwoo Goo, Hwi-yeol Yun, Sangkeun JungMemory-Augmented Neural Networks Memory-Augmented Language Models
College of Pharmacy Chungnam National University Daejeon, South Korea · Department of Computer Science & Engineering Chungnam National University Daejeon, South Korea
State-Space NTK Collapse Near Bifurcations
May 12, 2026James Hazelden, Eric Shea-BrownRecurrent Neural Networks Neural Network Training Dynamics
University of Washington
Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction
May 12, 2026Florian Hess, Florian Götz, Daniel DurstewitzNonlinear System Identification Recurrent Neural Networks
1Dept. of Theoretical Neuroscience, Central Institute of Mental Health,2026 Mannheim, Germany · Faculty of Mathematics and Computer Science, Heidelberg University, Germany · Faculty of Physics and Astronomy, Heidelberg University, Germany +1
Train Short, Act Long: Multistability and the Horizon Independence of Recurrent Policies
May 12, 2026Asad Bakija, Florent De Geeter, Julien Brandoit +2Dynamical Systems Reinforcement Learning
Dept. of Electrical Engineering and Computer Science University of Liège Liège, Belgium · University of Liège
Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons
May 12, 2026Aaron Spieler, Georg Martius, Anna LevinaRecurrent Neural Networks Neural Scaling Laws
University of Tübingen, Germany · Max Planck Institute for Biological Cybernetics, Tübingen, Germany · Max Planck Institute for Intelligent Systems, Tübingen, Germany
Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
May 12, 2026Arthur Fyon, Julien Brandoit, Loris Mendolia +3Energy-Efficient ML Keyword Spotting
University of Liège Liège, Belgium
Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
May 12, 2026Julien Brandoit, Arthur Fyon, Damien Ernst +1Energy-Efficient ML Recurrent Neural Networks
Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
A Random-Matrix Criterion for Initializing Gated Recurrent Neural Networks
May 11, 2026Tommaso Fioratti, Riccardo Marcaccioli, Francesco CasolaGated Recurrent Unit Neural Network Initialization
Capital Fund Management, 75007 Paris, France · Institute of Mathematics, EPFL, 1015 Lausanne, Switzerland
A Single-Layer Model Can Do Language Modeling
May 11, 2026Zanmin WangLanguage Model Pretraining Recurrent Language Models
Explainability of Recurrent Neural Networks for Enhancing P300-based Brain-Computer Interfaces
May 11, 2026Christian Oliva, Vinicio Changoluisa, Francisco B Rodríguez +1Brain-Computer Interfaces EEG Decoding
Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, c/ Francisco Tomas y Valiente, 11, Madrid, 28049, Madrid, Spain · Grupo de Investigación en Electrónica y Telemática, Universidad Politécnica Salesiana, Campus el Girón, Ave 12 de Octubre 24-22, Quito, 170143, Quito, Ecuador
Structured Recurrent Mixers for Massively Parallelized Sequence Generation
May 9, 2026Benjamin L. BadgerRecurrent Neural Networks Autoregressive Language Modeling
IBM
Transfer Learning for Dead Fuel Moisture Prediction Using Time-Warping Recurrent Neural Networks
May 8, 2026Jonathon Hirschi, Jan Mandel, Adam KochanskiRecurrent Neural Networks Transfer Learning
Department of Mathematical and Statistical Sciences, University of Colorado Denver, Colorado · Department of Meteorology and Climate Science, Wildfire Interdisciplinary Research Center, San Jose State University, San Jose, California
Convex Optimization of Spiking Neural Networks with Global and Approximation Guarantees
May 8, 2026Himanshu Udupi, Calvin Ang, Sungyoon Kim +2Neural Network Optimization Recurrent Neural Networks
University of Illinois Urbana-Champaign
Rethinking State Tracking in Recurrent Models Through Error Control Dynamics
May 8, 2026Jiwan Chung, Heechan Choi, Seon Joo KimRecurrent Neural Networks State Tracking
Yonsei University
MinMax Recurrent Neural Cascades
May 7, 2026Alessandro RoncaRecurrent Neural Networks Neural Network Training Dynamics
IRIS-AI
Understanding Cross-Language Transfer Improvements in Low-Resource HTR: The Role of Sequence Modeling
May 7, 2026Sana Al-azzawi, Chang Liu, Nudrat Habib +2Convolutional RNNs Recurrent Neural Networks
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering , Luleå, Sweden · Luleá University of Technology, Department of Computer Science, Electrical and Space Engineering , Luleå, Sweden
How Long Does Infinite Width Last? Signal Propagation in Long-Range Linear Recurrences
May 6, 2026Mariia SeleznovaNeural Network Initialization Recurrent Neural Networks
Ludwig-Maximilians-Universität München · Max Planck Institute of Quantum Optics
A Comparative Analysis of Machine Learning and Deep Learning Models for Tweet Sentiment Classification: A Case Study on the Sentiment140 Dataset
May 6, 2026Vita Anggraini, Cintya Bella, Bastian +3Social Media Analysis Sentiment Analysis
Faculty of Science · Faculty of Science[cs.CL]
Unifying Dynamical Systems and Graph Theory to Mechanistically Understand Computation in Neural Networks
May 5, 2026Jatin Sharma, Dan F. M Goodman, Danyal AkarcaRecurrent Neural Networks
Department of Electrical and Electronic Engineering Imperial College London
Learning reveals invisible structure in low-rank RNNs
May 5, 2026Yoav Ger, Omri BarakNeural Processes Recurrent Neural Networks
Ruth and Bruce Rappaport Faculty of Medicine and Network Biology Research Laboratory Technion – Israel Institute of Technology, Haifa, Israel
Symmetry, Defects, and Diffusion in Continuous-memory Recurrent Networks
May 5, 2026Hanson Hanxuan MoEquivariant Neural Networks Recurrent Neural Networks
Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
May 4, 2026Matthew Schlegel, Volodymyr Tkachuk, Adam White +1Reinforcement Learning Recurrent Neural Networks
University of Alberta

May 17, 2026J/K move · Enter open · S save

Von Economo neurons enable reliable social skill acquisition in recurrent spiking neural networks: a computational account with clinical predictions

Esila Keskin

School of Computing and Creative Technologies, University of the West of England, Bristol, UK

Neural Processes Recurrent Neural Networks Spiking Neural Networks Neural Network Training Dynamics

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Von Economo neurons (VENs) are selectively lost in behavioural-variant frontotemporal dementia (bvFTD) and reduced in autism spectrum conditions (ASC), yet their computational role in social learning remains unexplained. We train a spiking neural network (the VENCircuit) embedding VEN-like projection neurons (K=40, 2% of total) in a recurrent pyramidal circuit across 50 matched random initialisations with and without VENs. The network is trained on a controlled binary classification task; we make no claim to model social cognition directly. VEN-intact networks converged in 49/50 cases (98%) versus 35/50 (70%) for VEN-ablated networks (Fisher's exact OR=21.0, 95% CI 2.7-167, p=8.7e-5). Failed ablated networks showed complete absence of learning, inconsistent with a speed-of-learning account. Phase-ablation experiments show VEN removal is most disruptive during mid-training (epochs 5-25), when a co-adaptive dependency forms in the pyramidal circuit. We derive a formal account showing VENs provide a direct gradient pathway immune to Jacobian instabilities affecting the recurrent circuit. Inference-time VEN ablation caused a significant performance drop (Wilcoxon p=0.022), ranging from no change (16/20 networks) to catastrophic collapse (0.989 to 0.620). VENs function as acquisition scaffolds whose developmental absence produces stochastic learning failure - a computational analogue of variable social skill acquisition in ASC - with falsifiable predictions for organoid and electrophysiology studies.