Neural Processes

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Neural Processes study how to predict a function from a small set of observed input-output pairs, by learning across many related tasks and returning a distribution of possible outputs. Conditional, attentive, latent-variable, convolutional, and transformer variants differ in how they aggregate context and represent uncertainty; extensions use spectral features or neural operators for periodic data and physical fields. These methods are useful for sparse or irregularly sampled settings such as spatial interpolation, time-series forecasting, and partial-observation PDE prediction. A central tension is balancing expressive context interactions and calibrated uncertainty with consistency, computational efficiency, and the limits imposed by finite representations and amortized inference.

Latest papers

56 papers2 papers in the last four weeks, down 60% on the four weeks before. 0.0% of all new papers.

Sensorimotor behaviour from an untrained brain-and-nerve-cord connectome
Oct 24, 2026John R. Minnick, Jesus Gonzalez-Ferrer, Jinghui Geng +4Neural Processes Embodied Navigation
Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA. · UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA. · Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA. +1
Observation Assimilation Transformer: Probabilistic Weather Analysis from Sparse Station Observations
Oct 20, 2026Christian Donner, Michele Cattaneo, Maxim Samarin +4Data Assimilation Probabilistic Forecasting
Swiss Data Science Center, Eidgenössische Technische Hochschule Zürich (ETHZ) · Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland
Purin: A Biology-inspired Mechanism for Artificial Neural Networks
Sep 25, 2026Zishu Liu, Chunbo Luo, Christos GrecosNeural Processes Convolutional Neural Networks
University of Exeter · University of Wisconsin - Parkside
Error- and Prediction-Driven Motor Learning in the Cortico-Cerebellar Loop
Sep 24, 2026Ana Carolina Filipe, Rui Ponte Costa, Cláudia SoaresRobotic Control Neural Processes
Department of Computer Science, NOVA School of Science and Technology, Caparica, Portugal · Centre for Neural Circuits and Behaviour, University of Oxford
NPBoost: Neural Processes with Gradient-Boosted Fixed Effects
Sep 23, 2026Andrea Nava, Ken Rölli, Armin Begic +1Meta-Learning Neural Processes
Seminar for Statistics, ETH Zurich, Switzerland · Lucerne University of Applied Sciences and Arts, Switzerland
A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits
Sep 8, 2026Xiangnan Zhang, Jingxin Liu, Ranqi Lu +6Neural Processes Temporal Credit Assignment
School of Future Technologies, Beijing Institute of Technology, Beijing, 100081, China. · Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology), Ministry of Education, Beijing, 100081, China. · School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China. +5
From anatomy to computation: axonal delay dispersion shifts neurons from event to sequence detection
Sep 3, 2026Cheng Bi, Jipeng SunNeural Processes Neural Encoding
Independent Researcher · Department of Computer Science, Princeton University
Stimulus-Evoked Network Dynamics in Human Cortical Organoids: From a Graph-Computational Framework to Repeated-Stimulation Depression
Jul 30, 2026Esmaeil S. Nadimi, Vinay C. Gogineni, Jan-Matthias Braun +3Neural Processes
Applied AI and Data Science Unit, Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark · Biomedical Mass Spectrometry and System Biology Unit, Department of Biochemistry and Molecular Biology, Faculty of Science, University of Southern Denmark
Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting
Jul 17, 2026Ramin Soleimani, Andrea Visentin, Dirk PeschNeural Processes Time Series Forecasting
School of Computer Science and Information Technology, University College Cork, Cork, Ireland
Human-AI Agent Interaction as a Neuroplastic Training Environment
Jul 14, 2026Eranga Bandara, Ross Gore, Asanga Gunaratna +17Neural Processes Human-AI Interaction
Old Dominion University, Norfolk, VA, USA · AI Motion Labs, Melbourne, Australia · Lithuanian University of Health Sciences +6
Neural Operator Processes for Probabilistic Operator Learning under Partial Observations
Jun 22, 2026Jose Miguel Lara-Rangel, Serge GuillasNeural Processes PDE Operator Learning
University College London London, United Kingdom
Machine Learning Methods for Studying Latent Neural Activity Dynamics
Jun 9, 2026Shufeng Kong, Fumei Deng, Xinyi Dong +7Dynamical Systems Latent Variable Models
School of Software Engineering, Sun Yat-sen University, Zhuhai, China · School of Computer Science and Artificial Intelligence, Foshan University, Foshan, China · Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA +2
Hyperbolic Neural Population Geometry Benefits Computation
Jun 8, 2026Dennis Wu, Yi-Chun Hung, Braden Yuille +2Neural Representation Geometry Neural Processes
Department of Computer Science, Northwestern University · Center for Foundation Models and Generative AI, Northwestern University · Integrated Science Program, Northwestern University +3
Revisiting Neural Processes via Fourier Transform and Volterra Series
May 31, 2026Peiman Mohseni, Nick Duffield, Raymond K. W. WongNeural Processes Frequency-Domain Feature Learning
Texas A&M University, College Station, Texas, USA.
Cortex and subcortex play distinct roles over learning when cortical memory is limited
May 30, 2026Matthew Farrell, Taro ToyoizumiComputational Cognitive Modeling Reinforcement Learning
Laboratory for Neural Computation and Adaptation RIKEN Center for Brain Science, Wako, Japan · Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning Rules
May 28, 2026Nils LeuteneggerNeural Representation Geometry Predictive Coding
Independent Researcher, Switzerland
Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images
May 27, 2026Joséphine Raugel, Maximilian Seitzer, Marc Szafraniec +6Backpropagation Visual Representation Learning
Meta AI · Ecole Normale Supérieure, PSL University
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
May 26, 2026Siddharth Chaini, Federica B. Bianco, Ashish MahabalIrregular Time-Series Modeling Neural Processes
Department of Physics and Astronomy, University of Delaware, Newark, DE 19716, USA · University of Delaware, Data Science Institute, Newark, DE 19716, USA · Joseph R. Biden, Jr. School of Public Policy and Administration, University of Delaware, DE 19716, USA +3
Metropolis-Scale Resilient and Trustworthy Traffic Flow Inference Using Multi-Source Data
May 24, 2026Qishen Zhou, Yifan Zhang, Michail A. Makridis +3Uncertainty Quantification Neural Processes
School of Transportation, Jilin University, Changchun, 130022, China · Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China · Department of Computer Science, City University of Hong Kong (Dongguan), Dongguan, 523808, China +2
Characterizing the Representational Capacity of Neural Processes
May 22, 2026Robin YoungNeural Processes Neural Network Expressivity
University of Cambridge
Learning dynamical systems with biochemically informed neural ordinary differential equations
May 22, 2026Luis L. Fonseca, Reinhard C. Laubenbacher, Lucas BöttcherDynamical Systems Neural Processes
Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA · Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, Frankfurt am Main, 60322, Germany
Sparse Autoencoders Map Brain-LLM Alignment onto Cortical Semantic Topography
May 21, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuNeural Processes LLM Interpretability
The University of Hong Kong · Hong Kong, China · Stellaris AI Limited
Cross-Species RSA Reveals Conserved Early Visual Alignment but Divergent Higher-Area Rankings Across Human fMRI and Macaque Electrophysiology
May 21, 2026Nils LeuteneggerRepresentational Similarity Analysis Visual Representation Learning
Independent Researcher, Switzerland
Temporal Coding as a Substrate for Sensorimotor Object Inference: A Spiking Reinterpretation of Thousand Brains Architecture
May 21, 2026Joy BoseSpiking Neural Networks Active Sensing
Independent Researcher, Bengaluru, India
Three Costs of Amortizing Gaussian Process Inference with Neural Processes
May 20, 2026Robin YoungLatent Variable Models Amortized Inference
University of Cambridge, Cambridge, UK
TIDE: Asymmetric Neural Circuits for Stabilized Temporal Inhibitory-Excitatory Dynamics
May 19, 2026Alexander Kyuroson, Denis Kleyko, Marcus LiwickiNeural Network Stability Dynamic Neural Networks
Luleå University of Technology, Luleå, Sweden · Örebro University & RISE Research Institutes of Sweden
Efficient coding along the visual hierarchy
May 18, 2026Ananya Passi, Brian S. Robinson, Michael F. BonnerUnsupervised Learning Neural Processes
Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA · Applied Physics Laboratory, Johns Hopkins University, Baltimore, MD, USA
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
Dual-axis attribution of zebrafish tectal microcircuits for energy-efficient and robust neurocomputing
May 13, 2026Ningping Li, Hao Zhang, Yi ZhouEfficient Neural Network Inference Neural Processes
University of Science and Technology of China, Hefei, China
Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale
May 13, 2026John R. Minnick, Jesus Gonzalez-Ferrer, Kamran Hussain +6Brain-Computer Interfaces Neural Processes
Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA · UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA · Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA +2

Oct 24, 2026J/K move · Enter open · S save

Sensorimotor behaviour from an untrained brain-and-nerve-cord connectome

John R. Minnick, Jesus Gonzalez-Ferrer, Jinghui Geng, Jason K. Eshraghian+3

Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA. · UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA. · Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.+1

Neural Processes Embodied Navigation Embodied Cognition

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Connectomes now map the neurons and synapses of an adult fly's brain and nerve cord, but how much of behaviour follows from the wiring is unknown. Here we turn one such connectome into a spiking network of 172,433 neurons with a published brain model's untuned parameters, and run it alone and inside a simulated body. Two fixed rules join them: odours and looming objects drive sensory neurons, and motor neurons move the legs. Alone, the network passes a signal to the motor neurons it is known to drive when the pathway supplies about 2% or more of their input. In the body, the fly navigates around obstacles to 95% of the odour sources. A rewiring that keeps each neuron's connection counts and signs reaches 18%, and the fly's leg commands replayed with the senses off reach almost none, so navigation needs the measured wiring and sensory feedback. Dopamine-gated weakening of the mushroom body's output synapses turns approach to an odour into avoidance, which requires the pairing and is odour-specific once one olfactory synapse class is scaled. An untrained connectome thus supports a behaviour and its learned reversal, and the pathways it cannot drive mark what the diagram leaves out.