Meta-learning in natural and artificial intelligence

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512 · see citing works

Prototypical Networks for Few-shot LearningPreprint

Snell, J., Swersky, K., & Zemel, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05175 · Cited by 5,863 · see citing works

Statistical learning by 8-month-old infants

Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996) · Science · doi:10.1126/science.274.5294.1926 · Cited by 5,659 · see citing works

Matching Networks for One Shot LearningPreprint

Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04080 · Cited by 4,874 · see citing works

Human-level concept learning through probabilistic program induction

Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817 · see citing works

Optimization as a Model for Few-Shot Learning

Ravi, S., & Larochelle, H. (2017) · International Conference on Learning Representations · Cited by 2,766 · see citing works

Building machines that learn and think like people

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550 · see citing works

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533 · see citing works

Learning the value of information in an uncertain world

Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007) · Nature Neuroscience · doi:10.1038/nn1954 · Cited by 2,053 · see citing works

The formation of learning sets.

Harlow, H. F. (1949) · Psychological Review · doi:10.1037/h0062474 · Cited by 1,959 · see citing works

The Architecture of Cognitive Control in the Human Prefrontal Cortex

Koechlin, E., Ody, C., & Kouneiher, F. (2003) · Science · doi:10.1126/science.1088545 · Cited by 1,922 · see citing works

A New Factor in Evolution

Baldwin, J. M. (1896) · The American Naturalist · doi:10.1086/276408 · Cited by 1,781 · see citing works

Using goal-driven deep learning models to understand sensory cortex

Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621 · see citing works

Neuroscience-Inspired Artificial Intelligence

Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557 · see citing works

Meta-learning with memory-augmented neural networks

Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016) · International Conference on Machine Learning · Cited by 1,504 · see citing works

Learning to learn by gradient descent by gradient descent

Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., & de Freitas, N. (2016) · arXiv · doi:10.48550/arxiv.1606.04474 · Cited by 1,486 · see citing works

Origins of knowledge.

Spelke, E. S., Breinlinger, K., Macomber, J., & Jacobson, K. C. (1992) · Psychological Review · doi:10.1037/0033-295x.99.4.605 · Cited by 1,420 · see citing works

Learning to Learn

Thrun, S., & Pratt, L. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5529-2 · Cited by 1,291 · see citing works

Rule Learning by Seven-Month-Old Infants

Marcus, G., Vijayan, S., Rao, S. B., & Vishton, P. M. (1999) · Science · doi:10.1126/science.283.5398.77 · Cited by 1,246 · see citing works

Schemas and Memory Consolidation.

Tse, D., Langston, R. F., Kakeyama, M., Bethus, I., Spooner, P. A., Wood, E. R., Witter, M. P., & Morris, R. G. M. (2007) · Science · doi:10.1126/science.1135935 · Cited by 1,244 · see citing works

An information theoretical approach to prefrontal executive function

Koechlin, E., & Summerfield, C. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.04.005 · Cited by 1,183 · see citing works

Core knowledge

Spelke, E. S., & Kinzler, K. D. (2007) · Developmental Science · doi:10.1111/j.1467-7687.2007.00569.x · Cited by 1,089 · see citing works

Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia

O’Reilly, R. C., & Frank, M. J. (2005) · Neural Computation · doi:10.1162/089976606775093909 · Cited by 1,066 · see citing works

What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior

Behrens, T. E. J., Muller, T. H., Whittington, J. C. R., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.002 · Cited by 1,055 · see citing works

A deep learning framework for neuroscience

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0520-2 · Cited by 1,027 · see citing works

Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes

Badre, D. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.02.004 · Cited by 980 · see citing works

A Simple Neural Attentive Meta-LearnerPreprint

Mishra, N., Rohaninejad, M., Chen, X., & Abbeel, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.03141 · Cited by 961 · see citing works

Organizing conceptual knowledge in humans with a gridlike code

Constantinescu, A. O., O’Reilly, J. X., & Behrens, T. E. J. (2016) · Science · doi:10.1126/science.aaf0941 · Cited by 937 · see citing works

How schema and novelty augment memory formation

van Kesteren, M. T. R., Ruiter, D. J., Fernández, G., & Henson, R. N. (2012) · Trends in Neurosciences · doi:10.1016/j.tins.2012.02.001 · Cited by 860 · see citing works

Metalearning and neuromodulation

Doya, K. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00044-8 · Cited by 704 · see citing works

How Learning Can Guide Evolution.

Hinton, G. E., & Nowlan, S. J. (1987) · Cited by 682 · see citing works

Reinforcement learning, fast and slow

Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.006 · Cited by 667 · see citing works

Neural computations underlying arbitration between model-based and model-free learning

Lee, S. W., Shimojo, S., & O’Doherty, J. P. (2014) · Neuron · doi:10.1016/j.neuron.2013.11.028 · Cited by 642 · see citing works

Prefrontal cortex as a meta-reinforcement learning system

Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., Hassabis, D., & Botvinick, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0147-8 · Cited by 596 · see citing works

Learning to reinforcement learn

Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., & Botvinick, M. (2016) · arXiv · doi:10.48550/arxiv.1611.05763 · Cited by 579 · see citing works

A critique of pure learning and what artificial neural networks can learn from animal brains

Zador, A. M. (2019) · Nature Communications · doi:10.1038/s41467-019-11786-6 · Cited by 553 · see citing works

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy

Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.044 · Cited by 544 · see citing works

Learning to Learn: Introduction and Overview

Thrun, S., & Pratt, L. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_1 · Cited by 521 · see citing works

A solution to the learning dilemma for recurrent networks of spiking neurons

Bellec, G., Scherr, F., Subramoney, A., Hajek, E., Salaj, D., Legenstein, R. A., & Maass, W. (2020) · Nature Communications · doi:10.1038/s41467-020-17236-y · Cited by 514 · see citing works

Deep Neural Networks as Scientific Models

Cichy, R. M., & KAISER, D. H. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.01.009 · Cited by 453 · see citing works

Preschool children can learn to transfer: Learning to learn and learning from example

Brown, A. L., & Kane, M. J. (1988) · Cognitive Psychology · doi:10.1016/0010-0285(88)90014-x · Cited by 424 · see citing works

Cognitive control over learning: Creating, clustering, and generalizing task-set structure.

Collins, A. G. E., & Frank, M. J. (2013) · Psychological Review · doi:10.1037/a0030852 · Cited by 421 · see citing works

Foundations of human reasoning in the prefrontal cortex

Donoso, M., Collins, A. G. E., & Koechlin, É. (2014) · Science · doi:10.1126/science.1252254 · Cited by 405 · see citing works

Learning a synaptic learning rule

Bengio, Y., Bengio, S., & Cloutier, J. (1991) · IJCNN-91-Seattle International Joint Conference on Neural Networks · doi:10.1109/ijcnn.1991.155621 · Cited by 402 · see citing works

Prefrontal cortex and flexible cognitive control: Rules without symbols

Rougier, N. P., Noelle, D. C., Braver, T. S., Cohen, J. D., & O’Reilly, R. C. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0502455102 · Cited by 397 · see citing works

Meta-Gradient Reinforcement LearningPreprint

Xu, Z., Hasselt, H. V., & Silver, D. (2018) · Neural Information Processing Systems · doi:10.48550/arxiv.1805.09801 · Cited by 388 · see citing works

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.002 · Cited by 387 · see citing works

Doing Without Schema Hierarchies: A Recurrent Connectionist Approach to Normal and Impaired Routine Sequential Action.

Botvinick, M., & Plaut, D. C. (2004) · Psychological Review · doi:10.1037/0033-295x.111.2.395 · Cited by 362 · see citing works

Frontal cortex and the discovery of abstract action rules

Badre, D., Kayser, A. S., & D'Esposito, M. (2010) · Neuron · doi:10.1016/j.neuron.2010.03.025 · Cited by 337 · see citing works

Learning latent structure: carving nature at its joints

Gershman, S. J., & Niv, Y. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.02.008 · Cited by 326 · see citing works

Long short-term memory and learning-to-learn in networks of spiking neuronsPreprint

Bellec, G., Salaj, D., Subramoney, A., Legenstein, R. A., & Maass, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.09574 · Cited by 283 · see citing works

Recasting Gradient-Based Meta-Learning as Hierarchical BayesPreprint

Grant, E., Finn, C., Levine, S., Darrell, T., & Griffiths, T. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.08930 · Cited by 280 · see citing works

Deep reinforcement learning and its neuroscientific implications

Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.014 · Cited by 269 · see citing works

Meta-learning in Reinforcement Learning

Schweighofer, N., & Doya, K. (2003) · Neural Networks · doi:10.1016/s0893-6080(02)00228-9 · Cited by 261 · see citing works

Meta-Learning: A SurveyPreprint

Vanschoren, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03548 · Cited by 187 · see citing works

Optimal Behavioral Hierarchy

Solway, A., Diuk, C., Córdova, N. I., Yee, D. M., Barto, A. G., Niv, Y., & Botvinick, M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003779 · Cited by 145 · see citing works

Unsupervised Predictive Memory in a Goal-Directed AgentPreprint

Wayne, G., Hung, C.-C., Amos, D., Mirza, M., Ahuja, A., Grabska‐Barwińska, A., Rae, J. W., Mirowski, P., Leibo, J. Z., Santoro, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10760 · Cited by 137 · see citing works

Doing more with less: meta-reasoning and meta-learning in humans and machines

Griffiths, T. L., Callaway, F., Chang, M. B., Grant, E., Krueger, P. M., & Lieder, F. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2019.01.005 · Cited by 128 · see citing works

Toward an integration of deep learning and neuroscience

Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works

Simple Principles of Metalearning

Schmidhuber, J., Zhao, J., & Wiering, M. (1996) · doi:10.71910/supsi.7463 · Cited by 69 · see citing works

Been There, Done That: Meta-Learning with Episodic RecallPreprint

Ritter, S., Wang, J. X., Kurth-Nelson, Z. L., Jayakumar, S. M., Blundell, C., Pascanu, R., & Botvinick, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09692 · Cited by 60 · see citing works

8-Month-Old Infants Spontaneously Learn and Generalize Hierarchical Rules

Werchan, D. M., Collins, A. G. E., Frank, M. J., & Amso, D. (2015) · Psychological Science · doi:10.1177/0956797615571442 · Cited by 58 · see citing works

Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters

Khamassi, M., Enel, P., Dominey, P. F., & Procyk, E. (2013) · Progress in brain research · doi:10.1016/b978-0-444-62604-2.00022-8 · Cited by 54 · see citing works

Meta-learning of Sequential Strategies

Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019) · arXiv · doi:10.48550/arxiv.1905.03030 · Cited by 47 · see citing works

A Self-Tuning Actor-Critic AlgorithmPreprint

Zahavy, T., Xu, Z., Veeriah, V., Hessel, M., Oh, J., van Hasselt, H. P., Silver, D., & Singh, S. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12928 · Cited by 46 · see citing works

Learning to represent reward structure: A key to adapting to complex environments

Nakahara, H., & Hikosaka, O. (2012) · Neuroscience Research · doi:10.1016/j.neures.2012.09.007 · Cited by 25 · see citing works

Episodic Control as Meta-Reinforcement LearningPreprint

Ritter, S., Wang, J. S., Kurth-Nelson, Z. L., & Botvinick, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/360537 · Cited by 19 · see citing works

Meta-learning by the Baldwin effect

Fernando, C., Sygnowski, J., Osindero, S., Wang, J., Schaul, T., Teplyashin, D., Sprechmann, P., Pritzel, A., & Rusu, A. A. (2018) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3205651.3208249 · Cited by 18 · see citing works

Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network modelsPreprint

Dezfouli, A., Morris, R. W., Ramos, F., Dayan, P., & Balleine, B. W. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/328849 · Cited by 15 · see citing works

Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey

Acharya, D. B., Kuppan, K., & Divya, B. (2025) · IEEE Access · doi:10.1109/access.2025.3532853 · Cited by 717 · see citing works

Meta-learning Approaches for Few-Shot Learning: A Survey of Recent Advances

Gharoun, H., Momenifar, F., Chen, F., & Gandomi, A. H. (2024) · ACM Computing Surveys · doi:10.1145/3659943 · Cited by 235 · see citing works

A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition

Kim, K. K., Kim, M., Pyun, K. R., Kim, J., Min, J., Koh, S., Root, S. E., Kim, J., Nguyen, B. T., Nishio, Y., et al. (2022) · Nature Electronics · doi:10.1038/s41928-022-00888-7 · Cited by 178 · see citing works

Abstraction and analogy‐making in artificial intelligence

Mitchell, M. (2021) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14619 · Cited by 125 · see citing works

Bottom-Up and Top-Down Approaches for the Design of Neuromorphic Processing Systems: Tradeoffs and Synergies Between Natural and Artificial Intelligence

Frenkel, C., Bol, D., & Indiveri, G. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3273520 · Cited by 139 · see citing works

Organizing memories for generalization in complementary learning systems

Sun, W., Advani, M., Spruston, N., Saxe, A., & Fitzgerald, J. E. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01382-9 · Cited by 103 · see citing works

Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges

Khan, M. Q., Gaber, A., Schulz, P., & Fettweis, G. P. (2023) · IEEE Access · doi:10.1109/access.2023.3242582 · Cited by 98 · see citing works

A comprehensive survey on 6G and beyond: Enabling technologies, opportunities of machine learning and challenges

Jawad, A. T., Mâaloul, R., & Chaari, L. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110085 · Cited by 99 · see citing works

Learning from models beyond fine-tuning

Zheng, H., Shen, L., Tang, A., Luo, Y., Hu, H., Du, B., Wen, Y., & Tao, D. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00961-0 · Cited by 114 · see citing works

Deep Learning in Multimodal Fusion for Sustainable Plant Care: A Comprehensive Review

Yang, Z., Li, Y., Wang, R.-F., Hu, P., & Su, W. (2025) · Sustainability · doi:10.3390/su17125255 · Cited by 83 · see citing works

A multifaceted vision of the human-AI collaboration: a comprehensive review

Puerta-Beldarrain, M., Gómez-Carmona, O., Sánchez-Corcuera, R., Casado-Mansilla, D., López-de-Ipiña, D., & Chen, L. (2025) · IEEE Access · doi:10.1109/access.2025.3536095 · Cited by 73 · see citing works

Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, Its Applications, and Future Research Trajectories

Yang, N., Chen, S., Zhang, H., & Berry, R. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3405075 · Cited by 77 · see citing works

Multimodal Agent AI: A Survey of Recent Advances and Future Directions

Sun, Y., Sun, H., Ma, J., Zhang, P., & Huang, X. (2025) · Journal of Computer Science and Technology · doi:10.1007/s11390-025-4802-8 · Cited by 69 · see citing works

How much intelligence is there in artificial intelligence? A 2020 update

van der Maas, H. L. J., Snoek, L., & Stevenson, C. E. (2021) · Intelligence · doi:10.1016/j.intell.2021.101548 · Cited by 50 · see citing works

AutomaticAI – A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning

Czako, Z., Sebestyen, G., & Hângan, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115225 · Cited by 44 · see citing works

Challenges, evaluation and opportunities for open-world learning

Kejriwal, M., Kildebeck, E., Steininger, R. J., & Shrivastava, A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00852-4 · Cited by 43 · see citing works

Next-generation of recurrent neural network models for cognitionPreprint

Yang, G. R., & Molano‐Mazón, M. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.10.015 · Cited by 45 · see citing works

Meta-learned models of cognition

Binz, M., Dasgupta, I., Jagadish, A. K., Botvinick, M., Wang, J. X., & Schulz, E. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x23003266 · Cited by 38 · see citing works

Post-injury pain and behaviour: a control theory perspective

Seymour, B., Crook, R. J., & Chen, Z. S. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00699-5 · Cited by 41 · see citing works

Statistical Learning in Vision

Fiser, J., & Lengyel, G. (2022) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100720-103343 · Cited by 37 · see citing works

Modeling, Replicating, and Predicting Human Behavior: A Survey

Fuchs, A., Passarella, A., & Conti, M. (2023) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3580492 · Cited by 34 · see citing works

Rapid learning with phase-change memory-based in-memory computing through learning-to-learn

Ortner, T., Petschenig, H., Vasilopoulos, A., Renner, R., Brglez, Š., Limbacher, T., Piñero-Fuentes, E., Linares-Barranco, A., Pantazi, A., & Legenstein, R. A. (2025) · Nature Communications · doi:10.1038/s41467-025-56345-4 · Cited by 35 · see citing works

Meta-Health: Learning-to-Learn (Meta-learning) as a Next Generation of Deep Learning Exploring Healthcare Challenges and Solutions for Rare Disorders: A Systematic Analysis

Singh, K., & Malhotra, D. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09927-8 · Cited by 34 · see citing works

Learning environment-specific learning rates

Simoens, J., Verguts, T., & Braem, S. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011978 · Cited by 31 · see citing works

Recurrent networks endowed with structural priors explain suboptimal animal behavior

Molano‐Mazón, M., Shao, Y., Duque, D., Yang, G. R., Ostojic, S., & de la Rocha, J. (2023) · Current Biology · doi:10.1016/j.cub.2022.12.044 · Cited by 32 · see citing works

Interpretable SHAP Model Combining Meta-learning and Vision Transformer for Lithology Classification Using Limited and Unbalanced Drilling Data in Well Logging

Sun, Y., Pang, S., Zhao, Z., & Zhang, Y.-A. (2024) · Natural Resources Research · doi:10.1007/s11053-024-10396-4 · Cited by 28 · see citing works

Hybrid Model Integrating Fuzzy Systems and Convolutional Factorization Machine for Delivery Time Prediction in Intelligent Logistics

Zhu, D., Han, Z., Du, X. H., Zuo, D., Cai, L., & Xue, C. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3472043 · Cited by 27 · see citing works

Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance

Sugiyama, T., Schweighofer, N., & Izawa, J. (2023) · Nature Communications · doi:10.1038/s41467-023-39536-9 · Cited by 27 · see citing works

Based on purely physical information in deep learning optimizes soliton system parameter identification problem

Zhang, Z., Liu, M., Xing, X., Zhang, S., Yan, Z., & Liu, W. (2025) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2025.117852 · Cited by 25 · see citing works

Bottom-Up and Top-Down Neural Processing Systems Design: Neuromorphic Intelligence as the Convergence of Natural and Artificial IntelligencePreprint

Frenkel, C., Bol, D., & Indiveri, G. (2021) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-217499 · Cited by 24 · see citing works

Understanding the development of reward learning through the lens of meta-learning

Nussenbaum, K., & Hartley, C. A. (2024) · Nature Reviews Psychology · doi:10.1038/s44159-024-00304-1 · Cited by 28 · see citing works

A meta-learning method for few-shot bearing fault diagnosis under variable working conditions

Zeng, L., Jian, J., Chang, X., & Wang, S. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad28e7 · Cited by 22 · see citing works

Rediscovering Affordance: A Reinforcement Learning Perspective

Liao, Y.-C., Todi, K., Acharya, A., Keurulainen, A., Howes, A., & Oulasvirta, A. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501992 · Cited by 23 · see citing works

Confidence at the limits of human nested cognition

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Swin Transformer-Based Object Detection Model Using Explainable Meta-Learning Mining

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Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning

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Brain-inspired meta-reinforcement learning cognitive control in conflictual inhibition decision-making task for artificial agents

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Few-Shot SAR Target Recognition Through Meta-Adaptive Hyperparameters Learning for Fast Adaptation

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Modeling rapid language learning by distilling Bayesian priors into artificial neural networks

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Learning where to be flexible: Using environmental cues to regulate cognitive control.

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Enhancing Metacognitive and Creativity Skills through AI-Driven Meta-Learning Strategies

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Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study

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Deep Reinforcement Learning-Based Off-Road Path Planning via Low-Dimensional Simulation

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Specific connectivity optimizes learning in thalamocortical loops

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Attribute-guided prototype network for few-shot molecular property prediction

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Tracking the contribution of inductive bias to individualised internal models

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Algorithm Recommendation and Performance Prediction Using Meta-Learning

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Humans adaptively select different computational strategies in different learning environments.

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Overview of Generative AI Techniques for Cybersecurity

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Towards mental time travel: a hierarchical memory for reinforcement learning agentsPreprint

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How artificial intelligence cooperating with agent‐based modeling for urban studies: A systematic review

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Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning

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Experience-driven recalibration of learning from surprising events

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Continual Human-in-the-Loop Optimization

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VL-Meta: Vision-Language Models for Multimodal Meta-Learning

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A Scalable Meta Learning-Based Model to Secure IoT Networks

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Quantum Few-Shot Image Classification

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The Influence of “Artificial Intelligence + Human–Computer Interaction” on Teachers’ Psychological Changes in Academic Management in Colleges

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Scale Space Calibrates Present and Subsequent Spatial Learning in Barnes Maze in Mice

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Plant and Disease Recognition Based on PMF Pipeline Domain Adaptation Method: Using Bark Images as Meta-Dataset

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A Time Series Forecasting Approach Based on Meta-Learning for Petroleum Production under Few-Shot Samples

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A Survey of Human Intelligence Augmented Artificial Intelligence: An Autonomous Driving Perspective

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Multi-model machine learning for predicting tractor operator discomfort caused by whole-body vibration

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GCReID: Generalized continual person re-identification via meta learning and knowledge accumulation

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A contrastive rule for meta-learningPreprint

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IRAM–NET model: image residual agnostics meta-learning-based network for rare de novo glioblastoma diagnosis

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Meta-Learning-Assisted Untrained Neural Network for Electromagnetic Inverse Scattering Problems

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Compositional pretraining improves computational efficiency and matches animal behaviour on complex tasks

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Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study

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A Meta-Bayesian Approach for Rapid Online Parametric Optimization for Wrist-based Interactions

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Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores Using Graph Neural Networks and Meta-learning

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Discovering Adaptable Symbolic Algorithms from Scratch

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Ecologically pre-trained RNNs explain suboptimal animal decisionsPreprint

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Evolving Drug Design Methodology: from QSAR to AIDDPreprint

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Next-generation of recurrent neural network models for cognitionPreprint

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Automatic diagnosis of pneumothorax with M-mode ultrasound images based on D-MPL

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Goal-directed navigation in humans and deep reinforcement learning agents relies on an adaptive mix of vector-based and transition-based strategies

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Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

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Meta-Learning based efficient framework for diagnosing rare disorders: A comprehensive survey

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Adaptive curiosity about metacognitive ability.

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Learning how to reason and deciding when to decide

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When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education

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ICRL: independent causality representation learning for domain generalization

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Fault Diagnosis Method of Plunger Pump Based on Meta-Learning and Improved Multi-Channel Convolutional Neural Network Under Small Sample Condition

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Shared Minds: The Cognitive Parallels Between Humans and Artificial Intelligence

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Agentic AI systems: A systematic survey of multi-agent architectures, cognitive foundations, interaction, explainability, security, and performance evaluation

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Meta-transfer Learning for Massive MIMO Channel Estimation for Millimeter-Wave Outdoor Vehicular Environments

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Fundamentals of Evolutionary Machine Learning

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Regulation of reinforcement learning parameters captures long‐term changes in rat behaviour

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Dynamic Link Prediction for New Nodes in Temporal Graph Networks

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Artificial Intelligence for Understanding Human Behavior and Psychology

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Editorial: Bio A.I. - from embodied cognition to enactive robotics

Safron, A., Hipólito, I., & Clark, A. (2023) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2023.1301993 · Cited by 3 · see citing works

MODERN METHODS OF NEUROPHYSIOLOGY IN THE STUDY OF ARTIFICIAL INTELLIGENCE AND NEURAL INTERFACES

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Negative affect-driven impulsivity as hierarchical model-based overgeneralization

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Toward a science of prospective learning

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World models, artificial general intelligence and the hard problems of life–mind continuity: toward a unified understanding of natural and artificial intelligence

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Predicting CMA-ES Operators as Inductive Biases for Shape Optimization Problems

Friess, S., Peter Tiňo, B., Menzel, S., Sendhoff, B., & Yao, X. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9660001 · Cited by 2 · see citing works

The Interaction and Convergence of the Internet of Things and Artificial Intelligence

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Large-Scale Time Series Forecasting with Meta-Learning

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Meta-learning, social cognition and consciousness in brains and machines

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The reinforcement metalearner as a biologically plausible meta-learning framework

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Leveraging insights from neuroscience to build adaptive artificial intelligence

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Meta-learning and the evolution of cognition

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Meta-learning in active inference

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