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

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

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

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 6,245

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

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 4,975

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

Optimization as a Model for Few-Shot Learning

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

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

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

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

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 249

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 185

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

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 160

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

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 107

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 105

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 139

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 88

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

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 83

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 73

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

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

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

Next-generation of recurrent neural network models for cognitionPreprint

Yang, G. R., & Molano‐Mazón, M. (2021) · PsyArXiv (OSF Preprints) · doi:10.1016/j.conb.2021.10.015 · Cited by 49

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

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 43

Statistical Learning in Vision

Fiser, J., & Lengyel, G. (2022) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100720-103343 · Cited by 43

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 41

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

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

Learning environment-specific learning rates

Simoens, J., Verguts, T., & Braem, S. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011978 · Cited by 38

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 39

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

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

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 30

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 27

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

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 35

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 23

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 28

Confidence at the limits of human nested cognition

Recht, S., Jovanović, L., Mamassian, P., & Balsdon, T. (2022) · Neuroscience of Consciousness · doi:10.1093/nc/niac014 · Cited by 20

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

Baek, J.-W., & Chung, K. (2023) · Applied Sciences · doi:10.3390/app13053213 · Cited by 20

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

Russin, J., Pavlick, E., & Frank, M. J. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2510270122 · Cited by 27

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

Robertazzi, F., Vissani, M., Schillaci, G., & Falotico, E. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.06.020 · Cited by 19

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

Zeng, Z., Sun, J., Wang, Y., Gu, D., Han, Z., & Hong, W. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3325988 · Cited by 19

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

McCoy, R. T., & Griffiths, T. L. (2025) · Nature Communications · doi:10.1038/s41467-025-59957-y · Cited by 50

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

Xu, S., Simoens, J., Verguts, T., & Braem, S. (2023) · Journal of Experimental Psychology General · doi:10.1037/xge0001488 · Cited by 23

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

Khotimah, K., Rusijono, & Mariono, A. (2024) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v18i05.47705 · Cited by 17

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

Xiao, N., Huang, X., Wu, Y., Li, B., Zang, W., Shinwari, K., Тузанкина, И. А., Черешнев, В. А., & Liu, G. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1523902 · Cited by 20

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

Wang, X., Shang, E., Dai, B., Miao, Q., & Nie, Y. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3347531 · Cited by 16

Specific connectivity optimizes learning in thalamocortical loops

Lakshminarasimhan, K. J., Xie, M., Cohen, J. D., Sauerbrei, B., Hantman, A. W., Litwin-Kumar, A., & Escola, G. S. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114059 · Cited by 18

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

Hou, L., Xiang, H., Zeng, X., Cao, D., Zeng, L., & Song, B. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae394 · Cited by 18

Tracking the contribution of inductive bias to individualised internal models

Török, B., Nagy, D., Kiss, M. M., Janacsek, K., Németh, D., & Orbán, G. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010182 · Cited by 17

Algorithm Recommendation and Performance Prediction Using Meta-Learning

Palumbo, G., Carneiro, D. R., Guimares, M., Alves, V., & Nováis, P. (2022) · International Journal of Neural Systems · doi:10.1142/s0129065723500119 · Cited by 14

Humans adaptively select different computational strategies in different learning environments.

Verbeke, P., & Verguts, T. (2024) · Psychological Review · doi:10.1037/rev0000474 · Cited by 18

Overview of Generative AI Techniques for Cybersecurity

Sindiramutty, S. R., Prabagaran, K. R. V., Akbar, R., Hussain, M., & Malik, N. A. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-5415-5.ch001 · Cited by 14

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

Lampinen, A. K., Chan, S. C. Y., Banino, A., & Hill, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.14039 · Cited by 19

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

Guo, Z., & Liu, X. (2024) · Transactions in GIS · doi:10.1111/tgis.13152 · Cited by 15

Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning

Stiso, J., Lynn, C. W., Kahn, A. E., Rangarajan, V., Szymula, K. P., Archer, R., Revell, A. Y., Stein, J. M., Litt, B., Davis, K. A., et al. (2022) · eNeuro · doi:10.1523/eneuro.0361-21.2022 · Cited by 13

Experience-driven recalibration of learning from surprising events

Bakst, L., & McGuire, J. T. (2022) · Cognition · doi:10.1016/j.cognition.2022.105343 · Cited by 16

Continual Human-in-the-Loop Optimization

Liao, Y.-C., Streli, P., Li, Z., Christoph, G., & Holz, C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713603 · Cited by 15

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

Ma, H., Fan, B., Ng, B. K., & Lam, C. (2024) · Mathematics · doi:10.3390/math12020286 · Cited by 11

A Scalable Meta Learning-Based Model to Secure IoT Networks

Said, E. H., Otoum, Y., & Nayak, A. R. (2023) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2200226 · Cited by 9

Quantum Few-Shot Image Classification

Huang, Z., Shi, J., & Li, X. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3476339 · Cited by 9

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

Guan, H., Chen, Q., Han, S. I., & Zhang, B. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.730345 · Cited by 9

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

Tachiki, Y., Suzuki, Y., Kurahashi, M., Oki, K., Mavuk, Ö., Nakagawa, T., Ishihara, S., Gyoten, Y., Yamamoto, A., & Imayoshi, I. (2023) · eNeuro · doi:10.1523/eneuro.0505-22.2023 · Cited by 10

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

Cui, Z., Li, K., Kang, C., Wu, Y., Li, T., & Li, M. (2023) · Plants · doi:10.3390/plants12183280 · Cited by 9

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

Xu, Z., & Yu, G. (2024) · Energies · doi:10.3390/en17081947 · Cited by 8

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

Li, S., Yuan, K., Tang, Z., Zhang, Y., Ji, P., Ge, Q., Du, S., Huang, Y., & Chen, H. (2025) · Automotive Innovation · doi:10.1007/s42154-025-00361-z · Cited by 10

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

Singh, A., Nawayseh, N. K., Doyon-Poulin, P., Milosavljevic, S., Rakheja, S., Kumar, Y., Dewangan, K. N., Trask, C., & Samuel, S. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.111375 · Cited by 8

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

Liu, Z., Feng, C., Yu, K., Hu, J., & Yang, J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106561 · Cited by 8

A contrastive rule for meta-learningPreprint

Zucchet, N., Schug, S., von Oswald, J., Zhao, D., & Sacramento, J. (2021) · Zurich Open Repository and Archive (University of Zurich) · doi:10.48550/arxiv.2104.01677 · Cited by 8

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

Singh, K., & Malhotra, D. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10347-3 · Cited by 7

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

Huang, Q., Li, C., Ye, X., Xu, K., & Song, R. (2025) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2025.3539938 · Cited by 7

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

Hocker, D. L., Constantinople, C. M., & Savin, C. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01029-3 · Cited by 9

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

Fanizzi, A., Fadda, F., Maddalo, M., Saponaro, S., Lorenzon, L., Ubaldi, L., Lambri, N., Giuliano, A., Loi, E., Signoriello, M., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0303217 · Cited by 7

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

Liao, Y.-C., Desai, R., Pierce, A. M., Taylor, K. E., Benko, H., Jonker, T. R., & Gupta, A. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642071 · Cited by 6

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

Jegham, I., & Rekik, I. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16919-9_19 · Cited by 6

Discovering Adaptable Symbolic Algorithms from Scratch

Kelly, S., Park, D., Song, X., McIntire, M., Nashikkar, P., Guha, R., Banzhaf, W., Deb, K., Boddeti, V. N., Tan, J., et al. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341979 · Cited by 10

Ecologically pre-trained RNNs explain suboptimal animal decisionsPreprint

Molano‐Mazón, M., Shao, Y., Duque, D., Yang, G. R., Ostojic, S., & de la Rocha, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.15.444287 · Cited by 6

Evolving Drug Design Methodology: from QSAR to AIDDPreprint

Xu, J. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-9fwmg · Cited by 6

Next-generation of recurrent neural network models for cognitionPreprint

Yang, G. R., & Molano‐Mazón, M. (2021) · doi:10.31234/osf.io/w34n2 · Cited by 5

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

Zhang, T., Yan, S., Wei, G., Yang, L., Yu, T., & Ma, Y. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02765-2 · Cited by 4

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

Lan, D. C. L., Hunt, L. T., & Summerfield, C. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003296 · Cited by 6

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

García‐Peñalvo, F. J., Vázquez‐Ingelmo, A., & Garc韆-Holgado, A. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.023897 · Cited by 4

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

Singh, K., & Malhotra, D. (2024) · AIP conference proceedings · doi:10.1063/5.0199881 · Cited by 4

Adaptive curiosity about metacognitive ability.

Recht, S., Li, C., Yang, Y., & Chiu, K. (2024) · Journal of Experimental Psychology General · doi:10.1037/xge0001690 · Cited by 5

Learning how to reason and deciding when to decide

Braem, S., Held, L. K., Shenhav, A., & Frömer, R. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x22003090 · Cited by 4

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

Wang, P., Viberg, O., Law, E. L.-C., & Greiff, S. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10202-y · Cited by 3

ICRL: independent causality representation learning for domain generalization

Xu, L., & Shao, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-96357-0 · Cited by 3

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

Yang, X., Ma, J., Hu, H., Huang, J., & Jing, L. (2025) · Sensors · doi:10.3390/s25154587 · Cited by 4

Shared Minds: The Cognitive Parallels Between Humans and Artificial Intelligence

Tremblay, S., Marois, A., Zare, M., & Lafond, D. (2026) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/9946143 · Cited by 5

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

Khapre, S., Mersha, M. A., Olalekan, Z., Shakil, H., Iskandar, S., Moustafa, A., & Kalita, J. K. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134049 · Cited by 6

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

Tolba, B., El-Malek, A. H. A., Abo-Zahhad, M. M., & Elsabrouty, M. M. (2023) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc51644.2023.10060092 · Cited by 3

Fundamentals of Evolutionary Machine Learning

Banzhaf, W., & Machado, P. (2023) · Genetic and evolutionary computation · doi:10.1007/978-981-99-3814-8_1 · Cited by 3

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

Cinotti, F., Coutureau, É., Khamassi, M., Marchand, A. R., & Girard, B. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16449 · Cited by 4

Dynamic Link Prediction for New Nodes in Temporal Graph Networks

Zhu, X., Wu, Y., Zhang, Q., Chen, Z., & He, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650904 · Cited by 3

Artificial Intelligence for Understanding Human Behavior and Psychology

Chhabra, G., Kumar, S., Gupta, S., & Nagpal, P. (2023) · Springer eBooks · doi:10.1007/978-981-99-3039-5_2 · Cited by 3

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 5

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

Kirillova, G. A., & Urunova, G. (2022) · Научное обозрение Биологические науки (Scientific Review Biological Sciences) · doi:10.17513/srbs.1257 · Cited by 2

Negative affect-driven impulsivity as hierarchical model-based overgeneralization

Okan, A., & Hallquist, M. N. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2025.01.002 · Cited by 4

Toward a science of prospective learning

Kording, K. P., Vogelstein, J. T., Chaudhari, P., & Verstynen, T. (2025) · Neuron · doi:10.1016/j.neuron.2025.11.010 · Cited by 2

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

Safron, A., Levin, M., Klimaj, V., Sheikhbahaee, Z., Sakthivadivel, D. A. R., Razi, A., Ha, D., Hay, N., Schmidt, K., Rish, I., et al. (2026) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2024.0533 · Cited by 2

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

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

Chhabra, G., Kumar, S., Gupta, S. K., & Nagpal, P. (2023) · Springer eBooks · doi:10.1007/978-981-99-3039-5_1 · Cited by 2

Large-Scale Time Series Forecasting with Meta-Learning

Ma, S., & Fildes, R. A. (2023) · Palgrave advances in the economics of innovation and technology · doi:10.1007/978-3-031-35879-1_9 · Cited by 2

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

Langdon, A., Botvinick, M., Nakahara, H., Tanaka, K., Matsumoto, M., & Kanai, R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.10.004 · Cited by 87

The reinforcement metalearner as a biologically plausible meta-learning framework

Vriens, T., Horan, M., Gottlieb, J. P., & Silvetti, M. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x24000219

Leveraging insights from neuroscience to build adaptive artificial intelligence

Mathis, M. W. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02169-w · Cited by 11

Meta-learning and the evolution of cognition

Veit, W., & Browning, H. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x24000177 · Cited by 1

Meta-learning in active inference

Penacchio, O., & Clemente, A. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x24000074 · Cited by 1