mlsanityData to 7 Oct 2026

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

arXiv 2210.08340 ↗First posted 15 Oct 2022cs.AI q-bio.NCcited by 78

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Abstract (arXiv)

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities, inherited from over 500 million years of evolution, that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). Builds on 13 of its 73 references that are in the map. First posted on arXiv 15 Oct 2022.

Builds on

13 of 73 references.

References · 13

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  4. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  5. 2022A Generalist Agentcited by 802
  6. 2021Carbon Emissions and Large Neural Network Trainingcited by 561
  7. 2017Adversarial Attacks on Neural Network Policiescited by 314
  8. 2018Markerless tracking of user-defined features with deep learningcited by 203
  9. 2021Embodied Intelligence via Learning and Evolutioncited by 154
  10. 2021From Motor Control to Team Play in Simulated Humanoid Footballcited by 87
  11. 2022Large Language Models and the Reverse Turing Testcited by 38
  12. 2019Deep neuroethology of a virtual rodentcited by 22
1 more
  1. 2019Neuroscience-inspired online unsupervised learning algorithmscited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 19; 2024: 15; 2025: 28; 2026: 16.

78 papers.

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  1. 2025Limitations of Large Language Models in Clinical Problem-Solving Arising from Inflexible Reasoningcited by 29
  2. 2024Human-like object concept representations emerge naturally in multimodal large language modelscited by 24
  3. 2024Configurable Foundation Models: Building LLMs from a Modular Perspectivecited by 22
  4. 2023The feasibility of artificial consciousness through the lens of neurosciencecited by 18
  5. 2025Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trendscited by 17
  6. 2023Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenescited by 13
  7. 2023The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligencecited by 12
  8. 2025Brain-Model Evaluations Need the NeuroAI Turing Testcited by 12
  9. 2024Is artificial consciousness achievable? Lessons from the human braincited by 11
  10. 2024Poisson Variational Autoencodercited by 11
  11. 2023Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Gamescited by 10
  12. 2023Brain2Music: Reconstructing Music from Human Brain Activitycited by 10
38 more
  1. 2025From Screens to Scenes: A Survey of Embodied AI in Healthcarecited by 10
  2. 2025Better artificial intelligence does not mean better models of biologycited by 10
  3. 2023Genes in Intelligent Agentscited by 9
  4. 2023Identifying Interpretable Visual Features in Artificial and Biological Neural Systemscited by 9
  5. 2024Predictive Coding Networks and Inference Learning: Tutorial and Surveycited by 7
  6. 2025From reactive to cognitive: brain-inspired spatial intelligence for embodied agentscited by 7
  7. 2023Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networkscited by 6
  8. 2023Leveraging dendritic properties to advance machine learning and neuro-inspired computingcited by 6
  9. 2025Fast weight programming and linear transformers: from machine learning to neurobiologycited by 6
  10. 2023Does Conceptual Representation Require Embodiment? Insights From Large Language Modelscited by 5
  11. 2023Active Sensing with Predictive Coding and Uncertainty Minimizationcited by 5
  12. 2023Medical Foundation Models are Susceptible to Targeted Misinformation Attackscited by 5
  13. 2024Integration of cognitive tasks into artificial general intelligence test for large modelscited by 5
  14. 2023Explaining V1 Properties with a Biologically Constrained Deep Learning Architecturecited by 4
  15. 2025Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interactioncited by 4
  16. 2024No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networkscited by 3
  17. 2024Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligencecited by 3
  18. 2025Intrinsic Goals for Autonomous Agents: Model-Based Exploration in Virtual Zebrafish Predicts Ethological Behavior and Whole-Brain Dynamicscited by 3
  19. 2026Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robotscited by 3
  20. 2023A newborn embodied Turing test for view-invariant object recognitioncited by 2
  21. 2024MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generationcited by 2
  22. 2024A Contrastive Symmetric Forward-Forward Algorithm (SFFA) for Continual Learning Taskscited by 2
  23. 2024Graph-Based Representation Learning of Neuronal Dynamics and Behaviorcited by 2
  24. 2025FinchGPT: a Transformer based language model for birdsong analysiscited by 2
  25. 2025MIMIC-MJX: Neuromechanical Emulation of Animal Behaviorcited by 2
  26. 2026FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learningcited by 2
  27. 2026Modularity is the Bedrock of Natural and Artificial Intelligencecited by 2
  28. 2023Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learningcited by 1
  29. 2023Attention, not scale, drives human-AI alignment in multimodal language predictioncited by 1
  30. 2023Toward a Deeper Understanding: RetNet Viewed through Convolutioncited by 1
  31. 2024Emerging NeoHebbian Dynamics in Forward-Forward Learning: Implications for Neuromorphic Computingcited by 1
  32. 2024On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarizationcited by 1
  33. 2025Shifting Attention to You: Personalized Brain-Inspired AI Modelscited by 1
  34. 2025Neuromorphic Intelligencecited by 1
  35. 2025Foundation of Intelligence: Review of Math Word Problems from Human Cognition Perspectivecited by 1
  36. 2026NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligencecited by 1
  37. 2023A method for the ethical analysis of brain-inspired AIcited by 0
  38. 2023Meta Neural Coordinationcited by 0

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