# Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

arXiv: https://arxiv.org/abs/2210.08340 · HTML: https://mlsanity.com/paper/2210.08340/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 73 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [From Motor Control to Team Play in Simulated Humanoid Football](https://mlsanity.com/paper/2105.12196/) (2021)
- [Large Language Models and the Reverse Turing Test](https://mlsanity.com/paper/2207.14382/) (2022)
- [Deep neuroethology of a virtual rodent](https://mlsanity.com/paper/1911.09451/) (2019)
- [Neuroscience-inspired online unsupervised learning algorithms](https://mlsanity.com/paper/1908.01867/) (2019)

## Built on by (most cited first, 30 of 78)

- [Limitations of Large Language Models in Clinical Problem-Solving Arising from Inflexible Reasoning](https://mlsanity.com/paper/2502.04381/) (2025)
- [Human-like object concept representations emerge naturally in multimodal large language models](https://mlsanity.com/paper/2407.01067/) (2024)
- [Configurable Foundation Models: Building LLMs from a Modular Perspective](https://mlsanity.com/paper/2409.02877/) (2024)
- [The feasibility of artificial consciousness through the lens of neuroscience](https://mlsanity.com/paper/2306.00915/) (2023)
- [Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends](https://mlsanity.com/paper/2506.20966/) (2025)
- [Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes](https://mlsanity.com/paper/2305.11772/) (2023)
- [Brain-Model Evaluations Need the NeuroAI Turing Test](https://mlsanity.com/paper/2502.16238/) (2025)
- [The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence](https://mlsanity.com/paper/2307.07522/) (2023)
- [Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games](https://mlsanity.com/paper/2303.02160/) (2023)
- [Is artificial consciousness achievable? Lessons from the human brain](https://mlsanity.com/paper/2405.04540/) (2024)
- [Poisson Variational Autoencoder](https://mlsanity.com/paper/2405.14473/) (2024)
- [Brain2Music: Reconstructing Music from Human Brain Activity](https://mlsanity.com/paper/2307.11078/) (2023)
- [From Screens to Scenes: A Survey of Embodied AI in Healthcare](https://mlsanity.com/paper/2501.07468/) (2025)
- [Better artificial intelligence does not mean better models of biology](https://mlsanity.com/paper/2504.16940/) (2025)
- [Genes in Intelligent Agents](https://mlsanity.com/paper/2306.10225/) (2023)
- [Identifying Interpretable Visual Features in Artificial and Biological Neural Systems](https://mlsanity.com/paper/2310.11431/) (2023)
- [Fast weight programming and linear transformers: from machine learning to neurobiology](https://mlsanity.com/paper/2508.08435/) (2025)
- [Predictive Coding Networks and Inference Learning: Tutorial and Survey](https://mlsanity.com/paper/2407.04117/) (2024)
- [From reactive to cognitive: brain-inspired spatial intelligence for embodied agents](https://mlsanity.com/paper/2508.17198/) (2025)
- [Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks](https://mlsanity.com/paper/2301.09245/) (2023)
- [Leveraging dendritic properties to advance machine learning and neuro-inspired computing](https://mlsanity.com/paper/2306.08007/) (2023)
- [Does Conceptual Representation Require Embodiment? Insights From Large Language Models](https://mlsanity.com/paper/2305.19103/) (2023)
- [Active Sensing with Predictive Coding and Uncertainty Minimization](https://mlsanity.com/paper/2307.00668/) (2023)
- [Medical Foundation Models are Susceptible to Targeted Misinformation Attacks](https://mlsanity.com/paper/2309.17007/) (2023)
- [Integration of cognitive tasks into artificial general intelligence test for large models](https://mlsanity.com/paper/2402.02547/) (2024)
- [Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction](https://mlsanity.com/paper/2503.05231/) (2025)
- [Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture](https://mlsanity.com/paper/2305.11275/) (2023)
- [Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robots](https://mlsanity.com/paper/2603.06181/) (2026)
- [No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks](https://mlsanity.com/paper/2405.02369/) (2024)
- [Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence](https://mlsanity.com/paper/2411.15243/) (2024)

## 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.