NeuroAI前沿导读

Logistics

Course Name： AI+心理认知专题: NeuroAI前沿导读

Location:

Time: 2027 Spring

Credits: 2

Language: Chinese

Description:

Datasets

We have offer several free available datasets for students to play with.

Syllabus

• Week 01: Intoduction, ligistics, and Projects

  • Symbolic versus. Connectionlist

  • David Marr, Vision, first chapter

  • Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ. Building machines that learn and think like people. Behavioral and Brain Sciences. 2017;40:e253. doi:10.1017/S0140525X16001837

• Week 02: Human and Machine Vision

  • Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, and Tomaso Poggio. 2007. Robust Object Recognition with Cortex-Like Mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29, 3 (March 2007), 411–426. https://doi.org/10.1109/TPAMI.2007.56 (DL时代前的类脑视觉)

  • Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the national academy of sciences, 111(23), 8619-8624. (神经网络和人脑客体识别的开端)

  • Güçlü, U., & van Gerven, M. A. (2015). Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. Journal of Neuroscience, 35(27), 10005-10014. (神经网络和人类视皮层对应关系)

  • Conwell, C., Prince, J.S., Kay, K.N. et al. A large-scale examination of inductive biases shaping high-level visual representation in brains and machines. Nat Commun 15, 9383 (2024). https://doi.org/10.1038/s41467-024-53147-y (暴力搜索所有可能inductive bias的visual encoding modeling)

• Week 03: Brain-inspired Machine Models

  • Muttenthaler, L., Greff, K., Born, F. et al. Aligning machine and human visual representations across abstraction levels. Nature 647, 349–355 (2025). https://doi.org/10.1038/s41586-025-09631-6

  • Wang, Y., Yue, Y., Yue, Y. et al. Emulating human-like adaptive vision for efficient and flexible machine visual perception. Nat Mach Intell 7, 1804–1822 (2025). https://doi.org/10.1038/s42256-025-01130-7

  • Lu, Z., Thorat, S., Cichy, R.M. et al. Adopting a human developmental visual diet yields robust and shape-based AI vision. Nat Mach Intell 8, 735–748 (2026).

  • ICML, Yang, Zhang, 2021

• Week 04: Language (1)

  • Goldstein A, Zada Z, Buchnik E, Schain M, Price AR, Aubrey B, Nastase S, Feder A, Emauel D, Cohen A, et al. 2022. Shared computational principles for language processing in humans and deep language models. Nature Neuroscience. 25.3:369–380.

  • Goldstein A, Wang H, Niekerken L, Schain M, Zada Z, Aubrey B, Sheffer T, Nastase S, Gazula H, Singh A, et al. 2025. A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations. Nature human behaviour. doi:10.1038/s41562-025-02105-9.

• Week 05: Language (2)

  • Zou, J., Poeppel, D. & Ding, N. Constituent-constrained word prediction during language comprehension. Nat Neurosci 29, 1498–1509 (2026). https://doi.org/10.1038/s41593-026-02272-6

  • Cai, J., Kfir, Y., Jamali, M. et al. Mapping the neuronal building blocks of human language with language models. Nature 656, 425–433 (2026). https://doi.org/10.1038/s41586-026-10691-5

  • Yan, X., Chavez, A. G., Franch, M., Katlowitz, K. A., Gautam, I., Kim, B., ... & Sheth, S. A. (2026). Shared neural geometries for bilingual semantic representations in human hippocampal neurons. Cell.

• Week 06: Language and Vision

  • Wang, A.Y., Kay, K., Naselaris, T. et al. Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset. Nat Mach Intell 5, 1415–1426 (2023). https://doi.org/10.1038/s42256-023-00753-y

  • Chen, H., Liu, B., Wang, S. et al. Combined evidence from artificial neural networks and human brain-lesion models reveals that language modulates vision in human perception. Nat Hum Behav 10, 615–631 (2026). https://doi.org/10.1038/s41562-025-02357-5

• Week 07: Cognitive Maps

  • Banino, A., Barry, C., Uria, B., Blundell, C., Lillicrap, T., Mirowski, P., ... & Kumaran, D. (2018). Vector-based navigation using grid-like representations in artificial agents. Nature, 557(7705), 429-433. (神经网络解释网格细胞)

  • Cueva, C. J., & Wei, X. X. (2018). Emergence of grid-like representations by training recurrent neural networks to perform spatial localization. ICLR.

• Week 08: Working memory

  • Masse, N.Y., Yang, G.R., Song, H.F. et al. Circuit mechanisms for the maintenance and manipulation of information in working memory. Nat Neurosci 22, 1159–1167 (2019). https://doi.org/10.1038/s41593-019-0414-3

  • Orhan, A.E., Ma, W.J. A diverse range of factors affect the nature of neural representations underlying short-term memory. Nat Neurosci 22, 275–283 (2019). https://doi.org/10.1038/s41593-018-0314-y

  • Gong, D., Wan, X., & Wang, D. (2024, March). Working memory capacity of ChatGPT: An empirical study. In Proceedings of the AAAI conference on artificial intelligence (Vol. 38, No. 9, pp. 10048-10056).

• Week 09: Midterm Presentation and Defense

• Week 10: Long-term memory

  • Hopfield Network, a primer.

  • Spens, E., Burgess, N. A generative model of memory construction and consolidation. Nat Hum Behav 8, 526–543 (2024). https://doi.org/10.1038/s41562-023-01799-z

  • van de Ven, G.M., Siegelmann, H.T. & Tolias, A.S. Brain-inspired replay for continual learning with artificial neural networks. Nat Commun 11, 4069 (2020). https://doi.org/10.1038/s41467-020-17866-2

  • Holton, E., Braun, L., Thompson, J.A. et al. Humans and neural networks show similar patterns of transfer and interference during continual learning. Nat Hum Behav 10, 111–125 (2026). https://doi.org/10.1038/s41562-025-02318-y

  • Li, M., Jensen, K.T., Zhang, Q. et al. A neural network model of free recall learns multiple memory strategies. Nat Mach Intell 8, 1238–1250 (2026).

• Week 12: Reinforcement Learning

  • Eckstein, M.K., Summerfield, C., Daw, N.D. et al. Hybrid neural–cognitive models reveal how memory shapes human reward learning. Nat Hum Behav 10, 972–987 (2026). https://doi.org/10.1038/s41562-025-02324-0

  • Ji-An, L., Benna, M.K. & Mattar, M.G. Discovering cognitive strategies with tiny recurrent neural networks. Nature 644, 993–1001 (2025). https://doi.org/10.1038/s41586-025-09142-4

  • Bakermans, J.J.W., Warren, J., Whittington, J.C.R. et al. Constructing future behavior in the hippocampal formation through composition and replay. Nat Neurosci 28, 1061–1072 (2025). https://doi.org/10.1038/s41593-025-01908-3

• Week 13: Compositional and Generalizable Learning

  • Brenden M. Lake et al. Human-level concept learning through probabilistic program induction.Science,350,1332-1338(2015).DOI:10.1126/science.aab3050

  • Yang, G.R., Joglekar, M.R., Song, H.F. et al. Task representations in neural networks trained to perform many cognitive tasks. Nat Neurosci 22, 297–306 (2019). https://doi.org/10.1038/s41593-018-0310-2

  • Lake, B.M., Baroni, M. Human-like systematic generalization through a meta-learning neural network. Nature 623, 115–121 (2023). https://doi.org/10.1038/s41586-023-06668-3

• Week 14: Generative models of Cognition

  1. Hedayati, S., O’Donnell, R.E. & Wyble, B. A model of working memory for latent representations. Nat Hum Behav 6, 709–719 (2022). https://doi.org/10.1038/s41562-021-01264-9

  2. Goetschalckx, L., Andonian, A., & Wagemans, J. (2021). Generative adversarial networks unlock new methods for cognitive science. Trends in Cognitive Sciences, 25(9), 788-801.

  3. Gershman, S. J. (2019). The generative adversarial brain. Frontiers in Artificial Intelligence, 2, 18.

  4. What does the free energy principle tell us about the brain? https://gershmanlab.com/pubs/free_energy.pdf

• Week 15: AI as an alternative to human subjects.

  • Strachan, J.W.A., Albergo, D., Borghini, G. et al. Testing theory of mind in large language models and humans. Nat Hum Behav 8, 1285–1295 (2024). https://doi.org/10.1038/s41562-024-01882-z

  • Lu, J.G., Song, L.L. & Zhang, L.D. Cultural tendencies in generative AI. Nat Hum Behav 9, 2360–2369 (2025). https://doi.org/10.1038/s41562-025-02242-1

  • Myra Cheng et al. Sycophantic AI decreases prosocial intentions and promotes dependence.Science**391**,eaec8352(2026).DOI:10.1126/science.aec8352

  • Akata, E., Schulz, L., Coda-Forno, J. et al. Playing repeated games with large language models. Nat Hum Behav 9, 1380–1390 (2025). https://doi.org/10.1038/s41562-025-02172-y

  • Guilbeault, D., Delecourt, S. & Desikan, B.S. Age and gender distortion in online media and large language models. Nature 646, 1129–1137 (2025). https://doi.org/10.1038/s41586-025-09581-z

• Week 16: Final Presentation and defense