An up to date list of publications can also be found on Google Scholar

Preprints:

• Masset, P., & Gershman, S. J. (2025). Reinforcement learning with dopamine: a convergence of natural and artificial intelligence. The Handbook of Dopamine. Elsevier. In Press , PDF

• Choudhary, S., Masset, P., & Ba, D. (2025). Implicit Generative Modeling by Kernel Similarity Matching. arXiv preprint arXiv:2503.00655. Link , PDF

• Pjanovic, V., Zavatone-Veth, J., Masset, P., Keemink, S., & Nardin, M. (2025). Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems. bioRxiv, 2025-02. Link , PDF

• Boero, L., Wu, H., Zak, J. D., Masset, P., Pashakhanloo, F., Jayakumar, S., ... & Murthy, V. N. (2025). Perception and neural representation of intermittent odor stimuli in mice. bioRxiv, 2025-02. Link , PDF

• Ott, T., Bosc, M., Sanders, J. I., Masset, P., & Kepecs, A. (2024). Bounded optimality of time investments in rats, mice, and humans. bioRxiv, 2024-12. Link , PDF

Papers, Reviews & Book Chapters:

• Masset, P., Tano, P., Kim, H. R., Malik, A. N., Pouget, A., & Uchida, N. (2025). Multi-timescale reinforcement learning in the brain. Nature, 642(8068), 682–690. Link , Free-access link

  • Research Briefing in Nature: How dopamine neurons devalue delayed rewards Link

• Tolooshams, B., Matias, S., Wu, H., Temereanca, S., Uchida, N., Murthy, V. N., Masset, P., & Ba, D. (2025). Interpretable deep learning for deconvolutional analysis of neural signals. Neuron, 113(8), 1151-1168.e13. Link

  • Research Highlight in Nature Neuroscience: Mejia, L. A. (2025). Opening the deep learning box. Nature Neuroscience, 28(4), 709–709. Link

  • Blog post on the Kempner Institute Deeper Learning Blog: Mechanistic Interpretability: A Challenge Common to Both Artificial and Biological Intelligence

  • Research Highlight in Nature Methods: Vogt, N. (2025). Unraveling the black box in neural signal analysis. Nature Methods, 22(5), 894-894. Link

• Choudhary, S., Masset, P., & Ba, D. (2024). Self Supervised Dictionary Learning Using Kernel Matching. In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE. Link

• Zavatone-Veth, J., Masset, P., Tong, W., Zak, J. D., Murthy, V., & Pehlevan, C. (2023). Neural circuits for fast Poisson compressed sensing in the olfactory bulb. Advances in Neural Information Processing Systems, 36, 64793-64828. Link , PDF

• Bordelon, B., Masset, P., Kuo, H., & Pehlevan, C. (2023). Loss dynamics of temporal difference reinforcement learning. Advances in Neural Information Processing Systems, 36, 14469-14496. Link , PDF

• Masset, P., Qin, S., & Zavatone-Veth, J. A. (2022). Drifting neuronal representations: Bug or feature?. Biological cybernetics, 116(3), 253-266. Link , PDF

• Masset, P., Zavatone-Veth, J., Connor, J. P., Murthy, V., & Pehlevan, C. (2022). Natural gradient enables fast sampling in spiking neural networks. Advances in neural information processing systems, 35, 22018-22034. Link , PDF

• Ott, T., Masset, P., Gouvêa, T. S., & Kepecs, A. (2022). Apparent sunk cost effect in rational agents. Science Advances, 8(6), eabi7004. Link , PDF

• Masset, P., Ott, T., Lak, A., Hirokawa, J., & Kepecs, A. (2020). Behavior-and modality-general representation of confidence in orbitofrontal cortex. Cell, 182(1), 112-126. Link, PDF

• Lak, A., Hueske, E., Hirokawa, J., Masset, P., Ott, T., Urai, A. E., ... & Kepecs, A. (2020). Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon. Elife, 9, e49834. Link, PDF

• Hirokawa, J., Vaughan, A., Masset, P., Ott, T., & Kepecs, A. (2019). Frontal cortex neuron types categorically encode single decision variables. Nature, 576(7787), 446-451. Link, PDF

• Ott, T., Masset, P., & Kepecs, A. (2018, January). The neurobiology of confidence: From beliefs to neurons. In Cold Spring Harbor symposia on quantitative biology (Vol. 83, pp. 9-16). Cold Spring Harbor Laboratory Press. Link , PDF

• Chalk, M., Masset, P., Deneve, S., & Gutkin, B. (2017). Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology, 13(6), e1005582. Link , PDF

• Masset, P., & Kepecs, A. (2013). Categorical Decisions. In D. Jaeger & R. Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 1–5). Springer. Link, PDF