Paul Masset

Research Areas:

Behavioural Neuroscience

Research Summary:

My research group integrates ideas and methods from systems neuroscience, experimental psychology and machine learning to understand how the heterogeneity of neuron types endows brain circuits with efficient distributed computations underlying behavior and cognition. We develop and experimentally validate new models of distributed computation in brain circuits including a new understanding of how dopamine-based distributed reinforcement learning could be implemented in the brain. To answer these questions, we perform large scale electrophysiological recordings in rodents performing complex behavioral tasks, build new data analysis tools and develop new classes of biologically plausible neural networks.

Selected References:

Masset, P., Tano, P., Kim, H. R., Malik, A. N., Pouget, A., & Uchida, N. (2025). Multi-timescale reinforcement learning in the brain.ÌýNature,Ìý642(8068), 682–690.

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.ÌýNeuron,Ìý113(8), 1151-1168.e13.Ìý

Masset, P., & Gershman, S. J. (2025). Reinforcement learning with dopamine: A convergence of natural and artificial intelligence.ÌýThe Handbook of Dopamine. Elsevier.Ìý

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.

Masset, P., Qin, S., & Zavatone-Veth, J. A. (2022). Drifting neuronal representations: Bug or feature?.ÌýBiological cybernetics,Ìý116(3), 253-266.

Masset, P., Ott, T., Lak, A., Hirokawa, J., & Kepecs, A. (2020). Behavior-and modality-general representation of confidence in orbitofrontal cortex.ÌýCell,Ìý182(1), 112-126.