I am a neuroscientist with a background in Mathematics, currently working at the Universitat Politècnica de Catalunya in Barcelona. My research focuses on using Recurrent Neural Networks to explore the computational principles underlying decision-making processes.

My latest work, in collaboration with Alex Garcia-Duran, Alex Hyafil and Jaime de la Rocha (Molano-Mazón et al. 2024, Nature Communications, preprint) explores what rat and human movements during decision-making reveal about brain computations.

​Previously, in collaboration with Yuxiu Shao, Robert Yang and Srdjan Ostojic (Molano-Mazón et al. 2023 Current Biology, preprint), we showed that by pre-training RNNs on ecologically relevant environments, it is possible to recover a suboptimal behavior observed in rats performing a two-alternative forced-choice task that presents serial correlations. I have also written an opinion piece together with Dr. Robert Yang (Yang and Molano-Mazón 2021 Current Opinion in Neurobiology, preprint).

https://manuelmolano.wixsite.com/home

Neuroscience
Behavior
Decision making
Recurrent Neural Networks
Evolution

Molano-Mazón, M., Garcia-Duran, A., Pastor-Ciurana, J., Hernández-Navarro, L., Bektic, L., Lombardo, D., ... & Hyafil, A. (2024). Rapid, systematic updating of movement by accumulated decision evidence. Nature communications, 15(1), 1-19.

Molano-Mazón, M., Shao, Y., Duque, D., Yang, G. R., Ostojic, S., & De La Rocha, J. (2023). Recurrent networks endowed with structural priors explain suboptimal animal behavior. Current Biology, 33(4), 622-638.

Molano-Mazon, M., Barbosa, J., Pastor-Ciurana, J., Fradera, M., Zhang, R. Y., Forest, J., ... & Yang, G. R. (2022). NeuroGym: An open resource for developing and sharing neuroscience tasks.

Yang, G. R., & Molano-Mazón, M. (2021). Towards the next generation of recurrent network models for cognitive neuroscience. Current opinion in neurobiology, 70, 182-192.