I am a senior researcher at the National Research Council of Italy (CNR), Institute of Cognitive Sciences and Technologies (ISTC), Padova.
My principle research interests include neurocomputational modeling and analysis of human cognition, including visual perception, sensorimotor control, spatial navigation, goal-directed behavior, planning, numerical cognition. Research methods include computational modeling and advanced analysis of behavioral and neurophysiological data, as well as innovative behavioral experimentation with the use of robotic agents and virtual reality. I also explore the domains of robot control and human-robot interaction.
Principle computational frameworks of interest include predictive coding and Active Inference.
Currently I am the coordinator of the CNR units of two research projects, PACE financed by the PRIN2017 program of the Italian Ministry of University and Research and "MAIA", financed by the European Commission under Horizon2020. Those two projects investigate the computational principles of sensorimotor transformations in the Superior Parietal Cortex. Project MAIA also aims to develop novel approaches for decoding motor plans, or intentions. In past research projects, as coordinator or participant, I had investigated visual perception in reading, numerical perception and cognition in general, neural-level analysis.
Research highlight:
- Priorelli, M., Pezzulo, G., & Stoianov, I. P. (2023). Deep kinematic inference affords efficient and scalable control of bodily movements. Proceedings of the National Academy of Sciences, 120(51), 1–9. doi: 10.1073/pnas.2309058120
Priorelli, M., Pezzulo, G., & Stoianov, I. P. (2023). Active Vision In Binocular Depth Estimation : A Top - Down Perspective. Biomimetics, 8(445), 1–17. doi: doi.org/10.3390/biomimetics8050445
- Priorelli, M., Maggiore, F., Maselli, A., Donnarumma, F., Maisto, D., Mannella, F., Stoianov, I. P., & Pezzulo, G. (2023). Modeling motor control in continuous-time Active Inference: a survey. IEEE Transactions on Cognitive and Developmental Systems, PP, 1–15. doi: 10.1109/TCDS.2023.3338491
- Priorelli M., and Stoianov, I.P. (2023). Flexible Intentions: An Active Inference Theory. Frontiers in Computational Neuroscience, 17:1128694, 1–27. doi: 10.3389/fncom.2023.1128694
- Stoianov, I., Maisto, D., & Pezzulo G. (2022). The hippocampal formation as a hierarchical generative model supporting generative replay and continual learning., Progress in Neurobiology (217) doi:10.1016/j.pneurobio.2022.102329
- Stoianov, I., & Zorzi, M. (2012). Emergence of a “visual number sense” in hierarchical generative models. Nature Neuroscience (15) 194–6. doi:10.1038/nn.2996
- Stoianov, I., Genovesio, A., & Pezzulo, G. (2016). Prefrontal goal-codes emerge as latent states in probabilistic value learning. Journal of Cognitive Neuroscience (28), 140–157. doi:10.1162/jocn_a_00886
- Testolin, A., Stoianov, I., & Zorzi, M. (2017). Letter perception emerges from unsupervised deep learning and recycling of natural image features. Nature Human Behaviour, 1, 657–664. doi:10.1038/s41562-017-0186-2
- Stoianov, I., Pennartz, C., Lansink, C., & Pezzulo, G. (2018). Model-based spatial navigation in the hippocampus-ventral striatum circuit: a computational analysis. Plos Computational Biology, 1–28.
- Jezzini, A., Caruana, F., Stoianov, I., Gallese, V., & Rizzolatti, G. (2012). Functional organization of the insula and inner perisylvian regions. Proceedings of the National Academy of Sciences, 109, 10077–10082. doi:10.1073/pnas.1200143109
