At the core of my research interests there is a question: Will we ever be able to replicate (a part of) the brain? I do not have an answer, only more questions. How does the human vision work? Can current Artificial Intelligence models explain brain activity? Can we create an artificial network that mimics some brain functions? How can we advance brain imaging methods? Wandering across Cognitive Neuroscience and Artificial Intelligence (specifically Deep Learning), I address the goal of understanding human vision conducting behavioural, f/MRI, and computational modelling studies. For more insights on my research, please visit the research page.

Currently Lecturer @Glasgow University (UK). 


Muckli*, L., Petro*, L. S., Abbatecola, C., Adeel, A., Bergmann, J., Deperrois, N., Destexhe, A., Kriegeskorte, N., Levelt, C. N., Maass, W., Morgan, A. T., Papale, P., Pennartz. Cyriel M. A., Peters, B., Petrovici, M. A., Phillips, W. A., Roelfsema, P. R., Sachdev, R. N. S., Seignette, K., … Larkum*, M. E. (2023). The cortical microcircuitry of predictions and context – a multi-scale perspective (v_0.1). Zenodo.

Svanera, M. , Benini, S., Bontempi, D. and Muckli, L. (2021) CEREBRUM-7T: fast and fully volumetric brain segmentation of 7 Tesla MR volumesHuman Brain Mapping, (doi: 10.1002/hbm.25636)

Svanera, M., Morgan, A.T., Petro, L.S., Muckli, L. (2021). A self-supervised deep neural network for image completion resembles early visual cortex fMRI activity patterns for occluded scenes. Journal of Vision, 21, 5.

Bontempi, D., Benini, S.,  Signoroni, A., Svanera, M., Muckli, L. (2020). CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scannerMedical Image Analysis, 62, 101688.

Svanera, M., Morgan, A.T., Petro, L.S., Muckli, L. (2020). An unsupervised deep neural network for image completion resembles early visual cortex fMRI activity patterns for occluded scenes. bioRxiv. 

Svanera, M., Savardi, M., Benini, S., Signoroni, A., Raz, G., Hendler, T., Muckli, L., Goebel, R., & Valente, G. (2019). Transfer learning of deep neural network representations for fMRI decodingJournal of Neuroscience Methods, 328, 108319. 

Svanera M., S. Benini, G. Raz, T. Hendler, R. Goebel, and G. Valente. “Deep driven fMRI decoding of visual categories”. In: NIPS Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI, 2016). url: https: //

Benini, S., M. Svanera, N. Adami, R. Leonardi, and K.A. Bálint. “Shot Scale Distribution in Art Films”. In: Multimedia Tools and Applications, 2016. doi: 10.1007/s11042- 016-3339-9.

Gordiychuk, A., M. Svanera, S. Benini, and P. Poesio. “Size distribution of micro bubbles for a venturi type bubble generator: effect of different parameters on bubble mean size, statistics of the distribution”. In: Experimental Thermal and Fluid Science, 2016. doi: 10.1016/j.expthermflusci.2015.08.014.

Svanera M., U. Riaz Muhammad, R. Leonardi, and S. Benini. “Figaro, hair detection and segmentation in the wild”. In: Proceedings of IEEE International Conference on Image Processing (ICIP, 2016). doi: 10.1109/ICIP.2016.7532494.

Svanera M., S. Benini, N. Adami, R. Leonardi, and K.A. Bálint. “Over-the-Shoulder Shot Detection in Art Films”. In: 13th International Workshop on Content-Based Multimedia Indexing (CBMI, 2015). doi: 10.1109/CBMI.2015.7153627.


Michele.Svanera at

Room 713, Dept of Psychology, 62 Hillhead Street, Glasgow, G12 8QB