Alberto Testolin

Personal Page of Alberto Testolin

Alberto Testolin



Department of General Psychology,
University of Padova
via Venezia 12/2
35131 Padova (Italy)

tel : +39 049 827 6528
fax: +39 049 827 6600
e-mail: alberto.testolin(at)
office: room P13 (ground floor), Building Psico2


Research Interests

I am broadly interested in cognitive neuroscience, artificial intelligence and machine learning. In particular, I study how information is represented and processed in neural systems by means of computational simulations based on deep learning, recurrent neural networks and related types of probabilistic graphical models.

In the cognitive neuroscience field, my research spans the domains of visual perception, orthographic processing, sequence learning, statistical learning and language acquisition, visuospatial attention, integration of bottom-up and top-down processing, predictive coding, coordinate transformation, space coding and numerical cognition.

My research also covers the theoretical and technological aspects of machine learning. In particular, I study the application of deep learning methods to the optimization of telecommunication systems. I also study parallel computing architectures, advanced programming platforms for machine learning, and their application to problems in engineering optimization and multidimensional data analysis.

I am a member of the IEEE Task force on Deep Learning.

Ph.D., Cognitive Science, University of Padova (2015)
Laurea (M.Sc.), Computer Science (Artificial Intelligence), University of Padova (2011)

Representative publications (see full list and PDF files here)

  • Zorzi M, and Testolin A (2018). An emergentist perspective on the origin of number sense. Philosophical Transactions of the Royal Society B: Biological Sciences.
  • Testolin A, Stoianov I, and Zorzi M (2017). Letter perception emerges from unsupervised deep learning and recycling of natural image features. Nature Human Behaviour.
  • Testolin A, De Filippo De Grazia M, and Zorzi M (2017). The role of architectural and learning constraints in neural network models: A case study on visual space coding. Frontiers in Computational Neuroscience.
  • Testolin, A., and Zorzi, M. (2016). Probabilistic models and generative neural networks: towards a unified framework for modeling normal and impaired neurocognitive functions. Frontiers in Computational Neuroscience.
  • Zorzi, M, Zanella, A, Testolin, A , De Filippo De Grazia, M, and Zorzi, M. (2015). Cognition-based networks: a new perspective on network optimization using learning and distributed intelligence. IEEE Access.
  • Testolin, A., Stoianov, I., Sperduti, A., and Zorzi, M. (2015). Learning orthographic structure with generative neural networks. Cognitive Science.
  • Pasa, L., Testolin, A., and Sperduti, A. (2015). Neural networks for sequential data: a pre-training approach based on Hidden Markov Models. Neurocomputing.
  • Testolin, A., Stoianov, I., De Filippo De Grazia, M., & Zorzi, M. (2013). Deep unsupervised learning on a desktop PC : A primer for cognitive scientists. Frontiers in Psychology.