Alberto Testolin

Personal Page of Alberto Testolin

Alberto Testolin

testolin_fullsize

Assistant Professor

Department of General Psychology and Department of Information Engineering, University of Padova
via Venezia 8
35131 Padova (Italy)

tel : +39 049 827 6637
e-mail: alberto.testolin(at)unipd.it
office: room 16 (6th floor), Building Psico 1

 

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 statistical learning, visual perception, orthographic processing, visuospatial attention, integration of bottom-up and top-down processing, predictive coding, numerical cognition and mathematical learning.

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

I also enjoy working with physicists to better understand and characterize the underlying mechanisms that allow deep learning systems to be so effective. To this aim, we use methods from statistical mechanics and network theory to investigate the structural and functional properties of multi-layered neural networks.

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


Education
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)

  • Testolin A, Piccolini M, and Suweis, S (2019). Deep learning systems as complex networks. Journal of Complex Networks.
  • 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.
  • Testolin, A., Stoianov, I., Sperduti, A., and Zorzi, M. (2015). Learning orthographic structure with generative neural networks. Cognitive Science.