Davide Zoccolan

Biography

Davide Zoccolan obtained his Laurea (M.S. equiv) in Physics at the University of Torino (Italy) in 1997. He then joined the group of Prof. Vincent Torre at the International School for Advanced Studies (SISSA) of Trieste (Italy), where he studied sensory-motor integration, motor pattern generation, and decision-making in an invertebrate model system. After obtaining his PhD in Biophysics at SISSA in 2002, he was awarded a Long Term HFSP Postdoctoral Fellowship to work as a post-doctoral fellow in the research groups of Prof. James DiCarlo and Prof. Tomaso Poggio, at the McGovern Institute for Brain Research of the Massachusetts Institute of Technology (MIT), in Cambridge (USA). Here, he studied the neuronal mechanisms underlying visual object recognition, using a combination of computational modeling and single-unit neuronal recordings from primate inferotemporal cortex. Starting from 2006, in collaboration with Dr. David Cox, he developed an independent line of research within the DiCarlo’s lab, with the goal of establishing rodent models for the study of higher-level visual functions. In 2008, he pursued this research by joining the recently established lab of Dr. David Cox at the Rowland Institute at Harvard (Harvard University), in Cambridge (USA). In 2008, he has been awarded the Accademia Nazionale dei Lincei – Compagnia di San Paolo Grant to join SISSA Neurobiology and Cognitive Neuroscience Sectors and start, in 2009, a Visual Neuroscience Lab, where he studies the neuronal basis of visual object recognition using a combination of psychophysics and electrophysiology in rodents, and computational modeling. He is currently Associate Professor in Psychobiology and Physiological Psychology.

Research Interest

The visual system can effortlessly recognize hundreds of thousands of objects in spite of tremendous variation in their appearance, resulting, for instance, from changes in objects’ position and pose, variable lighting conditions, and presence of background objects in the visual field. To achieve such an invariant representation of the visual world is an extremely difficult computational problem that even the most advanced artificial vision systems are not fully able to solve. Our lab investigates the neuronal processing of visual object information, using a combination of psychophysics and multi-unit neuronal recordings in rodents, as well as simulations of computational models. Our hope is that this work will lead to a greater understanding of the neuronal substrates of visual perception and will assist in the development of machine vision systems and neuronal prostheses.