The idea is that the human visual system and that of higher animals constructs
a hierarchy of representations that gradually disentangle and separate the surfaces of different objects [ZODC09] allowing their perceptual discriminability. The experimental study of biological vision - and in particular of object recognition - is the leitmotiv of this Thesis.
The work I present in this Thesis was realized in the Visual Neuroscience Lab, guided by Prof. Davide Zoccolan, during the period that goes from May 2015 to December of the same year. The aim of this Lab - as stated on the web page - is to
understand the neuronal mechanisms underlying visual object recognition, using a
combination of psychophysics, electrophysiology, and computational modeling. The animal model of choice is the rat.
The computational work embrace almost all the main tools of data science, like data extraction, manipulation, and most importantly data exploration and analysis. In order to keep the pace with the evolution of machine learning techniques and computational tools that are changing the way to do computational neuroscience we decided to import modern machine learning algorithms into the workflow of the Lab.
There are many task in visual research where high performance computing (HPC)
has become an essential tool. We identified many problems and subproblems in our scientific projects where HPC can play and will play a key role in the present and in the future of our Lab.
Thesis not available.