Deep learning models of perception and cognition by Marco Zorzi

Deep learning models of perception and cognition by Marco Zorzi (University of Padova, Italy)

Deep learning in stochastic recurrent neural networks with many layers of neurons (“deep networks”) is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex representations of the sensory data through unsupervised learning. Using examples from research in my laboratory, I will show that deep learning models represent a major step forward for connectionist modeling in psychology and cognitive neuroscience. I will also focus on a new model of letter perception, which shows that learning written symbols can recycle the visual primitives of natural images, thereby requiring only limited domain-specific tuning.