Building deep neural network models to understand biological vision
Description
Recent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. This brain-inspired technology provides the basis for tomorrow’s computational neuroscience [1]. Deep convolutional neural nets trained for visual object recognition have internal representational spaces remarkably similar to those of the human and monkey ventral visual pathway [2]. Functional imaging and invasive neuronal recording provide increasingly rich measurements of brain activity in humans and animals, but a challenge is to leverage such data to gain insight into the brain’s computational mechanisms.
Speaker Bio
Nikolaus Kriegeskorte is a computational neuroscientist who studies how our brains enable us to see and understand the world around us. He received his PhD in Cognitive Neuroscience from Maastricht University, held postdoctoral positions at the Center for Magnetic Resonance Research at the University of Minnesota and the U.S. National Institute of Mental Health in Bethesda, and was a Programme Leader at the U.K. Medical Research Council Cognition and Brain Sciences Unit at the University of Cambridge. Kriegeskorte is a Professor at Columbia University, affiliated with the Departments of Psychology and Neuroscience. He is a Principal Investigator and Director of Cognitive Imaging at the Zuckerman Mind Brain Behavior Institute at Columbia University. Kriegeskorte is a co-founder of the conference “Cognitive Computational Neuroscience”, which had its inaugural meeting in September 2017 at Columbia University.