Understanding the processing of information in our cortex is a significant part of understanding how the brain works and of understanding intelligence itself, arguably one of the greatest problems in science today. In particular, our visual abilities are computationally amazing and we are still far from imitating them with computers. Thus, visual cortex may well be a good proxy for the rest of the cortex and indeed for intelligence itself. But despite enormous progress in the physiology and anatomy of the visual cortex, our understanding of the underlying computations remains fragmentary.
I will briefly review the anatomy and the physiology of primate visual cortex and then describe a class of quantitative models of the ventral stream for object recognition, which, heavily constrained by physiology and biophysics, have been developed during the last two decades and which have been recently shown to be quite successful in explaining several physiological data across different visual areas. I will discuss their performance and architecture from the point of view of state-of-the-art computer vision system. Surprisingly, such models also mimic the level of human performance in difficult rapid image categorization tasks in which human vision is forced to operate in a feedforward mode.
I will then focus on the key limitations of such hierarchical feedforward models for object recognition, discuss why they are incomplete models of vision and suggest possible alternatives focusing on the computational role of attention and its likely substrate – cortical backprojections. Finally, I will outline a program of research to attack the broad challenge of understanding in terms of brain circuits the process of image inference and in particular recognition tasks beyond simple scene classification.
Tomaso Poggio (MIT)
Tomaso A. Poggio, is the Eugene McDermott Professor in the Dept. of Brain & Cognitive Sciences at MIT and the director of the new NSF Center for Brains, Minds and Machines at MIT of which MIT and Harvard are the main member Institutions. He is a member of both the Computer Science and Artificial Intelligence Laboratory and of the McGovern Brain Institute. He is an honorary member of the Neuroscience Research Program, a member of the American Academy of Arts and Sciences, a Founding Fellow of AAAI and a founding member of the McGovern Institute for Brain Research. Among other honors he received the Laurea Honoris Causa from the University of Pavia for the Volta Bicentennial, the 2003 Gabor Award, the Okawa Prize 2009, the AAAS Fellowship and the 2014 Swartz Prize for Theoretical and Computational Neuroscience. He is one of the most cited computational scientists with contributions ranging from the biophysical and behavioral studies of the visual system to the computational analyses of vision and learning in humans and machines. With W. Reichardt he characterized quantitatively the visuo-motor control system in the fly. With D. Marr, he introduced the seminal idea of levels of analysis in computational neuroscience. He introduced regularization as a mathematical framework to approach the ill-posed problems of vision and the key problem of learning from data. In the last decade he has developed an influential hierarchical model of visual recognition in the visual cortex. The citation for the recent 2009 Okawa prize mentions his ââ¦outstanding contributions to the establishment of computational neuroscience, and pioneering researches ranging from the biophysical and behavioral studies of the visual system to the computational analysis of vision and learning in humans and machines.â His research has always been interdisciplinary, between brains and computers. It is now focused on the mathematics of learning theory, the applications of learning techniques to computer vision and especially on computational neuroscience of the visual cortex. A former Corporate Fellow of Thinking Machines Corporation and a former director of PHZ Capital Partners, Inc., he is a director of Mobileye and was involved in starting, or investing in, several other high tech companies including Arris Pharmaceutical, nFX, Imagen, Digital Persona and Deep Mind. Tomaso Poggio Eugene McDermott Professor Director NSF Science & Technology Center for Brains, Minds and Machines(CBMM) http://cbmm.mit.edu/ Core founding scientific advisor, MIT Quest for Intelligence McGovern Institute CSAIL (Computer Science and Artificial Intelligence Lab) Brain Sciences Department M.I.T., 46-5177B see http://whereis.mit.edu/?selection=46&Buildings=go 43 Vassar Street Cambridge, MA 02142 E-mail: firstname.lastname@example.org Phone: 617-253-5230 Fax: 617-253-2964 Web: http://cbcl.mit.edu/people/poggio/poggio-new.htm PoggioLab Web page: http://cbcl.mit.edu/
More from the Same Authors
2017 Poster: Do Deep Neural Networks Suffer from Crowding? »
Anna Volokitin · Gemma Roig · Tomaso Poggio
2015 Symposium: Brains, Minds and Machines »
Gabriel Kreiman · Tomaso Poggio · Maximilian Nickel
2015 Poster: Learning with Group Invariant Features: A Kernel Perspective. »
Youssef Mroueh · Stephen Voinea · Tomaso Poggio
2015 Poster: Learning with a Wasserstein Loss »
Charlie Frogner · Chiyuan Zhang · Hossein Mobahi · Mauricio Araya · Tomaso Poggio
2013 Poster: Neural representation of action sequences: how far can a simple snippet-matching model take us? »
Cheston Tan · Jedediah M Singer · Thomas Serre · David Sheinberg · Tomaso Poggio
2012 Poster: Learning Manifolds with K-Means and K-Flats »
Guillermo D Canas · Tomaso Poggio · Lorenzo Rosasco
2012 Poster: Multiclass Learning with Simplex Coding »
Youssef Mroueh · Tomaso Poggio · Lorenzo Rosasco · Jean-Jacques Slotine
2009 Poster: On Invariance in Hierarchical Models »
Jake Bouvrie · Lorenzo Rosasco · Tomaso Poggio
2008 Workshop: Cortical Microcircuits and their Computational Functions »
Tomaso Poggio · Terrence J Sejnowski