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Statistical Learning Theory and Applications

MIT Class 9.520 / 6.860 (Fall 2021)

Class 19 and Class 20: Real neural networks and the ventral stream

By Thomas Serre, Gabriel Kreiman and Tomaso Poggio

Slides Nov 16, 2021

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Neural Networks and the Ventral Stream

Tue Nov 3, 2020 and Th Nov 5th, 2020. 11am-12:30pm.

9.520/6.860: STATISTICAL LEARNING THEORY AND APPLICATIONS

Thomas Serre, Gabriel Kreiman and Tomaso Poggio

Serre et al 2007
HMAX, a biologically-inspired model of computations along the ventral visual stream
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Congratulations Aurélie Cordier!

Aurelie Cordier
Aurelie Cordier, M. Sc.

Congratulations to Aurélie Cordier who successfully defended her M.Sc. thesis!

Aurélie Cordier. Recognition of minimal images in the human brain. Harvard Medical School (2020).

Click here for a link to her thesis work.

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Congratulations Will Xiao!

Will Xiao
Will Xiao

Will Xiao was awarded the Stuart H.Q. & Victoria Quan Fellowship

See also recent work from Will Xiao:

Yuan L, Xiao W, Kreiman G, Tay FEH, Feng, JL, Livingstone, M (2020). Adversarial images for the primate brain. arXiv. 2011.05623 PDF

Xiao W. and Kreiman G. (2020). XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLoS Computational Biology 16(6): e1007973. PDF 

Ponce C.R., Xiao W., Schade P.F., Hartmann T.S., Kreiman G., Livingstone M. (2019). Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell, 177:999-1009. PDF

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Neuro 130/230: Visual object recognition (Fall 2019)

Fall 2019. Neuro 130/230. Visual object recognition: Computational and biophysical mechanisms

Visual recognition is essential for most everyday tasks including navigation, reading and socialization. Visual pattern recognition is also important for many engineering applications such as automatic analysis of clinical images, face recognition by computers, security tasks and automatic navigation. In spite of the enormous increase in computational power over the last decade, humans still outperform the most sophisticated engineering algorithms in visual recognition tasks. In this course, we will examine how circuits of neurons in visual cortex represent and transform visual information. The course will cover the following topics: functional architecture of visual cortex, lesion studies, physiological experiments in humans and animals, visual consciousness, computational models of visual object recognition, computer vision algorithms.

Textbook for the class:

Kreiman Biological and Computer Vision
Biological and Computer Vision. Cambridge University Press 2021