Classes
Computational Models and Neurophysiological Mechanisms
Visual object recognition is essential for most everyday tasks including navigation, reading and socialization. Visual pattern recognition is also of high importance for many engineering applications such as automatic analysis of clinical images, face or landmark 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. This course will examine how circuits of neurons in visual cortex process, represent and recall information. The course will cover the following topics: architecture of visual cortex, lesion studies, physiological experiments in humans and animals, computational models of visual object recognition and subjective perception.
Location: Biolabs 1058
Note: Wed 08/31Biolabs 1075
Class Schedule:
First meeting: Wed 08/31
Monday 09/05, 09/12, 09/19, 09/26, 10/03, 10/10, 10/17, 10/24, 10/31, 11/07
Wed 11/16
Monday 11/21, 11/28, 12/05, 12/12
Topics:
(Coming soon: Click
on link for lecture notes and reading assignments)
Introduction to pattern recognition.
Why is vision difficult? Overview of key questions in the field.
Visual input. Natural image statistics. The retina, LGN and
primary visual cortex.
Lesion studies in humans and animals.
Adventures into terra incognita:
Physiology beyond primary visual cortex.
Electrical stimulation in visual cortex and causality.
Computational models of visual object recognition.
Computer vision and aritifical intelligence.
Towards understanding subjective visual perception, consciousness and building intelligent machines.
http://klab.tch.harvard.edu/academia/classes/hms_neuro300_vision/index.html
http://tinyurl.com/vision-class
Suggested Books
Ullman S (1996) High-level vision. MIT Press.
Wandell BA (1995) Foundations of vision. Sunderland Sinauer Associates.
Chalupa LM and Werner JS (editors) (2003). The Visual Neurosciences. MIT Press.
Other good books
Purves and Lotto. (2003). Why we see what we do. Sinauer Books.
Ripley. Pattern recognition and neural networks (1996). Cambridge University Press.
Rao, Olshausen and Lewicki (eds) (2002). Probabilistic models of the brain. MIT Press.
Koch C (2005) The quest for consciousness. Roberts & Company Publishers.
Regan (2000) Human perception of objects. Sinauer Books.
Dayan and Abbott (2002). Theoretical Neuroscience. MIT Press.
Contact information:
Gabriel Kreiman, 617-919-2530
gabriel.kreiman@tch.harvard.edu
KLAB
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