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Vision Science. NRS 495 – Neuroscience Seminar Christopher DiMattina , PhD. The problem of vision. What is this?. A familiar object. Kersten & Yuille 2003. An array of numbers. Palmer 1999.
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Vision Science NRS 495 – Neuroscience Seminar Christopher DiMattina, PhD
The problem of vision NRS 495 - Grinnell College - Fall 2012
What is this? NRS 495 - Grinnell College - Fall 2012
A familiar object Kersten & Yuille 2003 NRS 495 - Grinnell College - Fall 2012
An array of numbers Palmer 1999 The only information your visual system has to represent the world is an array of photoreceptor activities (left) NRS 495 - Grinnell College - Fall 2012
The problem of vision • Transform a two-dimensional array of pixel intensities I(x,y) into an accurate three-dimensional model of the world • For moving images, I(x,y,t) into 4-D space-time model NRS 495 - Grinnell College - Fall 2012
Visual system NRS 495 - Grinnell College - Fall 2012
The visual system • The problem of vision is solved by the human visual system • Eye, retina, and numerous brain regions dedicated to vision Hubel 1995 NRS 495 - Grinnell College - Fall 2012
Extensive neural computation • Brain contains about 1011 neurons, 1014 synapses • Visual system takes up about 50% of cortex in monkey Felleman & Van Essen NRS 495 - Grinnell College - Fall 2012
Hierarchical processing • Dozens of cortical areas arranged in a complex hierarchy Felleman & Van Essen NRS 495 - Grinnell College - Fall 2012
Functional specialization NRS 495 - Grinnell College - Fall 2012
Complex cortical circuitry • Each cortical region contains complicated neural circuitry NRS 495 - Grinnell College - Fall 2012
Visual system • Visual processing in the brain is complicated • Brain is the only machine that fully solves the problem of vision • Will discuss the visual system from retina to high-level cortex NRS 495 - Grinnell College - Fall 2012
Theories of vision NRS 495 - Grinnell College - Fall 2012
Quote “A wing would be a most mystifying structure if one did not know that birds flew.” - Horace Barlow (1961) NRS 495 - Grinnell College - Fall 2012
Theory • To understand how the brain works, we need to know what problems it is trying to solve • Theory provides us with frameworks for conceptualizing the problems and goals of vision NRS 495 - Grinnell College - Fall 2012
Vision as inference • Perception is a process of inferring the most likely configuration of the environment from two-dimensional light patterns Herman von Helmholtz NRS 495 - Grinnell College - Fall 2012
Demonstration • Close your eyes and press on the left side of your left eye • You will see a spot of light in your right visual field • Your brain interprets activity of retinal neurons as the visual stimulus which would have caused such activity NRS 495 - Grinnell College - Fall 2012
Vision as inference • The two-dimensional retinal image does not uniquely specify the three-dimensional configuration of objects in the world • The brain must therefore infer the most likely 3-D configuration given the available 2-D information • Statistical regularities in the natural world help this process NRS 495 - Grinnell College - Fall 2012
An example Kersten & Yuille 2003 NRS 495 - Grinnell College - Fall 2012
An example Kersten & Yuille 2003 NRS 495 - Grinnell College - Fall 2012
Visual completion NRS 495 - Grinnell College - Fall 2012
Self-occlusion NRS 495 - Grinnell College - Fall 2012
Vision is an interpretive process • Active process of three-dimensional model building NRS 495 - Grinnell College - Fall 2012
Visual models are predictive NRS 495 - Grinnell College - Fall 2012
Quote “I skate to where the puck is going to be, not where it has been.” – Wayne Gretzky NRS 495 - Grinnell College - Fall 2012
Bayesian inference • Bayes rule provides a mathematical formalism for combining sense-data with prior knowledge of visual world • Quantitative framework for studying perceptual inference ) NRS 495 - Grinnell College - Fall 2012
Machine vision NRS 495 - Grinnell College - Fall 2012
Machine Vision • We can learn a lot about the problem of vision and how the brain might solve vision by programming computers to “see” • Rich two-way traffic of ideas between computational neuroscience and machine vision NRS 495 - Grinnell College - Fall 2012
Edge detection • Linear filters optimized for edge detection resemble center-surround receptive fields of neurons in the retina Marr 1982 NRS 495 - Grinnell College - Fall 2012
Object recognition • Hierarchical neural model of ventral visual stream yields state-of-the-art object recognition performance Riesenhuber & Poggio NRS 495 - Grinnell College - Fall 2012
Visual cognition NRS 495 - Grinnell College - Fall 2012
Vision and cognition • Vision does not simply represent objects • Objects are classified, remembered, and assigned meaning • Attention brings objects into visual awareness NRS 495 - Grinnell College - Fall 2012
Attention: Bottom-up • Low-level visual cues can cause some objects to “pop-out” and grab our attention (salience) • Effect depends on particular cues or combinations NRS 495 - Grinnell College - Fall 2012
Attention: Top down • Attention acts as an information processing bottleneck or a spotlight on incoming sensory information • We are not consciously aware of much of the information present in the visual scene (inattentional blindness) NRS 495 - Grinnell College - Fall 2012
What is changing? NRS 495 - Grinnell College - Fall 2012
Attention experiment DJ Simons NRS 495 - Grinnell College - Fall 2012
Consciousness NRS 495 - Grinnell College - Fall 2012
Consciousness • Why is the sensory information-processing the brain performs accompanied by subjective experience? • How come perception does not take place “in the dark”? NRS 495 - Grinnell College - Fall 2012
Qualia • Could a full understanding of neurobiology ever tell us what it is it like to be a bat ? • Could a color-blind neuroscientist understand what it is like to see red? NRS 495 - Grinnell College - Fall 2012
Neuroscience and consciousness • Strategy advocated by Crick and Koch is to look at the neural correlates of consciousness (NCC) • Identify neural activity which is necessary and sufficient for visual awareness NRS 495 - Grinnell College - Fall 2012
Example: Binocular Rivalry Sheinberg & Logothetis (1997) NRS 495 - Grinnell College - Fall 2012
Neural recordings in humans Quiroga et al (2005) NRS 495 - Grinnell College - Fall 2012
Neuroscience seminar NRS 495 - Grinnell College - Fall 2012
NRS 495: Neuroscience Seminar • This course will give you a solid foundation in vision science • Interdisciplinary course focusing on neuroscience • Seminar format with student presentation + discussions NRS 495 - Grinnell College - Fall 2012
Any Questions? NRS 495 - Grinnell College - Fall 2012
See you later! NRS 495 - Grinnell College - Fall 2012
