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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

Vision Science

NRS 495 – Neuroscience Seminar

Christopher DiMattina, PhD


The problem of vision
The problem of vision

NRS 495 - Grinnell College - Fall 2012


What is this
What is this?

NRS 495 - Grinnell College - Fall 2012


A familiar object
A familiar object

Kersten & Yuille 2003

NRS 495 - Grinnell College - Fall 2012


An array of numbers
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 vision1
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
Visual system

NRS 495 - Grinnell College - Fall 2012


The visual system
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
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
Hierarchical processing

  • Dozens of cortical areas arranged in a complex hierarchy

Felleman & Van Essen

NRS 495 - Grinnell College - Fall 2012


Functional specialization
Functional specialization

NRS 495 - Grinnell College - Fall 2012


Complex cortical circuitry
Complex cortical circuitry

  • Each cortical region contains complicated neural circuitry

NRS 495 - Grinnell College - Fall 2012


Visual system1
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
Theories of vision

NRS 495 - Grinnell College - Fall 2012


Quote
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
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
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
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 inference1
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
An example

Kersten & Yuille 2003

NRS 495 - Grinnell College - Fall 2012


An example1
An example

Kersten & Yuille 2003

NRS 495 - Grinnell College - Fall 2012


Visual completion
Visual completion

NRS 495 - Grinnell College - Fall 2012


Self occlusion
Self-occlusion

NRS 495 - Grinnell College - Fall 2012


Vision is an interpretive process
Vision is an interpretive process

  • Active process of three-dimensional model building

NRS 495 - Grinnell College - Fall 2012


Visual models are predictive
Visual models are predictive

NRS 495 - Grinnell College - Fall 2012


Quote1
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
Bayesian inference

  • Bayes rule provides a mathematical formalism for combining sense-data with prior knowledge of visual world

  • Quantitative framework for studying perceptual inference

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NRS 495 - Grinnell College - Fall 2012


Machine vision
Machine vision

NRS 495 - Grinnell College - Fall 2012


Machine vision1
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
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
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
Visual cognition

NRS 495 - Grinnell College - Fall 2012


Vision and cognition
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
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: 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
What is changing?

NRS 495 - Grinnell College - Fall 2012


Attention experiment
Attention experiment

DJ Simons

NRS 495 - Grinnell College - Fall 2012


Consciousness
Consciousness

NRS 495 - Grinnell College - Fall 2012


C onsciousness
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
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
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
Example: Binocular Rivalry

Sheinberg & Logothetis (1997)

NRS 495 - Grinnell College - Fall 2012


Neural recordings in humans
Neural recordings in humans

Quiroga et al (2005)

NRS 495 - Grinnell College - Fall 2012


Neuroscience seminar
Neuroscience seminar

NRS 495 - Grinnell College - Fall 2012


Nrs 495 neuroscience seminar
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
Any Questions?

NRS 495 - Grinnell College - Fall 2012


See you later
See you later!

NRS 495 - Grinnell College - Fall 2012


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