INF1-CG VMA Lecture 2: Neurons(for vision): photoreceptors,

ganglia, etc.Alyssa Alcorn

Henry S. Thompson30 September 2010

1. Machine representation andimplementation: Why is machine vision hard?Examples of machine vision:

• automated inspection systems in factories• face matching/face recognition• extracting shapes and planes from images• Identifying (exactly, or as to category) objects

Despite more than half a century of effort

• most machine vision applications are poor compared to their human equivalents• Raw image data is often very noisy

◦ or of poor quality (especially for 3D data)• There is often too much data

◦ so that computation becomes overly difficult or time-consuming

That's about implementation

But we're losing even at the representation/algorithm level as well

• Object/pattern recognition• figure-ground segmentation• salient (important) region detection• visually guiding robot/vehicle navigation or other actions

2. The Challenge of VisionSome questions which will be addressed in the following lectures on vision:

• How does light in the world get into your eye and become information/ representationin the brain?

• How do we extract useful information from this representation?• How are the needs of an organism reflected in its visual system?• Why could you starve your pet frog even if you put food on the ground all around it?• Our visual systems often get fooled. what can illusions tell us about visual processing?

Public domain• And last but not least, "Why is there a neuron in my brain that only responds to

pictures of Halle Berry?" (weird but allegedly true)

Source unknown

3. Visual processing roadmapVisual information starts as very small units

• Single cells in the retina detecting light• Light information gets passed through layers of the brain

◦ to become richer pieces of information◦ Say, the presence of a line◦ ...and even richer pieces (this is an edge of something)◦ ...all the way up to object recognition (this is a chair)

Not the whole truth. . .

• This course presents a brief and simplified version of neurons◦ and the early visual processing system

• in order to introduce our approach to cognitive modelling• We won't discuss color vision or depth perception at all

4. NeuronsNeurons are specialised cells

• connected with other neurons• which rapidly transmit information

Neurons have a specialised cell body

• with many bushy dendrites◦ which take incoming connections from other neurons

• and one axon (or nerve fiber)◦ which makes outgoing connections to other neurons

U. S. Government public domainMicrophotograph of neurons in hippocampus (Courtesy of Eric H. Chudler, original credit to theSlice of Life project.)

Axons and dendrites carry electrical signals called action potentials

• Which are brief spikes of voltage

We say a neuron fires

• when it raises an action potential on its axon

An axon-dentrite connection can be excitatory

• tending to make the target neuron fire too

or it can be inhibitory

• tending to make the target neuron not fire

5. Photoreceptors and visible lightA receptor is any neuron specialised to respond to energy from the environment (light,pressure, molecules)

The first layer of neurons in the retina of the eye are called photoreceptors

• Photoreceptors fire in response to detecting a required (threshold) amount of visiblelight

• Many wavelengths of light (UV light, X-rays, infrared) cannot be seen by humans◦ Other animals may see more, or less

• We see only a small part of the whole electromagnetic spectrumcolour bar positioned against wavelength bar (Courtesy of South Carolina Algal EcologyLaboratory)

Other retinal neurons do not respond directly to light

• But to excitatory and inhibitory messages from the photoreceptors

6. Convergence: Passing messages betweenlayersNeurons in the vision-processing areas of the brain (the visual cortex) are organized intolayers

• Every neuron in a higher layer receives messages from many neighboring neurons inthe layer below

◦ These higher neurons will fire if they receive sufficient excitation▪ without too much inhibition

• This phenomenon is known as convergence◦ Neural convergence is essentially a method of compressing and simplifying

visual information.

7. It's not all one way• Neurons not only connect directly to the next layer up

◦ but also connect laterally to neurons in the same layer• Neurons from later layers in some parts of the brain also have back projections that

pass information to lower layers◦ But we won't cover those in this course

Both upward and lateral connections can be inhibitory as well as excitatory

• You can think of neurons as summing their inputs, positive (excitatory) and negative(inhibitory)

• And firing if the result exceeds some threshhold

8. Receptive fieldsSome higher-layer neurons fire based on activity in their receptive fields

• That is, areas of the retina which contain multiple photoreceptors• All connected to the higher-layer neuron in question• Think of this as the area the neuron can ‘see’

Such a neuron responds to patterns of light, not just its presence/absence

• Different types of neurons respond to different patterns

Generally, the higher up in the processing stream

• the more complex the pattern

9. Receptive field exampleA ganglion is a higher-layer retinal neuron

• with a circular receptive field

From E. Bruce Goldstein, Sensation & Perception, Seventh Edition• The illustration here shows fields that respond to a spot of light

◦ surrounded by darkness (center surround cells).+ Photoreceptor with excitatory connection to ganglion- Photoreceptor with inhibitory connection to ganglion

10. Serendipitous science: Hubel and WieselEarly research had discovered the center-surround receptive fields of ganglion cells

• sensitive to spots of light

Hubel and Wiesel were interested in extending this type of research

• In the late 1950s

They focused on recording from individual neurons

• in the visual cortex of cats• This area is also known as the striate cortex or V1

Cats have receptive fields fairly similar to those in human visual processing

H&W found no response to any of the dot-like stimuli they tried

• but they did find cells fired as the edge of the slide was being inserted into theprojector

◦ In other words, a line detectorVideo of reconstruction of Hubel and Weisel discovering edge-detector neuron (Sourceunknown)

11. Hubel and Wiesel, cont'dThe true importance of this research was that it demonstrated that different neuron types couldbe viewed as steps in a processing hierarchy.

• Validating the idea that there are multiple steps between what is in the world and ourinternal representation

For example, some cortical cells responded to spots anywhere in a narrow rectangular region

• in a particular orientation

They also responded, more strongly, to a rectangular stimulus

• Covering the same target area• Or to a group of dots arranged at the same orientation• Ganglion cells were known to respond to spots of light.• So we have evidence for three layers now

From D. H. Hubel and T. N.Wiesel, The Journal of Physiology (1959) 148: "Receptive fields of single neurones in the cat’s striatecortex"

They also found fourth-layer cells with complex receptive fields

• For example, summing together multiple edge-detectors across a region of the retina

From D. H. Hubel and T. N.Wiesel, The Journal of Physiology (1959) 148: "Receptive fields of single neurones in the cat’s striatecortex"

Hubel and Wiesel's work on orientation selectivity (along with collaborator Roger Sperry) wonthe Nobel Prize for medicine/physiology in 1981

12. Columnar organization and tuning curvesNeuron which respond to lines and edges

• Will usually fire strongly for lines of a particular orientation• Will also fire less strongly for lines with a similar (but not ideal) orientation• Won't fire at all for stimuli with very dissimilar orientations.

This pattern of responding is known as a tuning curve

From E. Bruce Goldstein, Sensation & Perception, Seventh Edition• It also applies to many stimuli more complex than lines• As well as the simpler case of spot detection we saw already

Neurons directly on top of one another in the cortex (members of the same vertical column)

• tend to respond to stimuli with the same orientation

Adjacent columns have similar preferred orientations.

13. AdminNo change of venue for tomorrow

• We're still in AT 2.12• On the 2nd floor of Appleton Tower

Course texts (required, for details see this week's reading list):

• Sensation and Perception (Goldstein 2007 or 2010)• Vision (Marr, W.H. Freeman & Co, 1982. Also reprinted 2010)

Original papers (optional reading):

• “Receptive fields of single neurons in the cat’s striate cortex” (Hubel & Wiesel, 1959)

For more detailed information on neurons and neurobiology:

• Neurobiology (Shepherd, 1994.) There are probably more recent editions of this• Scholarpedia entry for “Neuron” at http://www.scholarpedia.org/article/Neuron