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Cognitive Systems: Human Cognitive Models in System Design. Retinal Oscillations that Encode Large Contiguous Features: Implications for how the Nervous System Processes Visual Information Garrett Kenyon Los Alamos National Laboratory. Need for Dynamic Binding . A Model Retina.

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slide1
Cognitive Systems: Human Cognitive

Models in System Design

Retinal Oscillations that Encode Large Contiguous Features:

Implications for how the Nervous System Processes Visual Information

Garrett Kenyon

Los Alamos National Laboratory

a model retina
A Model Retina

Kolb, Fernandez & Nelson

slide4
1

0

sec

Rate Code (Retina)

light

intensity

Dacey & Lee, Nature, 1994 (monkey)

slide10
Poisson

Retina

Detector

time 

Pop-out from synchrony

temporal code retina topology
Temporal Code (Retina): Topology

separate

bars

single

bar

Neuenschwander & Singer, Nature, 1996 (cat)

temporal code model topology
Temporal Code (Model): Topology

12

1

2

23

3

34

4

2

-2

-50

50 msec

temporal code retina size
Temporal Code (Retina): Size

increasing size 

Neuenschwander & Singer, Vision Res., 1999 (cat)

temporal code model size
Temporal Code (Model): Size

increasing size 

slide15
Retinal Oscillations in other Species

Frog

Primate

Ishikane et al, 1999

Frishman et al., 2000

Also oscillations in Rabbit, Salamander and Human retina

slide17
4

2

-40

0

-40

0

40

40

0

Experimental Test

Cat

Model

66

9.8º9.8º

44

6.3º6.3º

autocorrelation

0.7º0.7º

11

msec

msec

Neuenschwander & Singer (personal communication)

slide18
0

0

200

200

400

400

msec

msec

Single Trial Discrimination

Cat

Model

100%

80%

percent correct

60%

Neuenschwander & Singer (personal communication)

slide19
Object Detection with Spiking Neurons

3rd module

orientation 

2nd module

orientation 

1st module

orientation 

input layer

y 

x 

slide24
image frame

Lukas-Kanade

population code

Depth Map

biomimetic computing future work
Biomimetic Computing: Future Work
  • Implicit object/terrain classification using spiking neurons
    • Coherent motion
    • Smooth contours
    • Textures
    • Depth
  • Autonomous navigation and obstacle avoidance
    • Combine stereo and motion processing
    • Adaptive control: Visual feedback
    • Incorporate spiking neurons
    • On board hardware
  • Prototype robot with artificial vision
    • Commercial and Academic partners
slide26
Biomimetic Computing at LANL

Garrett Kenyon (P-21)

Bryan Travis (P-21)

John George (P-21)

James Theiler (NIS-2)

Greg Stephens (postdoc)

Mark Flynn (postdoc)

Kate Denning (grad student, UCSD)

Sarah Kolitz ( post baccalaureate)

Nils Whitmont (post baccalaureate)

Alex Nugent (post baccalaureate)

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