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Retinal Oscillations that Encode Large Contiguous Features:

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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Retinal Oscillations that Encode Large Contiguous Features:

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  1. 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

  2. Need for Dynamic Binding

  3. A Model Retina Kolb, Fernandez & Nelson

  4. 1 0 sec Rate Code (Retina) light intensity Dacey & Lee, Nature, 1994 (monkey)

  5. Rate Code (+ Temporal Code?)

  6. Rate Code

  7. Rate + Temporal Code

  8. Detection using Rate Code

  9. Detection using Rate + Temporal Code

  10. Poisson Retina Detector time  Pop-out from synchrony

  11. Temporal Code (Retina): Topology separate bars single bar Neuenschwander & Singer, Nature, 1996 (cat)

  12. Temporal Code (Model): Topology 12 1 2 23 3 34 4 2 -2 -50 50 msec

  13. Temporal Code (Retina): Size increasing size  Neuenschwander & Singer, Vision Res., 1999 (cat)

  14. Temporal Code (Model): Size increasing size 

  15. Retinal Oscillations in other Species Frog Primate Ishikane et al, 1999 Frishman et al., 2000 Also oscillations in Rabbit, Salamander and Human retina

  16. Hypothesis: Oscillations Encode Size

  17. 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)

  18. 0 0 200 200 400 400 msec msec Single Trial Discrimination Cat Model 100% 80% percent correct 60% Neuenschwander & Singer (personal communication)

  19. Object Detection with Spiking Neurons 3rd module  orientation  2nd module  orientation  1st module  orientation   input layer  y  x 

  20. Autonomous Robotic Navigation

  21. Population Coding

  22. Conventional vs. Biomimetic

  23. Depth from Motion

  24. image frame Lukas-Kanade population code Depth Map

  25. 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

  26. 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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