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Cohen & Andersen (2002) Nat Rev Neurosci 3

Learning and Adaptation Strategies in an Obstacle - Avoidance Task Performed in Monkeys CALTECH Biology Division – Andersen Lab Elizabeth B. Torres Richard Andersen. Goal? or Hand path?. Posture ?.

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Cohen & Andersen (2002) Nat Rev Neurosci 3

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  1. Learning and Adaptation Strategies in an Obstacle - Avoidance Task Performed in MonkeysCALTECHBiology Division – Andersen LabElizabeth B. TorresRichard Andersen

  2. Goal? or Hand path? Posture ? Motivation: What is encoded in the PRR region of the Posterior Parietal Cortex of the Monkey (Macaca Mulatta) Cohen & Andersen (2002) Nat Rev Neurosci 3 Lewis & Van Essen (2000) J Comp Neuroll 428

  3. Experimental Design:Obstacle Avoidance, 2 very different handpath solutions ??

  4. ?

  5. ? ?

  6. ? ? ?

  7. Handpaths constrained to a plane

  8. Handpaths constrained to a plane X

  9. Results1 – Subutilize 3D space

  10. Results1 - Subutilize 3D space2 - Adaptation

  11. Obstacles

  12. No Obstacles - Aftereffect

  13. No Obstacle - Deadapted

  14. Results1 - Subutilize 3D space2 – Adaptation3 – Speed Independence (during learning)

  15. Learning Period Speed Independence As the subject learns, More consistent, shorter motions, approach bell-shaped speed profiles From geometric (local) strategy (decoupled from speed) to Kinetic-based (global) optimization (eventually smooth, ballistic motion)

  16. Kalaska’s experim loads effect on PD A5 Dorsal M1

  17. sensory geometry motor Cognitive Goals Geometry Actions needs, understands signal outputs signal

  18. The Straight-line Path of a Curved World

  19. Generalized Pythagorean Theorem (curved world) Euclidean Case (flat world) Metric Tensor

  20. Inner Product (norm)

  21. Local Isometric Imbedding Pullback the Metric of X into Q

  22. The gradient flow generates geodesics paths (“straight-line” paths of a space whose curvature is task-dependent, because we have optimized with respect to a geometry dictated by the norm/cost the task dictates: i.e. dictated by theTARGET !!! Given this, What norm could we optimize in order to approximate these solution paths in hand space?, i.e. to capture the geometry (curvature) of task space and that of the underlying parameter space?

  23. Via Point Temporally, speed-based Spatially-based

  24. Norm in this TASK Space Init Hand Target D1 D2 ViaPoint

  25. Solving the Task Init Hand Target D1 D2 ViaPoint • Obstacles  Weight such that first priority is Via Point • No Obstacle (Deadaptation residual aftereffects)  More weight to Main Target, Via Point is not as important • No Obstacle Straight-line Paths 0 weight for Via Point, 1 for Main Target

  26. Apply Method to Data Paths

  27. Simulations

  28. Future Work • Neural Recordings • Neural Systems Identification

  29. Acknowledgements Sloan-Swartz Foundation Richard Andersen All members of the Andersen Lab for their immense help and incredible patience while teaching me

  30. Nobody asked questions related to this, but I had included the following 2 slides here in case someone wanted to know more about the model implementation of the theory in general

  31. Equation describing the autonomous flow of geometric motion

  32. Compatibility Condition p

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