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Electrosensory data acquisition and signal processing strategies in electric fish

Electrosensory data acquisition and signal processing strategies in electric fish. Mark E. Nelson Beckman Institute Univ. of Illinois, Urbana-Champaign. How Electric Fish Work. black ghost knifefish. elephant- nose fish. Fish tank upstairs. Distribution of Electric Fish.

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Electrosensory data acquisition and signal processing strategies in electric fish

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  1. Electrosensory data acquisition and signal processing strategies in electric fish Mark E. Nelson Beckman Institute Univ. of Illinois, Urbana-Champaign

  2. How Electric Fish Work

  3. black ghost knifefish elephant- nose fish Fish tank upstairs Distribution of Electric Fish

  4. Electric Organ Discharge (EOD) - Spatial

  5. EOD - Temporal Pulse Wave

  6. Electric Organ Discharge (EOD)

  7. Principle of active electrolocation

  8. Electroreceptors ~15,000 tuberous electroreceptor organs1 nerve fiber per electroreceptor organup to 1000 spikes/s per nerve fiber mechano MacIver, from Carr et al., 1982

  9. Individual Sensors (Electroreceptors) nerve spikes OUT DV IN

  10. Neural coding inelectrosensory afferent fibers

  11. Probability coding(P-type) afferent spike trains Phead = 0.337 Phead = 0.333 Phead = 0.333 00010101100101010011001010000101001010

  12. Principle of active electrolocation

  13. Electrosensory Image Formation

  14. Electrosensory Image Formation

  15. Electrosensory Image Formation

  16. Prey-capture video analysis

  17. Prey capture behavior

  18. Fish Body Model

  19. Motion capture software Motion capturesoftware

  20. MOVIE: prey capture behavior

  21. Electrosensory Image Reconstruction

  22. Estimating Daphnia signal strength • Voltage perturbation at skin Df: prey volume fish E-field at prey electrical contrast distance from prey to receptor THIS FORMULA CAN BE USED TO COMPUTE THE SIGNAL AT EVERY POINT ON THE BODY SURFACE

  23. MOVIE: Electrosensory Images

  24. System Capabilities • Electric fish can analyze electrosensory images to extract information on target • direction (bearing) • distance • size • shape • composition (impedance)

  25. Distance Discrimination

  26. Distance Discrimination

  27. Shape Discrimination

  28. Shape Discrimination

  29. Shape Generalization

  30. Shape “completion”

  31. Impedance Discrimination

  32. How Do They Do It? • Electric fish analyze dynamic 2D electrosensory images on the body surface to determine • target direction, distance, size, shape and composition (impedance) • Fish might perform an inverse mapping from 2D sensor data to obtain a dense 3D neural representation of world conductivity • sensor data 3D conductivity  action • Alternatively, fish might use sensor data to directly estimate target parameters • sensor data target parameters  action

  33. Parameter estimation (bearing)

  34. Parameter Estimation (cont.)

  35. Dynamic Movement Strategies • Fish are constantly in motion • not a single, static ‘snapshot’ • dynamic, spatiotemporal data stream • With respect to target objects in the environment, fish body movements simultaneously influence the relative positioning of • the sensor array • the electric organ • effector organs (e.g. mouth)

  36. MOVIE: Electrosensory Images

  37. Active motor strategies: Dorsal roll toward prey

  38. Probing Motor Acts chin probing back-and-forth (va et vient ) lateral probing tangentialprobing stationaryprobing

  39. Fish exploring a 4 cm cube

  40. CNS Signal Processing Strategies • Multi-scale filtering • spatial and temporal • Adaptive background subtraction • tail-bend suppression • Attentional ‘spotlight’ mechanisms • local gain control

  41. Multiple Maps

  42. Multi-scale Filtering HINDBRAIN PROCESSING Centromedial map High spatial acuity Low temporal acuity temporal integration PERIPHERAL SENSORS Centrolateral map Inter spatial acuityInter temporal acuity INPUT (from skin receptors) both spatial integration Lateral mapLow spatial acuityHigh temporal acuity

  43. Adaptive Background Subtraction

  44. Adaptive Background Subtraction

  45. Attentional ‘spotlight’ mechanism

  46. Summary • Fish can evaluate direction, distance, size, shape and composition of target objects • How? • model-based • parameter estimation based on 2D image analysis, not full 3D reconstruction • presumably some sort of (adaptive) (extended) (unscented) Kalman-like algorithm • extensive pre-filtering (virtual sensors?) • self-calibrating, adaptive noise suppression, multi-scale spatial and temporal signal averaging • dynamic control of source and array position

  47. Acknowledgements • Colleagues • Curtis Bell (OHSU) • Len Maler (Univ. Ottawa) • Gerhard von der Emde (Univ. Bonn) • Nelson Lab Members • Ling Chen, Rüdiger Krahe, Malcolm MacIver • Funding Agencies • NIMH, NSF The End

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