2. Design constraints for an active sensing system Insights from the Electric Sense Mark E. Nelson Beckman Institute Univ. of Illinois, Urbana-Champaign

3. TALK OUTLINE Brief background on active electrolocation Constraints on … Electric field generation – power considerations Detecting weak fields – thermal noise limits Signal processing under low SNR conditions Role of multiple topographic maps? Coupling of sensing and action Summary

4. Distribution of Electric Fish

5. Black ghost knifefish ( Apteronotus albifrons )

6. mechano MacIver, from Carr et al., 1982 Electroreceptor distribution ~14,000 tuberous electroreceptor organs

7. Ecology & Ethology of A. albifrons inhabits tropical freshwater rivers and streams in South America nocturnal; hunts at night for aquatic insect larvae and small crustaceans uses electric sense for prey detection, navigation, social interactions

8. Self-generated Electric Field

9. Electric Organ Discharge (EOD)

10. Principle of active electrolocation

11. Electric Field Generation Power Considerations What’s the metabolic cost of active sensing? Range related to field strength |E| Field strength falls as d -3 (inverse cube) Power in the electric field scales as |E| 2 Increasing range is expensive: Doubling range requires 8-fold increase in |E| 64-fold increase in power

12. Electric Field Generation Power Considerations Weakly electric fish devote about 1% of basal metabolic rate to EOD production Pulse fish discharge intermittently higher power per EOD pulse lower duty cycle Wave fish discharge continuously lower power per EOD cycle 100% duty cycle

13. Electric Field Generation Power Considerations Short, thick tails Long, thin tails

14. Electric Field Generation Electric Organ Design

15. Electric Field Generation Impedance matching Hopkins 99

16. Principle of active electrolocation

17. Prey-capture Behavior Daphnia magna (water flea) 1 mm

18. Prey capture behavior

19. Prey capture kinematics Distance to closest point on body surface acceleration Longitudinal velocity

20. Performance constraints Minimum sensory range to be useful? Analogy – driving in the fog Minimum useful range = stopping distance Stopping distance = velocity * stopping time fish cruising velocity ~ 10 cm/sec Stopping time = reaction + deceleration sensorimotor delay (~150 msec) + deceleration to zero (~150 msec) Stopping distance ~ 3 cm

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

22. Reconstructed Electrosensory Image

24. Daphnia signal characteristics Fish can detect small prey at a distance of r ~ 3 cm Voltage perturbation at that distance is  ~ 1  V

25. Electroreceptor Constraints Detection of microvolt perturbations? Thermal noise limits effective bandwidth 10  m cell RMS variation in membrane potential due to thermal fluctuations. Weaver & Astumian, Science, 1990 Johnson noise

26. Electroreceptor constraints Signal ~1  V, thermal noise ~30  V How to improve SNR Multiple receptor cells per receptor organ (N ~ 16, 30  V /  16 ~ 8  V RMS)

27. Electroreceptor Design

28. Electroreceptor constraints Signal ~1  V, thermal noise ~30  V How to improve SNR Multiple receptor cells per receptor organ Reduce bandwidth  f frequency receptor threshold

29. Neural coding (Probability code)

30. Change-point detection in P-type afferent spike trains 00010101100101010011001010000101001010 P head = 0.333 P head = 0.337 P head = 0.333

31. Signals, noise, and detectability Extra “signal” spikes Count window

32. Afferent spike train regularization P-type afferents exhibit remarkable regularity on time scales of about 50 ISIs (~ 200 msec) Variance-to-mean ratio F(I k ) for P-type afferents Shuffled data (no correlations) Ratnam & Nelson J. Neurosci. 2000

33. Decreased spike train variability enhances signal detectability

34. Information coding properties

35. Spike train regularization enhances information transmission Chacron et al. 2001

36. Other noise - SNR constraints Signal is on the order of ~ 1  V Intrinsic sensor noise (after spike train regularization) ~ 1  V How strong is the other background noise? Reafferent noise ~ 100  V Environmental noise ~ 100  V Solutions: Subtraction of sensory expectation (Task-dependent) spatiotemporal filtering

37. Central Processing in the ELL

38. Design constraints for active sensing Upper bound on source power ( optimize power delivery to the environment) Lower bound on receptor sensitivity (e.g., thermal noise limits) SNR constraints – clever solutions (e.g., limit receptor bandwidth, spike train statistics, subtraction of sensory expectation, task-dependent spatiotemporal filtering) ( Motor strategies for optimizing sensory acquisition Matching between sensory and locomotor volumes