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Design constraints for an active sensing system Insights from the Electric Sense Mark E. Nelson Beckman Institute Univ. of Illinois, Urbana-Champaign
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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
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Distribution of Electric Fish
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Black ghost knifefish ( Apteronotus albifrons )
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mechano MacIver, from Carr et al., 1982 Electroreceptor distribution ~14,000 tuberous electroreceptor organs
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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
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Self-generated Electric Field
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Electric Organ Discharge (EOD)
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Principle of active electrolocation
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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
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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
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Electric Field Generation Power Considerations Short, thick tails Long, thin tails
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Electric Field Generation Electric Organ Design
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Electric Field Generation Impedance matching Hopkins 99
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Principle of active electrolocation
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Prey-capture Behavior Daphnia magna (water flea) 1 mm
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Prey capture behavior
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Prey capture kinematics Distance to closest point on body surface acceleration Longitudinal velocity
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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
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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
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Reconstructed Electrosensory Image
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Daphnia signal characteristics Fish can detect small prey at a distance of r ~ 3 cm Voltage perturbation at that distance is ~ 1 V
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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
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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)
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Electroreceptor Design
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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
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Neural coding (Probability code)
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Change-point detection in P-type afferent spike trains 00010101100101010011001010000101001010 P head = 0.333 P head = 0.337 P head = 0.333
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Signals, noise, and detectability Extra “signal” spikes Count window
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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
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Decreased spike train variability enhances signal detectability
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Information coding properties
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Spike train regularization enhances information transmission Chacron et al. 2001
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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
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Central Processing in the ELL
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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
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