design constraints for an active sensing system insights from the electric sense mark e. nelson...
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Design constraints for an active sensing
system
Insights from the Electric Sense
Mark E. Nelson
Beckman InstituteUniv. of Illinois, Urbana-Champaign
TALK OUTLINEBrief 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
Distribution of Electric Fish
Black ghost knifefish (Apteronotus albifrons)
mech
an
o
MacIver, fromCarr et al., 1982
Electroreceptor distribution ~14,000 tuberous electroreceptor organs
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
Self-generated Electric Field
Electric Organ Discharge (EOD)
Principle of active electrolocation
Electric Field GenerationPower 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
Electric Field GenerationPower Considerations
Weakly electric fish devote about 1% of basal metabolic rate to EOD productionPulse fish
discharge intermittently higher power per EOD pulse lower duty cycle
Wave fish discharge continuously lower power per EOD cycle 100% duty cycle
Electric Field GenerationPower Considerations
Short, thick tails
Long, thin tails
Electric Field GenerationElectric Organ Design
Electric Field GenerationImpedance matching
Hopkins 99
Principle of active electrolocation
Prey-capture Behavior
Daphnia magna(water flea)
1 mm
Prey capture behavior
Prey capture kinematics
Distance to closest point on body surface
acceleration
Longitudinal velocity
Performance constraints
Minimum sensory range to be useful?Analogy – driving in the fogMinimum useful range = stopping distanceStopping 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
Voltage perturbation at skin :
Estimating signal strength
waterprey
waterpreyfish ar
rE
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electrical contrastprey 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
Reconstructed Electrosensory Image
Daphnia signal characteristics
Fish can detect small prey at a distance of r ~ 3 cmVoltage perturbation at that distance is ~ 1 V
waterprey
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Electroreceptor Constraints
Detection of microvolt perturbations? Thermal noise limits
fkTRV 4)( 2
)4/(1 RCf
VCkTV 30/)( 2
effective bandwidth
10 m cell
RMS variation in membrane potential due to thermal fluctuations. Weaver & Astumian, Science, 1990
Johnson noise
Electroreceptor constraints
Signal ~1 V, thermal noise ~30 VHow to improve SNR Multiple receptor cells per receptor
organ (N ~ 16, 30 V /16 ~ 8 V RMS)
Electroreceptor Design
Electroreceptor constraints
Signal ~1 V, thermal noise ~30 V
How to improve SNR Multiple receptor cells per receptor organ Reduce bandwidth f
frequency
rece
pto
r th
resh
old
fkTRV 4)( 2
Neural coding (Probability code)
Change-point detectionin P-type afferent spike trains
00010101100101010011001010000101001010
Phead = 0.333
Phead = 0.337 Phead =
0.333
Signals, noise, and detectability
Extra “signal” spikes
Count window
Afferent spike train regularization
P-type afferents exhibit remarkable regularity on time scales of about 50 ISIs (~ 200 msec)
Variance-to-mean ratio F(Ik) for P-type afferents
Shuffled data(no correlations)
Ratnam & Nelson J. Neurosci. 2000
Decreased spike train variability
enhances signal detectability
Information coding properties
Spike train regularization enhances informationtransmission
Chacron et al. 2001
Other noise - SNR constraints
Signal is on the order of ~ 1 VIntrinsic 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
Central Processing in the ELL
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