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