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Electrosensory data acquisition and signal processing strategies
in electric fish
Mark E. Nelson
Beckman InstituteUniv. of Illinois, Urbana-Champaign
How Electric Fish Work
Distribution of Electric Fish
Fish tank upstairs
blackghost
knifefish
elephant-nosefish
Electric Organ Discharge (EOD) - Spatial
EOD - Temporal
Electric Organ Discharge (EOD)
Principle of active electrolocation
mech
an
o
MacIver, fromCarr et al., 1982
Electroreceptors
~15,000 tuberous electroreceptor organs1 nerve fiber per electroreceptor organ
up to 1000 spikes/s per nerve fiber
Individual Sensors (Electroreceptors)
VIN
nerve spikesOUT
Neural coding inelectrosensory afferent fibers
Probability coding(P-type) afferent spike trains
00010101100101010011001010000101001010
Phead = 0.333
Phead = 0.337 Phead =
0.333
Principle of active electrolocation
Electrosensory Image Formation
Electrosensory Image Formation
Electrosensory Image Formation
Prey-capture video analysis
Prey capture behavior
Fish Body Model
Motion capture softwareMotion capturesoftware
MOVIE: prey capture behavior
Electrosensory Image Reconstruction
Voltage perturbation at skin :
Estimating Daphnia signal strength
waterprey
waterpreyfish ar
rE
/21
/133
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
MOVIE: Electrosensory Images
System Capabilities
Electric fish can analyze electrosensory images to extract information on target
direction (bearing) distance size shape composition (impedance)
Distance Discrimination
Distance Discrimination
Shape Discrimination
Shape Discrimination
Shape Generalization
Shape “completion”
Impedance Discrimination
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
Parameter estimation
(bearing)
Parameter Estimation (cont.)
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)
MOVIE: Electrosensory Images
Active motor strategies: Dorsal roll toward prey
Probing Motor Acts
chin probing back-and-forth (va et vient )
lateral probing
tangentialprobing
stationaryprobing
Fish exploring a 4 cm cube
CNS Signal Processing Strategies
Multi-scale filtering spatial and temporal
Adaptive background subtraction tail-bend suppression
Attentional ‘spotlight’ mechanisms local gain control
Multiple Maps
Multi-scale Filtering
INPUT
(from skin receptors)
Centromedial map High spatial acuity Low temporal acuity
Centrolateral map Inter spatial acuityInter temporal acuity
Lateral map Low spatial acuityHigh temporal acuity
tem
pora
l
inte
grat
ion
bothspatial
integration
HINDBRAIN PROCESSING
PERIPHERALSENSORS
Adaptive Background Subtraction
Adaptive Background Subtraction
Attentional ‘spotlight’ mechanism
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
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
