Modeling How the Bat, Eptesicus
fuscus, Captures Targets Using
Echolocation
Harry R. Erwin, PhD
University of Sunderland
School of Computing and Technology
Or “How does the bat do it?”
• Results from behavioral and computational
research performed with Cynthia F. Moss,
Ph.D., at the the Auditory Neuroethology
Laboratory, Department of Psychology,
University of Maryland, College Park.
• With thanks to Willard Wilson, Peter Abrams,
Myriam Tron, Amy Kryjak, and Paul Kelley.
The basic problem: how the bat
captures prey using echolocation
• Figure from Webster and Brazier,
Experimental Studies on Target
Detection, Evaluation and
Interception by Echo-locating Bats
, 1965
• A bat (Myotis lucifugus) capturing
a moth in foliage.
• 100 millisecond intervals
• The bat had first detected the tree
about 500 milliseconds before the
first image.
• Data available to the bat—a few
biosonar snapshots in the dark.
Some hints: Erstorientierung and
Wiederorientierung
• Reported by Möhres and Öttingen-Spielberg in 1949.
Erstorientierung—when bats first encounter a novel situation.
Wiederorientierung—when bats fly in a familiar space.
• First observed in the behavior of a bat that was accustomed to
roosting in a cage in a room. The researchers rotated the cage
and eventually removed it, and noted that the bat continued to
behave as if the cage were in its normal position until forced to
reorient.
• This is evidence that a bat may use and maintain a world model
that is only modified if circumstances force it to.
Follow-up: Listening in the dark
• Rawson and Griffin investigated this further (see Griffin, Listening in
the dark, the Acoustic Orientation of Bats and Men, Yale, 1958, and
Griffin, “Cognitive aspects of echolocation,” in Nachtigall and
Moore, ed., Animal Sonar: Processes and Performance, Plenum
Press, 1988).
• Asked whether the bats even called when they were dead-reckoning.
• Experiment involved placing and moving obstacles in a flight room.
• Answer: the bat still called, but seemed to ignore the resulting returns.
• More recent evidence suggests echolocation has no cost for bats in
flight (Speakman and Racey, Nature, 350:421-423, 4 April 1991)
Our research goal: To understand
sensory-motor integration in bat
• Using static targets, first answer:
– What localization cues does the bat use?
– What flight control algorithm does the bat use?
– What else has to modeled accurately?
• Then, using moving targets address:
– Is the capture planning algorithm predictive or nonpredictive?
– And characterize the capture planning algorithm.
Predictive versus non-predictive
• Predictive: use models of the target’s motion
and of the bat’s self-motion to control the
capture process
• Non-Predictive: use current state estimates to
control the capture process
– Simple homing
– Lead pursuit
– Lag pursuit
A gray-box model of bat
echolocation and target capture
Target
Position
Acoustics
Sensory
Processing
Motor
Planning
Motor
Responses
• Founded on work (Kuc 95) in sonar-controlled robotics, but
with more biological realism.
• Uses non-predictive control and assumes a stationary target (a =
1.0 and b = 0.0 for an a/b tracking filter)
• Realistic aerodynamic models
• Calibrated from behavioral trial data
• Model behavior based on a world model updated
asynchronously by the echolocation of targets of interest.
Why not a neural model?
• Detailed neuroanatomy was unknown.
• Detailed neurophysiology was unknown.
• Computational cost of modeling a large network
was unacceptably large.
• Experimental work to validate a network model
was unacceptably large.
How target position was estimated
• Range from echo delay time.
• Azimuth by comparing intensity in the two ears.
• Elevation was more difficult…
Problems in estimating elevation
• Traditional (in the literature) “narrowband”
approaches—using intensity samples taken at a small
number of frequencies—failed.
• Back-propagation approach failed—the acoustic scene
was too complex.
• Elevation estimates were necessary to replicate the
observed behavior.
• Finally, a “wideband” match/mismatch process— using
spectral intensity measurements—was successful.
Why?— the intensity spectra of
returns at 0 degrees azimuth
20 kHz
80 kHz
+50
Elevation
-70
Frequency
The notch
From Wotton, Jenison, and Hartley, 1997
Programming the wideband
approach for vertical localization
• Build a library of comparison spectra, measured
at 1 kHz intervals between 20 and 80 kHz, each
spectrum matching an elevation. This was done
at the start of each modeling run.
• Take wide-band spectral measurements in each
ear, average and normalize them.
• Select the elevation that matched most closely
in the L2 norm (i.e., winner-take-all).
How the corresponding neural
system might work
• Use a continuous distribution of comparison
patterns across a neural module, from high
elevation down to low elevation.
• Reafference is then applied to steer the module
to attend to a specific region in space in a
“predictor/corrector” system.
• The signal is matched to all comparison patterns
in parallel as part of a winner-take-all process.
This was motivated by work at
Curtis Bell’s lab
• Evidence for negative images in cerebellum-like
structures of the brain that participate in a
match/mismatch process.
• Curtis C Bell, Angel Caputi and Kirsty Grant, 1997, “Physiology and
Plasticity of Morphologically Identified Cells in the Mormyrid
Electrosensory Lobe”, Journal of Neuroscience, 17:6409--6423.
• Curtis C Bell, Victor Z Han and Yoshiko Sugawara and Kirsty Grant, 1997,
“Synaptic plasticity in a cerebellum-like structure depends on temporal
order”, Nature, 387:278--281.
• Curtis C Bell, D Bodznick, J Montgomery and J Bastian, 1997, “The
Generation and Subtraction of Sensory Expectations within Cerebellum-Like
Structures”, Brain, Behavior and Evolution, 50:17–31.
Model performance with a
stationary target
Top view of
trajectories
Scale in meters
Conclusions from stationary targets
• Localization is three-dimensional, using range,
azimuth and elevation cues
• Capture behavior is more than simple homing—
the bat seems to maneuver for a good position
to perform a homing capture
• Aerodynamics are important
• Trajectories were sensitively dependent on
when targets were detected and decisions made.
Actual performance of a bat
capturing a moving target
Top view
Performance of a non-predictive
algorithm with moving target
Top view
General case
Shows that the bat
was using predictive
tracking
Improved performance after
implementation of a BenedictBordner fixed weight tracking filter
Top view
Predicts future position
This approach generally
performed well but usually
not as good as shown here.
b = a2/(2.0 - a)
Models explored
• Non-predictive
• Fixed-weight predictive (estimates position and
velocity)
• Kalman filter (variable weight)
• Curve fitting model. Assumes the bat learns the
motion (including acceleration) and matches the
target measurements to what it has learned to
allow it to predict the future target location
Some evidence for tracking in
acceleration—side view
Length of acoustic
recording: 8 sec
Length of video
recording: 2.7 sec
Markers indicate the bat
and target locations at
the times when the bat
vocalized.
Evidence for tracking in
acceleration—top view
These bats produce a
‘terminal buzz’—a series of
short cries emitted at about
5-10 msec intervals as they
approach landing or targets.
It is theorized that it is
triggered on the basis of
range. This bat produced the
abortive terminal buzz at an
unusually long range (0.6 m).
Markers are at positions
when the bat vocalized.
Model performance
Case
Mean
Standard
Captures
Approach
Deviation (cm) Attempted
Distance (cm)
Trial Performance 1.9
1.4
10
Nonpredictive
Fixed weight
predictive
Kalman
predictive
Curve fitting
model
1.7
10.7
8.0
1
9
17.9
13.9
6
5.4
4.7
9
Typical performance
The large minimum
distance to target seen
here (about 0.10 m) was
typical of all but the
curve-fitting model.
This suggests that the
bat was taking account
of target acceleration in
its control algorithm.
Conclusions and research directions
• Alpha/beta filter clearly inadequate as a model.
• Fixed and variable weight tracking filters appear unlikely
– In particular, Kalman filters are computationally expensive, involving
matrix inversion, and handle non-linearity poorly.
– Kalman filtering of echolocation cues is known to bias the resulting
localization.
• Learning-based pattern recognition, using innate models,
previous experience, and observations to predict the location of
the target at capture, performed best.
• Now exploring the use of implicit neural models where the bat
has learned where to fly and does not have an explicit
representation of the target.