1. Modeling How the Bat, Eptesicus fuscus, Captures Targets Using Echolocation
Harry R. Erwin, PhD
University of Sunderland
School of Computing and Technology
Thank you for inviting me to speak this afternoon.
First, some background: I’m a computational neuroscientist. That means I’m a computer scientist who is trained to work closely with neuroscientists, supporting their research into how the brain works. I share their interests, but I’m also interested in how I would build a robot to work like the brain.
This research extends my dissertation research and has been performed at the Auditory Neuroethology Laboratory of the University of Maryland in collaboration with Professor Cynthia F. Moss.

2. 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.
I’d also like to thank Willard Wilson, Peter Abrams, Myriam Tron, Amy Kryjak, and Paul Kelley of the lab.

3. 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.
This the basic problem we have been studying.
<Read slide.>
I think the relevance to robotics is clear.
A question that has been debated in the bat research literature is whether the bat uses predictive or non-predictive control during target capture, and we appear to have an answer.

4. 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.

5. 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: , 4 April 1991)

6. 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 non-predictive?
And characterize the capture planning algorithm.

7. 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

8. 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.
The model I have been developing is a gray-box model in the tradition of David Marr’s work. I originally proposed a neural model, but the lack of anatomical and physiological data and the sheer magnitude of the system to be modeled caused me to back off to the current model.
<Read slide.>
For those of you who have a background in radar tracking, the model used a simple tracking filter that assumed a stationary target and updated the target state estimate as a linear combination of the current position measurement and the previous estimate.
This model has been revised for the moving target studies.

9. 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.

10. How target position was estimated
Range from echo delay time.
Azimuth by comparing intensity in the two ears.
Elevation was more difficult…

11. 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.

12. 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

13. 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).

14. 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.

15. 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:
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:
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.

16. Model performance with a stationary target
Top view of
trajectories
Scale in meters
The original model performed well for stationary targets.
<Describe slide.>

17. 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.

18. Actual performance of a bat capturing a moving target
Top view
This illustrates the behavior of the bat when capturing a moving target. Scale is in meters.
<Describe slide.>

19. Performance of a non-predictive algorithm with moving target
Top view
General case
Shows that the bat was using predictive tracking
The original model performed poorly for the moving target case. This essentially resolves the question of predictive or non-predictive control. We then investigated some models of predictive control.

20. Improved performance after implementation of a Benedict-Bordner 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)
A Benedict-Bordner filter supposedly responds optimally to target maneuvers, treated as a ramp function. It did handle this case fairly well, but was less good for some other behavioral trials.

21. 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

22. 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.
One of the trials lasted for almost 3 seconds and gave us some insight into the bat’s capabilities. This is a side-view. The bat enters on the right hand side of the figure and spirals down to a target capture. Each marker corresponds to a bat or target location at the time the bat vocalized. The wavy motion here (pointing) appears to reflect the wing beats of the bat.

23. 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.
This is the top view. <read slide.> The bat produced two terminal buzzes, one here and one here. The interesting thing about the first buzz was that the bat was some distance away from the target, evidence that the terminal buzz is not a range-dependent change in cry production behavior.

24. Model performance Case Mean Approach Distance (cm)
Standard Deviation (cm)
Captures Attempted
Trial Performance
1.9
1.4 10
Nonpredictive
1.7 - 1
Fixed weight predictive
10.7
8.0 9
Kalman predictive
17.9
13.9 6
Curve fitting model
5.4
4.7

25. 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.
This modeling run started about 1 second prior to target capture. I also made full length runs, but the model bat’s behavior differed markedly from that of the real bat. <describe> The key point is that an alpha/beta filter was inadequate to replicate the bat’s capture of the target. The real bat adjusted for the target’s acceleration in addition to the position and velocity.

26. 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.
This is where we’re going now. We had earlier found learning-based pattern recognition to be a possible mechanism underlying target localization in elevation. We’ve begun to explore how it may play a role here.
Thank you for your time. Any questions?