Fig. 1. Sand scorpion stinging captured burrowing
cockroach. Photo taken under UV illumination.
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Philip H. Brownell
(Fax: 001(541)737-0501,
brownell@ava.bcc.orst.edu )
Department of Zoology, Oregon State University
Corvallis, OR 97331-2914, USA
J. Leo van Hemmen
(Phone: +49-89-289-12362,
lvh@ph.tum.de )
Physik Department, TU Muenchen
D-85747 Garching bei Muenchen, Germany
Popular Version of Papers
Y3.03
and
Y3.04
Friday, March 24, 2000, 9:12am and 9:48am
APS March 2000 Meeting, Minneapolis
As large, day-active and predominantly visual creatures, we humans are slow
to appreciate the elegant senses that smaller, nocturnal animals use to
find their way around in the dark. The habits of most night-active
animals, and their sensory abilities, are largely unknown to us simply
because of our inability to observe them directly. A rare exception is the
nocturnal sand scorpion from the dunes of the Mojave Desert. These
insect-eating arachnids - evolutionary cousins of spiders - fluoresce
brilliant yellow-green light under portable blacklights, making them easily
the most observable of Nature's night shift. Through studies of these
animals we are gaining a new appreciation of the non-visual sensory cues
available to animals, and the means by which nervous systems receive,
process and interpret the world outside.
As burrowing animals, sand scorpions have evolved an exquisite sensitivity to
information that comes to them through the surface they live on. The most
important of these signals are subtle mechanical vibrations and faint odors
in sand that are far below our ability to detect. Specialized vibration
sensors on the scorpion's legs guide them to insect prey walking nearby; a
unique appendage called the pectine can discriminate, by smell alone, the
footsteps left by a prospective mate. An important attribute of the sand
dune environment is that it presents and nearly silent sensory backgound
for scorpions, a virtual absence of vibrational or chemical 'noise' that
might interfere with detection of subtle biological signals.
To run this gambit as a nocturnal predator of faster-moving insects, sand
scorpions make use of sensory cues we would not expect to find in sand.
Sand is a sound-absorbent medium and does not favor conduction of
mechanical waves. But over distances of a few decimeters, this natural
substrate conducts both surface waves and compressional sound waves through
the body of sand. These waves have large enough amplitude and low enough
frequency (peak power between 300-400 Hz) to be biologically detectable. Sensors at the tips
(tarsi) of the scorpion's eight legs do just that: One sensor, called the
basitarsal slit sensillum, detects the surface wave propagated by sand,
while others - the numerous sensory hairs touching the surface -
are more responsive to compressional waves. As an insect approaches, the
vibrational signals it generates tip off the scorpion to its location. In
a series of swift and accurate turns, the scorpion moves forward with
pincers outstretched until the prey is eventually grasped and stung
(Fig. 1).
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Fig. 2. Split platform apparatus for testing
scorpion's ability discriminate stimulus time window. |
But how does the sand scorpion determine direction of the sand wave source
so accurately? We can get a handle on the mechanism of localization by
experimentally manipulating the scorpion's sensory experience. One way is
to knock out the eight vibration sensors on the legs one at a time, or in
patterns that reveal how each sensor contributes to the overall computation
of target location. Such manipulations give very systematic changes in the
accuracy of the turning response. In the extreme, animals having only one
or two intact sensors only respond in one direction, toward the leg with
intact sensors. Knowing this we are safe to conclude that target
localization is mediated by a definable and mechanistically simple sensory
system, one that is open to computational modeling and theoretical
analysis.
A second experimental test tells us another essential piece of information
- that the timing of leg stimulation by the passing surface wave, and not
relative amplitude of movement, is the sensory cue that specifies target
angle. This is shown experimentally by spanning the legs of the sand
scorpion between two independently moveable platforms (Fig. 2) so that
time and amplitude of left and right leg movements can be controlled
directly. With all of the right legs on one platform and all of the left
legs on the other, we can ask the animal "which platform moves first?" -
the scorpion answers by turning in that direction. What we learn from this
experiment is that a scorpion can tell when some of its sensors are
stimulated as little as 0.2 milliseconds before the others. Moreover, even
when the "late" platform vibrates at much larger amplitude than the "early"
platform (cf. Fig. 2), the scorpion shows its strong preference for the
time delay cue over relative intensity of leg stimulation by always turning
toward the platform that moves first.
A theory to account for the scorpion's prey localizing ability can be
developed now as a computational model involving a time window. The
model asserts that the surface waves emanating from a target's movements,
say a burrowing cockroach 90 degrees to the right of the scorpion, pass
beneath the eight points of leg contact with the substrate. This array of
vibration sensors is circular (of radius 2-3 cm in adult animals) so that
leg R3 (nearest the source) detects the wave first, followed by R4, R2,
etc. in turn, until the receptors on the opposite side of the array (on
legs L2 and L3) are excited. The neural signals ('spikes') generated by
R3's slit sensilla now propagate to a network of second-order neurons in
the scorpion's brain. These are arranged in a circular structure analogous
to the sensory field. The model assumes that spikes from all sensory
neurons propagate to the central processing neurons with identical delay,
and that eight command neurons in the brain encode the eight
directions of the animal's vibration sensors (see Fig. 3).
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Fig. 3. Model of contra-inhibitory integration of
sensory inputs from circular array of vibration receptors, R1-R4 and
L1-L4. Eight command neurons (black) represent the eight directions of
the legs they receive input from. Neurons R2, R3 and R4 constitute L2's
inhibitory triad through their connections (filled circles) to an
inhibitory neuron (gray) that inhibits (open circles) L2.
The neurons L1, L2, and L3 constitute R3's
inhibitory triad. |
It is a key element of the model, inspired by work of Brownell [1] and
Brownell and Farley [2], that an inhibitory partner neuron, iR3, is excited
at the same time as R3 (cf. Fig. 3). The latter then sends an inhibitory
signal to the neuron opposite to it in the array, L2, along with inhibitory
inputs from iR2 and iR4, the neighboring partners in this inhibitory
triad. For a passing surface wave, the delay between excitation of R3
and L2 in our example is about 1 ms, enough time for R3 to become
vigorously excited while L2 is inhibited by the inhibitory triad. One can
make the above distinction quantitative by assigning a time window to each
command neuron. Each window is opened by the stimulus coming from its
corresponding sensory input and closed by the inhibitory triad opposite to
it. In our example, R3's time window is wide; that of L2 is narrow, if
opened at all. Altogether, we can picture interneuron R3 becoming most
excited and L2 firing hardly at all, with a gradual transition of activity
along the coding ring from R3 to L2. In a sense, the population 'votes' on
the direction of the target, a so-called population code [3].
The most crucial test of our theoretical model is how well it predicts the turning behavior of animals with various
patterns of sensory deficit - the "knock out" experiments described above. Our theory passes this practical test
very well, and then goes one step
further: Since the slit sensilla and other neurons may, but need not,
respond to a passing wave, all that can be, and is, predicted by the model [4] is a probability density for the
scorpion's response and its intrinsic scatter. It is
natural to ask [5], Can the system's `hardware' be tuned so as to
exploit the noise as well as possible? By varying
the effectiveness of synaptic excitation and inhibition within the model circuit, a clear but broad minimum appears
in the response error. This suggests that evolution
through natural selection could fine-tune these central processing networks
for optimal performance. For an animal whose ancestors first appeared on
earth some 450 million years ago, we might safely assume that it has done so.
Supported by National Science Foundation grant (IBN-9320362) and
the German Science Foundation (DFG, FG Hörobjekte).
[1] P.H. Brownell, Science 197, 497-482 (1977);
Sci. Am. 251(6) 94-105 (1984)
[2] P.H. Brownell and R.D. Farley, J. Comp. Physiol. 131,
23-30 & 31-38 (1979)
[3] A. Georgopoulos, A.B. Schwartz, and R.E. Kettner,
Science, 233 1416-1419 (1986)
[4] W. Stürzl, R. Kempter, and J.L. van Hemmen,
Theory of Arachnid Prey Localization, preprint
[5] K. Wiesenfeld, F. Moss, Nature 373, 33-36 (1995)
TU Munich
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