|
| |
|
|
|
|
||||
| Home Help Feedback Subscriptions Archive Search Table of Contents | ||||
First published online April 18, 2008
Journal of Experimental Biology 211, 1355-1361 (2008)
Published by The Company of Biologists 2008
doi: 10.1242/jeb.010165
Behavioral and neural responses of juvenile crayfish to moving shadows
1 Department of Psychology, University of Maryland, College Park, MD 20742,
USA
2 Neuroscience and Cognitive Science Program, University of Maryland, College
Park, MD 20742, USA
* Author for correspondence (e-mail: jherberholz{at}psyc.umd.edu)
Accepted 12 February 2008
 |
Summary |
|---|
 TOP Summary INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
|---|
Key words: behavioral choice, crayfish, escape, giant neuron, predator
|
INTRODUCTION |
|---|
|
TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
|---|
;
Furshpan and Potter, 1959;
Zucker et al., 1971;
Edwards and Herberholz, 2005).
In addition, the escape systems provide a rare opportunity to test the effects
of neuromodulators on individual neurons and small neural networks
(Yeh et al., 1996;
Teshiba et al., 2001;
Edwards et al., 2002).
Traditionally, escape behavior in crayfish has been investigated by means
of tactile stimulation directed to different body parts, which elicits rapid
flexions of the abdomen that thrust the animals away from the point of
stimulation. Crayfish are equipped with three neural circuits controlling
three different types of tail-flip that differ in response latency and move
the animal in different directions (Wine
and Krasne, 1972; Wine and
Krasne, 1982). The fastest tail-flips are generated by pairs of
bilateral command neurons, the medial giant interneurons (MGs) and the lateral
giant interneurons (LGs). MGs and LGs have non-overlapping receptive fields
and produce reflexive, stereotyped escape behaviors in response to strong
tactile stimulation (Wine and Krasne,
1972; Wine and Krasne,
1982; Herberholz et al.,
2004; Edwards and Herberholz,
2005). Phasic stimulation of the head and thorax activates the
MGs, resulting in a behavioral sequence that rapidly thrusts the animal
backwards, whereas stimuli applied to the abdomen evoke LG-mediated tail-flips
that thrust the animal upward and forward
(Wine and Krasne, 1972) (for a
review, see Edwards et al.,
1999). In contrast to the LGs, the MGs have been implicated in
responses to visual stimuli, although only one published report exists to
support this notion (Wine and Krasne,
1972). Traditionally, visually evoked tail-flips have been
associated with non-giant (Non-G) circuitry
(Wine and Krasne, 1972;
Wine and Krasne, 1982). The
Non-G circuit lacks giant neurons and is less hardwired, allowing for sensory
guidance and predetermination of escape angle and direction. The behavioral
output is much less prompt and much more variable compared with the
stereotypical giant-mediated tail-flips
(Wine and Krasne, 1972). Non-G
tail-flips cannot be distinguished by behavioral appearance from tail-flips
controlled by the giant neurons, thus additional measurements with implanted
electrodes or bath electrodes are required for unambiguous identification
(Krasne et al., 1997;
Herberholz et al., 2001;
Herberholz et al., 2004;
Finley and Macmillan,
2002).
When testing the effects of feeding on the excitability of the LGs
(Krasne and Lee, 1988), it was
found that the LG neurons were inhibited when the animals were eating, while
sensory and motor systems were unaffected. This supported the notion that the
LGs belong to a set of response-dedicated `trigger' neurons, i.e. neurons that
serve as decision makers for behavioral choice. Several decision makers in the
nervous system have since been identified, each responsible for and active
during a specific behavior. Mutual inhibition among these decision-making
neurons creates a behavioral hierarchy
(Edwards, 1991); escape in
crayfish is inhibited during backward walking and feeding
(Bellman and Krasne, 1983;
Beall et al., 1990), and in sea
slug escape, swimming inhibits feeding while feeding inhibits withdrawal
(Kovac and Davis, 1977;
Kovac and Davis, 1980;
Jing and Gillette, 2000). More
recently, the concept of single decision-making neurons has been expanded by
the discovery of large neuron populations that participate in the
decision-making process leading to behavioral choice
(Kristan and Shaw, 1997;
Shaw and Kristan, 1997;
Calabrese, 2003). In addition,
it has been shown that dissimilar or conflicting behaviors are often
controlled by shared neural circuitry
(Gillette et al., 2000;
Esch and Kristan, 2002;
Popescu and Frost, 2002). This
suggests that populations of decision makers exist and single decision-making
neurons contribute to a set of different behaviors that are also shaped by the
environment (Esch et al.,
2002; Briggman et al.,
2005).
We found that juvenile crayfish that were exposed to moving visual threat stimuli while searching for food always displayed one of two antipredatory behaviors. Each animal made a discrete behavioral choice by either tail-flipping backwards or interrupting forward locomotion. The frequency of tail-flips was highest when slowly moving shadows were presented, while stops dominated in response to fast-moving shadows. Tail-flip responses were mediated by activity in the MG neurons, which are elements of a neural circuit primarily associated with escape behavior elicited by mechanosensory stimulation.
|
MATERIALS AND METHODS |
|---|
|
TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
|---|
A total of 92 animals of similar size (ranging from 3.4 cm to 3.6 cm, mean ± s.d. 3.5±0.1 cm; measured from rostrum to tail) were successfully tested in our experiments.
The set-up (Fig. 1A,B) consisted of an experimental tank (height 21 cm, length 31 cm, width 17 cm) separated into different compartments and filled with deionized water to a height of 5.0 cm. The tank was constructed so that water could flow from one end of a narrow tunnel (height 4 cm, length 24 cm, width 5.5 cm) to a `start compartment' (height 21 cm, length 6 cm, width 12 cm) located at the other end (Fig. 1A). Water was directed into the tunnel using a 0.5 cm diameter polyethylene tube connected to a reservoir. Flow was regulated at a rate of 190 ml min–1 by means of a flow meter (Cole-Parmer Instrument Company, Vernon Hills, IL, USA). Water left the tank through a 1.0 cm round opening placed in the start compartment 5.0 cm above the bottom of the tank. The start compartment was separated from the tunnel by a removable barrier. A pair of bath electrodes was attached to the tunnel walls, located 8 cm from the tunnel entrance (Fig. 1A). The inside of the tunnel was painted white and three sides of the tank were covered with white paper. Additionally, the side facing the light source and shadow-generating apparatus was covered with black cardboard (Fig. 1B). A video camera (Canon XL2) was positioned above the tank for recording the behavior. The camera was connected to a TV monitor (Sony WEGA) used to observe the animals during trials (Fig. 1B). A food odor solution was produced by crushing 1.0 g of medium-sized shrimp pellets (Ocean Nutrition Formula One, Aqua Pets Americas), and filtering the extract dissolved in 1 l of distilled water. Stock solution was made fresh every few days and 200 ml of it was diluted in 5 l of distilled water for the experimental solution. Food coloring (red food color; McCormick, Hunt Valley, MD, USA) was used to visualize the flow and five measurements were performed without animals in the tank. The measurements showed consistent flows that arrived at the bath electrodes 13±1 s after they were turned on and at the start compartment 17±3 s later.
|
Shadows were generated by moving a rectangular piece of plastic (15 cmx7.5 cm) through a light beam focused on the experimental tank (Fig. 1B). Crayfish inside the experimental tank were unable to see the goose-neck illuminator (Fiber Lite MI-150; Dolan Jenner Industries, Boxborough, MA, USA) used as the light source or the apparatus that generated the shadow. Brightness inside the tank was measured each day with a lux light meter (SM 700; Milwaukee Instruments Inc., Rocky Mount, NC, USA) before experiments were started. Brightness was reduced from 200 lx (before shadow) to 8 lx when the plastic rectangle completely covered the light beam. The plastic rectangle was moved by a weighted lever, which was held in place before its release by a trigger mechanism. The trigger was operated manually, and upon release caused the plastic rectangle to swing through the light beam. The speed of the rectangle (and resulting shadows) was changed by altering the weight on the lever and by altering the initial start position of the lever.
Shadows were modeled to move at three changing velocities
(Fig. 1C) to most closely
resemble shadows that are cast by an attacking predator. Average velocities
were based on the fast-start (attack) swim speed of predacious fish
(Webb, 1978;
Harper and Blake, 1991;
Domenici and Blake, 1997).
Velocities and accelerations of the shadows were measured with an array of
five silicone photodiodes (Allied Electronics, Fort Worth, TX, USA) each
spaced 4 cm apart and aligned in the center of the experimental tank. The
photodiodes were arranged to cover a distance of 16 cm, ranging from the end
of the tank (where the shadows first became visible) to the position of the
bath electrodes, i.e. the location where the shadows passed above the animals.
The diodes were coupled to an amplifier (A-M Systems, Sequim, WA, USA) and
signals were recorded on a personal computer with Axoscope software (Axon
Instruments, Union City, CA, USA). Five repetitions were recorded for each
shadow and average velocities (between each pair of diodes) were computed from
these measurements (Fig. 1C).
Shadow movements were extremely consistent for each measurement and were
repeatedly confirmed by control measurements during the course of the
experiments. Average velocities (between the first and last photodiode) were
determined as 1.3 m s–1 for slow shadows, 2.4 m
s–1 for medium shadows and 3.7 m s–1 for
fast shadows. Faster shadows accelerated more than slower shadows, although
the differences were small (Fig.
1C).
The behavioral response to a single shadow exposure was recorded on
videotape and electrical recordings derived from the bath electrodes were
stored on a computer. Bath electrodes were used as previously described
(Herberholz et al., 2001;
Herberholz et al., 2004). In
short, the two bath electrodes of a pair were placed on opposite sides inside
the tunnel to record field potentials generated during tail-flips
(Fig. 1A,B). The electrodes
were made of copper wire (24 AWG, 0.25 mm insulation except for the tips;
Belden CDT Inc., St Louis, MO, USA) and connected to an amplifier (A-M
Systems). The bath was grounded using a ground wire. Amplified signals
(x1000) were filtered, digitized and recorded with Axoscope software on
a personal computer. Identification of giant-mediated tail-flips is warranted
by their initial large muscle potentials (mostly due to the simultaneous
activation of muscles by the giant motorneurons) and by the immediately
preceding giant neuron action potentials. LG- and MG-mediated tail-flips can
further be distinguished by clear differences in behavioral appearance.
Recordings from Non-G tail-flips lack the giant neuron action potential and
the large initial deflection, consisting only of smaller and more erratic
muscle potentials (Herberholz et al.,
2001; Herberholz et al.,
2004; Finley and Macmillan,
2002).
Each experiment was started by transferring a single animal from its home tank into the start compartment and allowing it to acclimate for 10 min. Following this period, the video camera positioned above the tank was turned on, the barrier separating the start compartment from the tunnel was carefully opened and the flow of food odor was started. At this time, the software program that recorded the electronic signals from the bath electrodes was also started. Attracted by the food odor, animals entered the tunnel and walked towards the end where the highest concentration of food odor was present. The movement of the crayfish was observed by watching the camera display on the TV monitor. Crayfish sometimes interrupted their movement while walking towards the source of the food odor. Animals that had stopped near the bath electrodes before shadow presentation were later excluded from the results. In all cases, as soon as the rostrum and eyes of the animal passed the pair of bath electrodes, the plastic rectangle was manually released, thus producing a shadow rapidly moving towards and then passing over the animal. Each animal was exposed to only one shadow, and different groups of animals were exposed to different shadow velocities. All individual compartments of the experimental tank were thoroughly washed between each single experiment.
Unless otherwise stated, data are presented as means and standard deviation (mean ± s.d.). Statistical software (SPSS version 14.0; SPSS Inc., Chicago, IL, USA) was used for analysis and each applied statistical test is specified in the text.
|
RESULTS |
|---|
|
TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
|---|
We applied single-frame video analysis (measured on the TV screen) to determine the position, body orientation and location of each animal inside the tunnel as shown in the last video frame before the behavioral response to the shadows was produced. Positions were assigned as left (L), right (R) and center (C), and body orientations were measured with a protractor in angles that diverged from 0°, a position in which the long axis of the animal's body paralleled the tunnel walls. Deviations to the right and left were assigned negative and positive values, respectively. Most animals moved along the sides of the tunnel and were positioned on the left or right (L 43, R 38) whereas fewer animals were positioned in the middle of the tunnel (C 11). On average animals were orientated at an angle of –0.8±11.1° (N=92), which indicates that their bodies were typically aligned with the tunnel walls. Since manual operation of the shadows was guided by the animals' location tracked on the monitor screen, we used the video recordings to determine the exact location of each animal immediately before the behavioral response to the shadow was generated. All tested crayfish were in very similar locations when exposed to the shadows, on average 0.56±0.15 cm (N=92) past the bath electrodes.
|
Fig. 2A shows an example of a crayfish that responded with a tail-flip to a slow-moving shadow. Single frames from the video recording taken during the experiment illustrate the position of the animal in the start compartment at the beginning of the experiment (Fig. 2A, 1), the position in the tunnel approaching the bath electrodes (2), the execution of the tail-flip in response to the shadow (3), and the location of the animal shortly after the tail-flip was produced (4).
When exposed to different shadows (see Materials and methods for
description of shadows), each behavioral output was observed at different
frequencies (Fig. 2B). In
response to slowly moving shadows, most crayfish tail-flipped (71%;
N=22) while fewer animals stopped (29%; N=9). Shadows of
medium velocity elicited a smaller number of tail-flips (43%; N=13)
and more stops (57%; N=17) while tail-flips were almost absent (7%;
N=2) and stops were almost exclusively used (93%; N=29) in
response to fast-moving shadows (Fig.
2B). The observed patterns of behavioral output expressed in
response to the three shadow types differed significantly (Chi-squared test:
P
0.01; Fig. 2B);
responses to slow shadows were significantly different from responses elicited
with shadows of medium and fast speed (Fisher's exact tests:
P0.05 and P0.01, respectively) and responses
observed for shadows of medium velocity differed significantly from responses
evoked by fast-moving shadows (Fisher's exact test: P0.01).
Neural activity during tail-flips
Field potentials generated by animals that tail-flipped in response to
shadows were recorded with the bath electrodes and subsequently analyzed as
described in Materials and methods. Stops never produced field potentials of
notable size in any of the electronic recordings.
The bath electrode recordings obtained for three tail-flips (N=3; 8%) were of low quality due to high noise levels and were not included in the analysis. All other tail-flips (N=34; 92%) were identified by their distinct electronic signature as tail-flips controlled by activity in giant interneurons (Fig. 3). Giant-mediated tail-flips can be unambiguously identified by the giant neuron action potential that precedes large phasic muscle potentials (Fig. 3A,B). All tail-flips were further classified as MG-mediated tail-flips because they induced a backward motion that is characteristic for MG tail-flips, and also because the only other giant interneurons that control tail-flips (i.e. the LG interneurons) cannot be activated by visual input.
|
; Wine,
1984).
Comparison of stops and tail-flips
Since juvenile crayfish use only two different escape strategies (tail-flip
or stop) in response to moving shadows, we decided to analyze possible factors
that may influence the behavioral choice
(Fig. 4). Although we
controlled for hunger state with a rigorous feeding schedule (see Materials
and methods), we tested whether animals that tail-flipped could be differently
motivated (i.e. being more or less hungry) when moving towards the simulated
food source than animals that did not tail-flip. To identify possible
differences in motivation, we measured the time it took the animals to leave
the start compartment and walk to the bath electrodes after the door was
opened and the flow of food odor was started. Animals that later stopped in
response to shadows and animals that later responded with tail-flips showed
very similar times for approach, thus indicating that the behavioral response
to shadows was probably not influenced by different motivational states
(Fig. 4A). Animals that stopped
took on average 163±104 s (N=55) to approach the bath
electrodes while animals that tail-flipped spent on average 159±114 s
(N=37) before reaching the same position in the tunnel, demonstrating
a non-significant difference (Mann–Whitney test: P
0.7;
Fig. 4A).
|
We also compared the positions, body orientations and locations for animals
that made different decisions. The animals' positions inside the tunnel (left
side, center, right side) did not differ significantly (Chi-squared test:
P0.3; Fig. 4B)
for animals that employed stops (L 24, C 5, R 26; N=55) or tail-flips
(L 19, C 6, R 12; N=37). Body orientations were also very similar and
non-significant (Mann–Whitney Test: P0.6;
Fig. 4C) between crayfish that
stopped (–1.2±10.8°; N=55) and crayfish that used
tail-flips (–0.1±11.7°; N=37). Finally, the
locations (in reference to the bath electrodes) of the animals that stopped
(0.55±0.16 cm; N=55) were very similar to the locations for
animals that tail-flipped (0.57±0.14 cm; N=37), reflecting a
non-significant difference (Mann–Whitney test: P0.4;
Fig. 4D). Thus none of the
measured parameters differed significantly for animals that utilized either
tail-flips or stops as a response to the shadows.
|
DISCUSSION |
|---|
|
TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
|---|
We observed inter-individual variability among crayfish exposed to the same
type of stimulus. For example, in response to shadows that moved at medium
velocity, about half the animals chose to tail-flip, whereas the other half
produced stops. All tested animals were kept under the same controlled
conditions; they were equally sized and were fed an equal amount of food at
the same time on the same day. Because they showed very similar latencies for
initiating their food search, it appears that animals that tail-flipped and
animals that stopped were equally motivated to forage. Since all animals were
only tested once using a single shadow presentation, we don't know whether
individuals had a predisposition for one defensive behavior or the other and
whether this choice preference may also be echoed in other behavioral
situations. However, variability in antipredator behaviors is common and has
been described in several other model systems including rodents, where the
prey's choice of freezing or fleeing in response to an approaching predator is
often based on individual differences
(Eilam et al., 1999;
Edut and Eilam, 2004;
Eilam, 2005).
Dichotomous antipredator behaviors have been reported for other
invertebrate and vertebrate animals; for example, in response to distant
predators, freezing responses often dominate while fleeing is elicited as the
predator closes in on the prey (Ranter,
1976; Ranter,
1977; Ydenberg and Dill,
1986). Moreover, threats occurring during inescapable confinement
have been shown to cause freezing more than fleeing in several species of
mammal (Blanchard et al.,
2001).
We found that the number of tail-flips was reduced in favor of stops when
shadows moved towards the animals at high velocities. The effects of different
predator attack speeds on prey escape behavior have been sparsely studied. One
recent report showed that blue tits dodge sideways more often when exposed to
a fast-approaching predator model than when attacked at low speed
(Lind et al., 2002). In our
study, crayfish exposed to fast shadows may have experienced `inescapable'
situations because the high predator attack speeds may have made a timely
escape response physiologically impossible. Thus, in response to such
inescapable attacks, tail-flipping behavior could have decreased because the
associated costs of tail-flipping (e.g. loss of energy, increasing the
distance to the food, enhancing visibility) may have outweighed any benefits
this escape strategy has over the stopping strategy. This possibility requires
further investigation, including measuring the escape latencies of crayfish in
relation to shadow positions and testing how crayfish respond to shadows that
approach from the back – a situation in which MG-controlled tail-flips
could be maladaptive.
Stopping, tail-flipping, and foraging are mutually exclusive behaviors,
i.e. they cannot happen at the same time. Backward walking and defense posture
inhibit the LG neurons in adult crayfish
(Glantz, 1974a;
Glantz, 1974b;
Beall et al., 1990), and
Bowerman and Larimer (Bowerman and Larimer,
1974) described a single descending interneuron in crayfish brain
connectives that upon activation suppressed all ongoing movements and froze
the animal in position. Therefore, it seems plausible that neurons responsible
for temporarily freezing juvenile crayfish inhibit neurons that promote
forward walking and the MG neurons; but relevant analyses of the actual
circuitry have yet to be conducted.
To our knowledge, neurons that connect visual inputs to the MGs have not
been described; a possible consequence of sparse documentation of MG-mediated
tail-flips in response to visual stimuli observed in freely behaving crayfish.
Prior to this study, MG activity evoked by a purely visual stimulus was only
reported once, and shadows were found to be insufficient to reliably evoke MG
tail-flips (Wine and Krasne,
1972). Visual stimuli primarily activate Non-G circuitry that
produces tail-flips of varying angles and directions
(Wine and Krasne, 1972;
Wine and Krasne, 1982).
Wiersma (Wiersma, 1961)
reported giant-mediated responses to visual stimulation but this was before
the nature of Non-G tail-flips was recognized; the tail-flips observed by
Wiersma were most probably mediated by Non-G circuitry
(Wine and Krasne, 1972).
Here we show for the first time that MG escape tail-flips are frequently
used as the initial response to moving shadows. Consistent activation of the
MGs in response to shadows may be facilitated by a number of factors: the
animals were searching for food when stimulated, they were in motion and they
were of juvenile stage. Therefore, the motivational state, behavioral state
and developmental state of the animals may have affected the thresholds for
visually elicited MG tail-flips. The complete absence of Non-G tail-flips as
the primary response to moving shadows in our study may be explained by the
relatively high stimulus velocities we used; Non-G tail-flips are
characterized by longer response latencies than giant-mediated tail-flips,
which make them an ineffective escape strategy when rapid responses are
required (Wine and Krasne,
1972; Reichert and Wine,
1983; Kramer and Krasne,
1984). The importance of the MG circuit in mediating adaptive
escape responses is further supported by two recent reports: the MGs are
frequently activated during aggressive encounters between two crayfish when a
sudden drop in MG threshold identifies the loser of the fight
(Herberholz et al., 2001), and
activity in the MG circuit underlies most escape responses when juvenile
crayfish are attacked by natural predators such as dragonfly nymphs
(Herberholz et al., 2004).
By successfully identifying the MG neurons as major contributors to decision-making in crayfish, we can now take advantage of their accessibility for intracellular physiological experiments. These future investigations will provide a deeper understanding of the neural mechanisms underlying decision-making processes and behavioral choice.
|
References |
|---|
|
TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
|---|
Beall, S. P., Langley, D. J. and Edwards, D. H.
(1990). Inhibition of escape tailflip in crayfish during backward
walking and the defense posture. J. Exp. Biol.
152,577
-582.
Bellman, K. L. and Krasne, F. B. (1983).
Adaptive complexity of interactions between feeding and escape in crayfish.
Science 221,779
-781.
Blanchard, D. C., Griebel, G. and Blanchard, R. J. (2001). Mouse defensive behaviors: pharmacological and behavioral assays for anxiety and panic. Neurosci. Biobehav. Rev. 25,205 -218.[CrossRef][Medline]
Bowerman, R. F. and Larimer, J. L. (1974).
Command fibres in the circumoesophageal connectives of crayfish. I. Tonic
fibres. J. Exp. Biol.
60, 95-117.
Briggman, K. L., Abarbanel, H. D. I. and Kristan, W. B., Jr
(2005). Optical imaging of neuronal populations during
decision-making. Science
307,896
-901.
Calabrese, R. (2003). Behavioral choices: how neuronal networks make decisions. Curr. Biol. 13,140 -142.[CrossRef][Medline]
Domenici, P. and Blake, R. W. (1997). The kinematics and performance of fish fast-start swimming. J. Exp. Biol. 200,1165 -1178.[Abstract]
Edut, S. and Eilam, D. (2004). Protean behavior under barn-owl attack: voles alternate between freezing and fleeing and spiny mice flee in alternating patterns. Behav. Brain Res. 155,207 -216.[CrossRef][Medline]
Edwards, D. H. (1991). Mutual inhibition among neural command systems as a possible mechanism for behavioral choice in crayfish. J. Neurosci. 11,1210 -1223.[Abstract]
Edwards, D. H. and Herberholz, J. (2005). Crustacean models of aggression. In The Biology of Aggression (ed. R. J. Nelson), pp.38 -61. New York: Oxford University Press.
Edwards, D. H., Heitler, W. J. and Krasne, F. B. (1999). Fifty years of a command neuron: the neurobiology of escape behavior in the crayfish. Trends Neurosci. 22,153 -161.[CrossRef][Medline]
Edwards, D. H., Yeh, S. R., Musolf, B. E., Antonsen, B. L. and Krasne, F. B. (2002). Metamodulation of the crayfish escape circuit. Brain Behav. Evol. 60,360 -369.[CrossRef][Medline]
Eilam, D. (2005). Die hard: a blend of freezing and fleeing as a dynamic defense – implications for the control of defensive behavior. Neurosci. Biobehav. Rev. 29,1181 -1191.[CrossRef][Medline]
Eilam, D., Dayan, T., Ben-Eliyahu, S., Schulman, I. I., Shefer, G. and Hendrie, C. A. (1999). Differential behavioural and hormonal responses of voles and spiny mice to owl calls. Anim. Behav. 58,1085 -1093.[CrossRef][Medline]
Esch, T. and Kristan, W. B. (2002).
Decision-making in the leech nervous system. Integr. Comp.
Biol. 42,716
-724.
Esch, T, Mesce, K. A. and Kristan, W. B.
(2002). Evidence for sequential decision making in the medicinal
leech. J. Neurosci. 22,11045
-11054.
Finley, L. A. and Macmillan, D. L. (2002). An analysis of field potentials during different tailflip behaviours in crayfish. Mar. Freshw. Behav. Physiol. 35,221 -234.[CrossRef]
Furshpan, E. J. and Potter, D. D. (1959).
Transmission at the giant motor synapses of the crayfish. J.
Physiol. 145,289
-325.
Gillette, R., Huang, R. C., Hatcher, N. and Moroz, L. L.
(2000). Cost-benefit analysis potential in feeding behavior of a
predatory snail by integration of hunger, taste, and pain. Proc.
Natl. Acad. Sci. USA 97,3585
-3590.
Glantz, R. M. (1974a). The visually evoked defense reflex of the crayfish: habituation, facilitation, and the influence of picrotoxin. J. Neurobiol. 5, 263-280.[CrossRef][Medline]
Glantz, R. M. (1974b). Habituation of the motion detectors of the crayfish optic nerve: their relationship to the visually evoked defense reflex. J. Neurobiol. 5, 489-510.[CrossRef][Medline]
Harper, D. G. and Blake, R. W. (1991). Prey
capture and the fast start performance of Northern Pike Esox lucius.J. Exp. Biol. 155,175
-192.
Herberholz, J., Issa, F. A. and Edwards, D. H.
(2001). Patterns of neural circuit activation and behavior during
dominance hierarchy formation in freely behaving crayfish. J.
Neurosci. 21,2759
-2767.
Herberholz, J., Sen, M. M. and Edwards, D. H.
(2004). Escape behavior and escape circuit activation in juvenile
crayfish during prey-predator interactions. J. Exp.
Biol. 207,1855
-1863.
Jing, J. and Gillette, R. (2000). Escape swim
network interneurons have diverse roles in behavioral switching and putative
arousal in Pleurobranchaea. J. Neurophysiol.
83,1346
-1355.
Kovac, M. P. and Davis, W. J. (1977).
Behavioral choice: neural mechanisms in Pleurobranchaea.
Science 198,632
-634.
Kovac, M. P. and Davis, W. J. (1980). Neural
mechanisms underlying behavioral choice in Pleurobranchaea. J.
Neurophysiol. 43,469
-487.
Kramer, A. P. and Krasne, F. B. (1984).
Crayfish escape behavior: production of tailflips without giant fibre
activity. J. Neurophysiol.
52,189
-211.
Krasne, F. B. and Lee, S. C. (1988). Response-dedicated trigger neurons as control points for behavioral actions: selective inhibition of lateral giant command neurons during feeding in crayfish. J. Neurosci. 8,3703 -3712.[Abstract]
Krasne, F. B., Shamsian, A. and Kulkarni, R.
(1997). Altered excitability of the crayfish lateral giant escape
reflex during agonistic encounters. J. Neurosci.
17,709
-716.
Kristan, W. B., Jr and Shaw, B. K. (1997). Population coding and behavioral choice. Curr. Opin. Neurobiol. 7,826 -831.[CrossRef][Medline]
Lind, J., Kaby, U. and Jakobsson, S. (2002). Split-second escape decisions in blue tits (Parus cearuleus). Naturwissenschaften 89,420 -423.[CrossRef][Medline]
Popescu, I. R. and Frost, W. N. (2002). Highly
dissimilar behaviors mediated by a multifunctional network in the marine
mollusk Tritonia diomedea. J. Neurosci.
22,1985
-1993.
Ranter, S. C. (1976). Animals' defenses: fighting in predator-prey relations. In Advances in the Study of Communication and Affect: II. Nonverbal Communication of Aggression (ed. P. Pliner, T. M. Alloway and L. Krames), pp.175 -190. New York: Plenum.
Ranter, S. C. (1977). Immobility in invertebrates: what can we learn? Psychol. Rec. 1, 1-14.[Medline]
Reichert, H. and Wine, J. J. (1983). Coordination of lateral giant and non-giant systems in crayfish escape behavior. J. Comp. Physiol. 153, 3-15.[CrossRef]
Shaw, B. K. and Kristan, W. B., Jr (1997). The
neuronal basis of the behavioral choice between swimming and shortening in the
leech: control is not selectively exercised at higher circuit levels.
J. Neurosci. 17,786
-795.
Teshiba, T. M., Shamasian, A., Yashar, B., Yeh, S. R., Edwards,
D. H. and Krasne, F. B. (2001). Dual and opposing modulatory
effects of serotonin on crayfish lateral giant escape command neurons.
J. Neurosci. 21,4523
-4529.
Webb, P. W. (1978). Fast-start performance and
body form in seven species of teleost fish. J. Exp.
Biol. 74,211
-226.
Wiersma, C. A. G. (1947). Giant nerve fibre
system of the crayfish. A contribution to comparative physiology of synapses.
J. Neurophysiol. 10,23
-38.
Wiersma, C. A. G. (1961). Reflexes and the central nervous system. In The Physiology of Crustacea. Vol. II (ed. T. H. Waterman), pp. 241-279. New York, London: Academic Press.
Wine, J. J. (1984). The structural basis of an
innate behavioural pattern. J. Exp. Biol.
112,283
-319.
Wine, J. J. and Krasne, F. B. (1972). The
organization of escape behavior in the crayfish. J. Exp.
Biol. 56,1
-18.
Wine, J. J. and Krasne, F. B. (1982). The cellular organization of crayfish escape behavior. In The Biology of Crustacea (ed. D. C. Sandeman and H. L. Atwood), pp.241 -292. New York: Academic Press.
Ydenberg, R. C. and Dill, L. M. (1986). The economics of fleeing from predators. Adv. Study Behav. 16,229 -249.
Yeh, S. R., Fricke, R. A. and Edwards, D. H. (1996). The effect of social experience on serotonergic modulation of the escape circuit of crayfish. Science 271,366 -369.[Abstract]
Zucker, R. S., Kennedy, D. and Selverston, A. I.
(1971). Neural circuits mediating escape responses in crayfish.
Science 173,645
-650.
CiteULike 
Complore 
Connotea 
Del.icio.us 
Digg 
Reddit 
Technorati 
Twitter What's this?
This article has been cited by other articles:
|
|
 |
|
  C. M. Harley, B. A. English, and R. E. Ritzmann Characterization of obstacle negotiation behaviors in the cockroach, Blaberus discoidalis J. Exp. Biol., May 15, 2009; 212(10): 1463 - 1476. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
