Crustacean Visual Circuits Underlying Behavior
Summary and Keywords
Decapod crustaceans, in particular semiterrestrial crabs, are highly visual animals that greatly rely on visual information. Their responsiveness to visual moving stimuli, with behavioral displays that can be easily and reliably elicited in the laboratory, together with their sturdiness for experimental manipulation and the accessibility of their nervous system for intracellular electrophysiological recordings in the intact animal, make decapod crustaceans excellent experimental subjects for investigating the neurobiology of visually guided behaviors. Investigations of crustaceans have elucidated the general structure of their eyes and some of their specializations, the anatomical organization of the main brain areas involved in visual processing and their retinotopic mapping of visual space, and the morphology, physiology, and stimulus feature preferences of a number of well-identified classes of neurons, with emphasis on motion-sensitive elements. This anatomical and physiological knowledge, in connection with results of behavioral experiments in the laboratory and the field, are revealing the neural circuits and computations involved in important visual behaviors, as well as the substrate and mechanisms underlying visual memories in decapod crustaceans.
Decapod Crustaceans in Neuroscience Research
Crustaceans, such as crayfish, lobsters, and crabs, have been extensively used and constitute classical subjects for neuroscience research. Investigations on these animals have provided insights of general neurobiological interest relating to matters as diverse as the function of GABA as an inhibitory neurotransmitter (Kravitz, Potter, & Van Gelder, 1962), the operation of electrical synapses (Furshpan & Potter, 1959), the organization of neural substrates involved in behavioral decision-making (Herberholz & Marquart, 2012), coincidence detection as a mechanism to trigger behavioral responses (Edwards, Heitler, & Krasne, 1999), the way in which social hierarchy can affect the properties of individual neurons (Krasne, Heitler, & Edwards, 2014; Yeh, Fricke, & Edwards, 1996), and the neural and molecular mechanisms of long-term memory formation and consolidation (Fustiñana, de la Fuente, Federman, Freudenthal, & Romano, 2014; Pedreira & Maldonado, 2003; Sztarker & Tomsic, 2011). These accomplishments have been feasible due to particular advantages offered by decapod crustaceans for the neurophysiological approach, such as the presence of giant neurons with powerful computational capabilities that are easily accessible for electrophysiological recording, together with the animal’s strength for experimental manipulations and their readiness to behave in laboratory conditions.
This article describes current knowledge of neural circuits involved in visually guided behavior of decapod crustaceans, with emphasis on studies carried out in the crab Neohelice granulata (previously Chasmagnathus granulatus), the species in which the most compelling research on this subject has been done.
Visually Guided Behaviors and Decision-Making Strategies in Crabs
Anyone walking on a beach inhabited by crabs who has tried to approach them for a better look surely got frustrated by their sudden escapes. This is because crabs are well equipped with a visual system that is highly sensitive to image motion, such as that generated by the attack of a predator or the enthusiastic approach of a curious person. Yet, crabs use visual information for multiple behaviors other than avoidance responses. For example, female fiddler crabs are attracted by the male’s beckoning display as part of their courtship (Murai & Backwell, 2006), and the semiterrestrial crab Neohelice granulata performs a robust chasing and prey capture behavior that is elicited by the sight of a small crab or a dummy moving at ground level (Tomsic, Sztarker, Berón de Astrada, Oliva, & Lanza, 2017). The neural circuits involved in these behaviors are still barely known. Therefore, the current description is focused on the neural elements involved in the better-studied visually elicited defensive behavior.
Escaping is not the only strategy a crab has to avoid a visual danger stimulus (VDS). Field and laboratory studies demonstrated that, upon the sight of an approaching predator, the first strategy adopted by the animal is to freeze (Hemmi, 2005a, 2005b; Hemmi & Tomsic, 2015). The advantage of a freezing stage is twofold: on the one hand, it reduces the probability of being detected by the predator, and, on the other hand, it stabilizes the image, allowing a better assessment of the threatening stimulus (Hemmi & Pfeil, 2010; Hemmi & Tomsic, 2012). However, if freezing proves to be worthless because the predator continues approaching, running away is adopted. If the predator is approaching too fast and escaping is not enough, a third defensive strategy is the crab’s raising and pointing its claws toward the menace. Risk assessment and, consequently, the decision about which of the strategies should be implemented, rely on visual information. Once a particular strategy has been adopted, additional decisions are made. For example, when escape is chosen, the animal must decide when to initiate the run, in which direction, at what speed, and so on. Carefully controlled behavioral experiments in the laboratory demonstrated that, while escaping from VDS, a crab continuously adjusts the speed and the direction of its run according to the observed changes occurring in the visual stimulus. The locomotor performance is assessed in a treadmill-like device using computer-generated visual stimuli (Figure 1A). Figure 1B shows that, when challenged with a looming stimulus (the 2D representation of an object’s approach), which expands to different final sizes (simulating that the object stops its approach at different distances, as shown in the lower panel), crabs first freeze, then run away from the danger, and then, whenever the image stops growing, they immediately decelerate (upper panel). This shows that crabs continuously track the object’s approach. If, instead of directly approaching the crab, the object moves across the screen, following a trajectory tangential to the animal (Figure 1C), crabs continuously adjust their direction of escape to run directly away (i.e., at 180°) from the danger stimulus position (Figure 1D). These and other results demonstrate that the escape response is not ballistic, but a constantly regulated behavior (Medan, Berón de Astrada, Scarano, & Tomsic, 2015; Oliva & Tomsic, 2012). Some of the rules involved in visuomotor regulation and the way they are achieved by a number of identified central neurons have been discovered. But, before the neurons are presented and their operations are described, a description of the eye and the organization of the visual nervous system of crustaceans is warranted.
The Eye of Decapod Crustaceans: Adaptations to Visual Environment and to Behavioral Requirements
Like insects, decapod crustaceans possess two compound eyes. However, in contrast to the fixed eyes of hexapods, the eyes of decapods are mounted on the tips of movable peduncles called eyestalks. Many crustaceans, such as prawns, shrimps, crayfish, and lobsters, have their eyestalks oriented horizontally, whereas in crabs they tend to be vertically erected. The compound eye (Figure 2A) is composed of thousands of facets, or ommatidia, that, except for a small portion of cuticle toward the middle side, are distributed all around the tip of the eyestalk (Smolka & Hemmi, 2009; Zeil & Al-Mutairi, 1996). Thus, the visual field of crabs encompasses 360° of the azimuth plane. Species that inhabit mudflats possess a rim of maximal optical resolution at the eye’s equator (Berón de Astrada, Bengochea, Medan, & Tomsic, 2012; Zeil & Al-Mutairi, 1996). Animals actively keep this rim aligned with the horizon by tilting the eyestalks to compensate postural changes. This is an adaptation to the flat environment, where the occurrence of moving objects more likely corresponds to walking neighboring crabs that are seen few degrees above or below the equatorial rim of the eye (Zeil & Hemmi, 2006, 2014). In Neohelice, the eyes also present a pronounced peak of maximal resolution over the lateral pole (Berón de Astrada et al., 2012), which is associated with the sideways orientation preference adopted by the animal when it needs to run fast. In fact, when escaping from a VDS, a crab fixes on, and keeps tracking, the moving stimulus with the lateral pole of the eye by rotating its body (Land & Layne, 1995).
The Visual Nervous System
The central nervous system of crustaceans includes three main regions, the optic lobes placed within the eyestalks (sometimes referred to as the lateral brain), the supraesophageal ganglion (midbrain), and the thoracic ganglia. The optic lobes are connected with the supraesophageal ganglion by the protocerebral tract, while the supraesophageal and the thoracic ganglia are linked by connectives (for a detailed description, see Sandeman, Kenning, & Harzsch, 2014). Although important visual integration processes certainly occur in the midbrain of crustaceans (Glantz, Kirk, & Viancour, 1981; Wood & Glantz, 1980a, 1980b), the organization and functioning of the circuits operating at that level are still barely explored. Therefore, this article concentrates on what is known about visual circuits from studies of the optic neuropils. It is worth noting that, despite their apparent peripheral location in the eyestalk, the deepest optic neuropils are high integrative brain centers.
The visual systems of decapod crustaceans and insects are thought to be homologous (Sinakevitch, Douglass, Scholtz, Loesel, & Strausfeld, 2003; Strausfeld, 1998, 2005; Strausfeld, Ma, & Edgecombe, 2016). Below the retina there are four retinotopic neuropils named from periphery to center: lamina, medulla, lobula, and lobula plate. The optic lobe also comprises the many regions included in the lateral protocerebrum (Harzsch, 2002; Strausfeld, 1998; Sztarker, Strausfeld, & Tomsic, 2005). Figure 2B shows a schematic representation of an eyestalk and the size and location of the different neuropils that compose the optic lobe. As in insects, the optic neuropils are connected by two chiasmata, one between the lamina and the medulla, and a second one between the medulla and the lobula. The presence of two chiasmata poses a challenge in determining how the visual space is represented in each visual neuropil. The visual map organization shown in Figure 2C and 2D was worked out by using dye tracers (Berón de Astrada, Medan, & Tomsic, 2011) and later was confirmed by calcium image experiments (Berón de Astrada, Bengochea, Sztarker, Delorenzi, & Tomsic, 2013). In this figure, vertical color lines represent meridian positions across the retina at 0° (yellow), 90° (red), and 180° (violet), while horizontal lines represent positions in elevation at the eye equator (blue), and at 40° above (green) and 20° below the equator (light blue). In the first chiasm, between the lamina and medulla, the order of representation corresponding to the horizontal plane is rotated (Figure 2C). In the second chiasm, between medulla and lobula, the order of representation corresponding to the vertical plane is rotated (Figure 2D).
The retina is formed by the contribution of eight photoreceptor cells per ommatidium (Strausfeld & Nässel, 1980). The eye of Neohelice possesses approximately 9,000 ommatidia, giving rise to the same amount of columnar units or cartridges in the lamina. The columnar arrangement is retinotopically preserved without coarsening from the lamina through the medulla and the lobula, so that each column brings information from a particular part of the visual field (Berón de Astrada et al., 2013; Sztarker et al., 2005; Sztarker, Strausfeld, Andrew, & Tomsic, 2009; Sztarker & Tomsic, 2014). Figure 3A shows a histological section of the medulla and the lobula where the vertical columnar organization can be observed. Visual information is processed while flowing centripetally through the vertical columnar neurons, and it is collected by relatively few neurons with extensive horizontal processes running across the retinotopic mosaic. The receptive fields of these large tangential neurons encompass large portions of visual space. The horizontally oriented processes of the large tangential neurons, together with the arborization of columnar neurons at particular depths in each optic neuropil, define horizontal (tangential) anatomical layers. This general organization is represented in the scheme of Figure 3B. A brief description of the cytoarchitecture of each one of the optic neuropils is provided below. In order to give an idea of the great complexity of these centers, the organization and cellular elements of the simplest of the optic neuropils, the lamina, is described in detail.
The lamina is the first optic neuropil. Figure 4A shows an image of two broad bundles of photoreceptor axons (PFB) that progressively split into smaller bundles before entering the lamina. The eight photoreceptors from each ommatidium enter the lamina into a single cartridge, forming the core of each columnar anatomical unit. Figure 4B shows a horizontal cross section of the lamina where the cartridge structure can be recognized as a reticular mosaic defined by tangential processes. In the center of each cartridge are the photoreceptor axons (Figure 4B; insets at the left show an enlarged view and a representation of the small square in the picture). Seven of the photoreceptors (R1–R7) end in the lamina. Figure 4C shows examples of the variety of ending specializations. The axon of photoreceptor 8 (R8) traverses the lamina (left part of Figure 4C) and ends near the surface of the medulla (Figure 5A). The photoreceptor signals are passed on to monopolar cells (examples of M1–M5 are shown in Figure 4C) and T cells (T1–T2 in Figure 4C), which carry information from the lamina to the medulla (efferent neurons; see also Figure 5A). The lamina also contains the terminals of centrifugal cells, which bring back information from the medulla to the lamina (C in Figure 4C).
Considering its general organization, the lamina is basically a synaptic layer enclosed by two tangential layers (the distal tangential layer, DTL, and the proximal tangential layer, PTL), and it is covered by monopolar cell bodies (Figure 4A and 4C). Each tangential layer is formed by the long processes of tangential neurons. Two lamina tangential neurons have been described: Tan1, arborizing mainly in DTL, and the wide Tan2, arborizing only in PTL (Figure 4C).
Regarding monopolar cells, several types can be distinguished in crabs, most of which are consistent with previously described types in decapods (Hafner, 1973; Nässel, 1975, 1977; Stowe, 1977). The classes M1–M4 are small-field elements (they have their dendritic branches restricted to the parental cartridge), extending dendritic processes along the whole synaptic layer, like M1 and M2, or only in a sublayer, like M3 and M4. The classes denominated M5 and M6 have dendrites that spread bilaterally into the adjacent cartridges (Sztarker et al., 2009). Monopolar cells seem to be conserved among eumalacostracan crustaceans, the only exception being wide-. field monopolars, which seem to show a greater variation between species in both presence and shape (Nässel, 1975, 1977; Stowe, 1977; Sztarker et al., 2009). This variation might be related to the lifestyles of the species. Wider branches are indeed found in dim-light species, where they probably reflect an adaptation for spatial summation, as is thought to occur in nocturnal insects (Theobald, Greiner, Wcislo, & Warrant, 2006). In semiterrestrial crabs, the presence of two types of wide-field monopolar neurons (M5 and M6) might be an adaptation related to the fact that, although crabs spend many hours as terrestrial inhabitants, they also spend part of the 24-hour cycle submerged in turbid water.
In the medulla, the diversity and complexity of the neural elements are highly amplified. Based on the pattern of ramification of input and columnar neurons, the medulla has been divided into 11 layers (Sztarker & Tomsic, 2014). The first eight layers receive input terminals from the lamina and are considered together as the distal medulla. The last three layers constitute the proximal medulla. About 50 types of transmedullary neurons and 13 types of centrifugal cells have been identified (Sztarker & Tomsic, 2014). In both groups, some cells extend their processes exclusively in one cartridge, while others have dendrites covering several cartridges. In addition, two types of T cells branching only in the proximal medulla were found (Sztarker & Tomsic, 2014). The medulla contains different classes of tangential neurons, including the sustaining and dimming neurons.
The lobula presents a complex and compact structure formed by numerous layers. The variety of types of columnar neurons contained in each cartridge is yet to be described, but it is expected that a similar number of, or even more, types of columnar neurons than the ones described for the medulla will be found.
Because different cell types have their tangential processes oriented exclusively either along the anteroposterior or the lateromedial axis, the fibroarchitectural appearance of the lobula depends on the orientation of the section. In transverse sections, four strata of tangential processes oriented lateromedially are seen (Sztarker et al., 2005). These strata are separated by regions containing arborization profiles belonging to columnar elements and local interneurons, with the profiles of tangential processes running anteroposteriorly. Longitudinal sections of the lobula demonstrate five strata composed of tangential processes oriented anteroposteriorly (Figure 3B; Sztarker et al., 2005). Additionally, 10 distinct input layers bringing information from the medulla have been identified (Bengochea & Berón de Astrada, 2014). The physiology and morphology of several types of wide tangential lobula cells that reflect distinct aspect of visual behaviors have been extensively studied.
The lobula plate is a small neuropil that receives retinotopic information from two different sets of columnar neurons originating in the medulla and lobula, respectively. In the anteroposterior plane, the neuropil possesses four layers defined by the arborizations of such columnar inputs. It presents tangential neurons exiting through the protocerebral tract or connecting the neuropil with the lateral protocerebrum (Bengochea, Berón de Astrada, Tomsic, & Sztarker, 2018).
Physiological Responses of Identified Neurons Throughout the Optic Lobe
Given their small diameter, most columnar elements are hard to record from with intracellular electrodes; however, systematic recordings are possible in photoreceptors and monopolar cells (Glantz, 2007; Glantz & Bartels, 1994). Tangential cells, on the other hand, are easy to record from at the different optic neuropils. Along a descent from the periphery to the more central areas of the optic lobe, a progressive increase in the integrative and computational properties of the cells is observed (Figure 5). At the periphery, photoreceptors respond to a pulse of light with a tonic (i.e., maintained during the entire period of illumination) passive depolarization (Figure 5B, plate i), whereas monopolar and tangential cells from the lamina (Tan1; Figure 5B, plate ii) respond with tonic passive hyperpolarizations. At the level of the medulla, dimming neurons (DN) and sustained neurons (SN) are spiking cells that also respond to a light pulse with a tonic hyperpolarization and a tonic depolarization, respectively, with a corresponding decrease or increase in the firing rate of action potentials (Figure 5B, plates iii and iv). In all these neurons, the magnitude of the tonic response is proportional to the intensity of the light pulse, i.e., they encode duration and intensity of the pulse. A type of transmedullary neuron, which connects the medulla with the lobula, shows a phasic (i.e., transient) response to the onset and the offset of the light pulse (on-off; Figure 5A and 5B, plate v). Thus, neurons at this stage appear to be suited for detecting and encoding illumination changes rather than intensity levels. None of the aforementioned elements, however, shows a special sensitivity for motion. Although some responses to motion can be detected in these cells (Figure 5B, right traces), they are far less conspicuous than responses to a light pulse (left traces). On the contrary, at the level of the lobula, all recorded neurons, such as the monostratified lobula giants (MLG; Figure 5B, plate vi), proved to be much more reactive to moving stimuli than to stationary changes in the intensity of light.
Sustaining and Dimming Neurons
One of the first classes of visual interneurons described in several decapod species is the so-called sustaining fibers (Waterman, Wiersma, & Bush, 1964; reviewed in Wiersma, Roach, & Glantz, 1982). They were discovered by performing extracellular recordings from the optic tract, which prevented identifying their morphology and location. Later studies using intracellular recordings and staining revealed that sustaining fibers belong to wide tangential neurons from the medulla, and they appear to be one of two principal output elements of this neuropil (Glantz, 2014).
Because of their large size, wide tangential neurons are much easier to record from with intracellular electrodes than columnar elements. In this way, sustaining neurons, together with another class of tangential elements of the medulla, called dimming neurons, have been characterized in crayfish (reviewed in Glanz & Miller, 2002) and crabs (Berón de Astrada, Tuthill, & Tomsic, 2009). These neurons have an extensive dendritic tree with branches that run on a single layer along the lateromedial axis of the medulla. The branches converge toward the medial side into a single process that exits the medulla and then descends along the protocerebral tract (Figure 5A). In crayfish, 14 sustaining units have been identified by their functional and anatomical receptive fields (Kirk, Waldrop, & Glantz, 1983). These neurons are involved in the dorsal light reflex, which rotates the eyestalk so that the dorsal retina faces the brightest segment of the dorsal visual space (Glantz & Schoroeter, 2007). Furthermore, sustaining and dimming neurons have been shown to be preferentially sensitive to vertically and horizontally polarized light, respectively (Berón de Astrada et al., 2009; for reviews, see Glantz & Miller, 2002, and Glantz, 2014). However, connection of these neurons with behaviors that may be guided or affected by the polarized plane of light has not yet been established (but see Glantz & Schoroeter, 2007). Compared to their responses to stationary changes in light intensity, such as a pulse of light, the responses of sustaining and dimming neurons to image motion are weak (Figure 5B, plates iii and iv). These responses, however, are maintained upon repeated stimulation, showing no signs of habituation (Tomsic, Berón de Astrada, & Sztarker, 2003). Sustaining and dimming neurons do not respond to visual stimulation of the contralateral eye (Sztarker & Tomsic, 2004), nor do they respond to mechanical stimulation to the animal’s body. These various features contrast with those of wide tangential neurons from the lobula.
Lobula Giant (LG) Neurons: Common Features
After performing extracellular recordings from the protocerebral tract in a wide variety of decapod species, Wiersma and colleagues described, based on response properties, different types of motion-sensitive fibers (reviewed in Wiersma et al., 1982). More recently, intracellular recording and staining of neurons reflecting similar response properties allowed identification of their location and morphology in the crab Neohelice (Berón de Astrada & Tomsic, 2002; Medan, Oliva, & Tomsic, 2007). Because of their size and the neuropil from which these neurons originate, they have been generically termed lobula giant (LG) neurons. The role of LG neurons in visually guided behaviors has been intensively investigated.
In contrast to the wide tangential neurons of the medulla, the LGs are barely sensitive to a pulse of light, but highly responsive to moving stimuli (Figure 5B, plate vi). At present, four classes of LGs have been morphologically identified and physiologically characterized. Two of the classes present monostratified arborizations in the lobula (MLG types 1 and 2), while the other two classes are bistratified (BLG types 1 and 2). The dendritic tree of these neurons consists of several branches that run parallel to each other along the lateromedial axis of the lobula. The dendrites converge into an axonal trunk that descends toward the midbrain (Figure 5A, see also Figure 9B; Berón de Astrada & Tomsic, 2002; Medan et al., 2007; Sztarker et al., 2005). The LGs respond to a moving stimulus with an intense discharge of action potentials. The response to a single moving object is more intense than the response to the movement of the whole visual field, indicating that these neurons are tuned to object detection rather than to panoramic flow field processing (Figure 6; Medan et al., 2007). As is characteristic in all movement-sensitive neurons, including those of vertebrates, the response of the LGs is relatively independent of the background level of illumination (Berón de Astrada & Tomsic, 2002). The response of each neuron is highly consistent on repeated stimulation, but stimulation at intervals shorter than 10 min produces a rapid response reduction. The vast majority of LGs respond to visual stimuli presented either to the ipsilateral or to the contralateral eye, thus demonstrating that processing of binocular visual information occurs at the level of the lobula (Scarano, Sztarker, Medan, Berón de Astada, & Tomsic, 2018; Sztarker & Tomsic, 2004). In addition, three of the four classes respond to both visual information and mechanical stimuli applied to different parts of the body, demonstrating that the integration of multimodal information also occurs in the lobula.
Specific Features of the Different LG Neurons
Despite the commonalities just described, there are important differences between the different classes of LGs. The MLG1 type neurons form an ensemble of 16 elements distributed over the lateromedial lobula axis (the axis that maps the 360° azimuthal positions of visual space; Berón de Astrada et al., 2011). Figure 7A–7C shows three intracellularly stained MLG1 elements, located at different positions in the lobula. At the bottom of each picture is represented the magnitude of response of the neuron to a stimulus moved in the right, left, and up-and-down directions on the left (L), front (F), or right (R) side of the animal. The recordings were taken from the lobula in the right eyestalk. Given the reversal of the retinotopic positions introduced by the optic chiasms (Figure 2C), the MLG1 unit located closer to the medial side of the lobula (Figure 7A) has a receptive field looking at the right side of the animal (green trace), while the MLG1 unit located closer to the lateral side of the lobula (Figure 7C) has a receptive field oriented toward the left (i.e., contralateral to the recording site) side of the animal (red trace). This organization is schematically illustrated in Figure 7D. There are more MLG1 units dedicated to coverage of the lateral visual field, i.e., the area of maximal optical resolution that is used by the animal to fixate and track moving objects. Morphological as well as physiological measurements show that the receptive fields of neighboring MLG1 elements overlap extensively, suggesting that the information on object position is encoded as a population vector (Medan et al., 2015). Thus, with its elements having receptive fields oriented toward different azimuthal positions, the MLG1 system is perfectly suited to encode and convey information on the positions of objects, which is required to escape directly away from a visual threat (Medan et al., 2015).
Interestingly, the vertical receptive field center of the MLG1 elements is at the level of the eye’s equator, coinciding with the horizontal rim of maximal optical resolution observed in the ommatidial array (Berón de Astrada et al., 2011). Because crabs align the eye’s equator with the horizon (Zeil, 1990), the acute visual rim and the vertical receptive field center of the MLG1 neurons are specialized for the perception of events that take place a few degrees above and below the horizon. In addition, MLG1 neurons are sensitive to horizontally, rather than vertically, moving objects (Medan et al., 2015). These optical and neuronal features are clear adaptations to the vertically compressed mudflat world of the crab, where most object motions correspond to the movements of neighboring crabs along the horizontal plane (Tomsic et al., 2017).
The firing rate of MLG1 neurons follows the dynamic of expansion of looming stimuli to the extent that the image expansion remains below 35°, i.e., during the early stage of escaping (Oliva & Tomsic, 2014). In contrast, the apparently unique MLG2 neuron, with a receptive field that encompasses the whole 360°, was found to respond to stimuli approaching from anywhere around the crab, and to encode looming information for images expanding even beyond 35°. Using a wide variety of stimulus dynamics, we found that the MLG2 neuron (Figure 8A and 8B) faithfully encodes the angular velocity of looming stimuli, and thus conveys the information that is used by the animal to continuously adjust its running speed (Oliva & Tomsic, 2016). Figure 8 (panels C, D, and E), illustrates three different dynamics of expansion and the correspondence between the looming stimulus (lower curved line of each panel), the response of a single MLG2 (second trace from bottom), the averaged responses of neurons recorded from different animals (third trace from bottom), and the averaged velocities of escape from several animals (upper trace).
As already described, when a crab faces an approaching object, its first strategy is to freeze, but if the risk escalates because the object continues to approach, the crab decides to run away. Furthermore, if the predator approaches too fast or it is too close, the crab may raise its claws toward the threatening stimulus (Scarano & Tomsic, 2014). The decision to implement any of these defensive strategies depends on the risk assessment made by the animal, which to a large extent is based on the stimulus visual information (but see Hemmi & Tomsic, 2015; Tomsic et al., 2017). In addition to the MLG1 and MLG2 neurons, bistratified lobula giant neurons type 1 and 2 (BLG1 and BLG2, respectively) have been partially investigated in an attempt to understand the neural basis of the crab’s choice of strategy when responding to visual threats. BLG1 neurons appear to have some sensitivity for stimulus elevation (Medan et al., 2007), which makes these elements potentially capable of encoding the distance to an object (Hemmi & Zeil, 2003) or of categorizing a visual object by its elevation (Layne, Land, & Zeil, 1997; Tomsic et al., 2017). In contrast to the three neuronal classes already described, the BLG2 neuron responds to a looming stimulus at the very beginning of its expansion, at approximately the time when the freezing response occurs (Figure 9A; see also Figure 1B), and the activity of this neuron stops with further approach of the stimulus, when the MLG1 and MLG2 neurons start to fire and the crab begins to run away (Figure 9).
Interestingly, a variety of conditions that affect the level of escape, such as seasonal variations, changes in stimulus features, and whether the crab perceives stimuli monocularly or binocularly, also consistently affect the response of LG neurons in a way that closely matches the effects observed at the behavioral level (Sztarker & Tomsic, 2008, 2011). Moreover, crabs inhabiting an area with a high risk of bird predation respond more strongly to VDS (but not to other visual and nonvisual stimuli) than crabs that inhabit an area of low predation risk. Remarkably, the behavioral differences are accompanied by differences in the response of the LG neurons (Magani, Luppi, Nuñez, & Tomsic, 2016). Neurons recorded in animals from the population with the stronger escape response responded with a larger number of spikes to a visual danger stimulus than neurons from animals of the less reactive population. These results represent an exceptional example of the effect of an ecological pressure observed at the level of individual identified neurons (Magani et al., 2016).
Therefore, the motion-sensitive LG neurons are thought to play an important role in the implementation of defensive responses. The feature-detection differences found among the distinctive giant neurons of the lobula, the anatomical proximity of these neurons (Figure 9B), and the matching of their particular responses with different aspects of the defensive responses to visual stimuli suggest that the microcircuit of the LG neurons can operate as a decision-making node that coordinates visual behavioral strategies (Tomsic, 2016).
Neural Plasticity in the Optic Lobe Fosters Long-Term Visual Memory
As active and long-living creatures, decapod crustaceans rely on learning and memory for survival. The learning abilities of crustaceans are well documented (for a review, see Tomsic & Maldonado, 2014). In particular, the crab Neohelice represents one of the classical invertebrate models for studying the neurobiology of memory (for a review, see Tomsic & Romano, 2013). Extensive studies have been performed on the long-term memory changes of the crab escape response to repeated presentations of visual danger stimuli (VDS). Briefly, upon a few repeated presentations of VDS, the crab learns to resign escaping and is able to retain the acquired memory for many days. The endurance of the memory depends on the number of training trials and the intertrial interval. Fifteen training trials separated by few seconds (massed training) lead to a rapid and profound response reduction that, however, fully recovers in 15 min. In contrast, the same number of trials separated by 3 min (i.e., a spaced training session of 45 min) renders a memory that can be retained for more than 5 days (Hermitte, Pedreira, Tomsic, & Maldonado, 1999). This long-term memory is determined by an association between the VDS and the contextual environment (Tomsic, Pedreira, Romano, Hermitte, & Maldonado, 1998)—thus it is an associative memory called a context-signal memory (Tomsic et al., 2003). Memories induced by massed and spaced training are accounted by changes occurring at different neural loci and involve distinct mechanisms of neural plasticity (for reviews, see Tomsic, Berón de Astrada, Sztarker, & Maldonado, 2009; Tomsic & Maldonado, 2014; Tomsic & Romano, 2013). Fast, albeit transient, changes take place in the columnar neurons that link the medulla with the lobula (Berón de Astrada et al., 2013), while the durable visual memories are held by changes that occur in deeper neuropils, for instance at the level of the LG neurons (Sztarker & Tomsic, 2011; Tomsic et al., 2003). A combination of behavioral and physiological results showed that the transient changes occurring in the columnar elements are retinotopically specific (Berón de Astrada et al., 2013). In contrast, the long-term memory comprises complex attributes, such as stimulus spatial generalization and stimulus recognition, which are accounted for by the performance of the LG neurons (Sztarker & Tomsic, 2011). The recognition of the contextual environment, however, is not reflected by the LG neurons (Sztarker & Tomsic, 2011). In fact, this memory attribute has been found to involve the participation of neurons in the hemiellipsoid bodies, a region of the lateral protocerebrum within the eyestalk that is thought to be homologous to the mushroom body of insects (Maza et al., 2016).
Neural Circuits for Processing Panoramic Optic Flow
When moving through space, animals experience a self-generated image motion of the panorama, known as optic flow, which provides essential information for navigating the environment (Krapp & Hengstenberg, 1996). In insects, the information from optic flow is conveyed through retinotopic columnar neurons to be integrated in the lobula plate by wide tangential cells, which constitute one of the most studied and best understood visual motion circuits (for a review, see Borst, 2014). Lobula plate tangential cells of insects are involved in the control of optomotor responses (Geiger & Nässel, 1981; Heisenberg, Wonneberger, & Wolf, 1978). Decapod crustaceans have strong optomotor responses (Barnes & Horridge, 1969; Horridge & Sandeman, 1964; Tomsic & Maldonado, 1990), but the area of the decapod brain involved in processing panoramic optic flow information is still uncertain. For years, attempts to find neurons sensitive to optic flow within the lobula of Neohelice have failed. In fact, all identified LG neurons proved to be particularly sensitive to single-object motion, rather than optic flow (Figure 6; Medan et al., 2007). Moreover, all of them show rapid habituation to repeated object presentation (reflecting the behavioral changes), which rules out these cells as candidates for controlling the unbendable optomotor response (Tomsic, 2016). A recent study providing the first detailed anatomical description of the lobula plate of a crustacean, the crab Neohelice, adds strong support to the notion that the insect and crustacean lobula plate are homologous neuropils. The study shows the presence of four layers in the neuropil, that it receives two separate retinotopic inputs coming from medulla and lobula, and the presence of tangential neurons all mirroring the organization of the insect’s lobula plate (Bengochea et al., 2018). Therefore, the sum of evidence points to the lobula plate as the center of optic flow processing in crustaceans. Final confirmation, however, is pending physiological recordings from neurons in this center.
What Can We Learn About Visual Circuits From Field Studies?
Neurophysiological studies aimed at identifying particular neurons and characterizing their role in a behavioral response are only feasible in laboratory conditions, i.e., in an unnaturally deprived environment where rudimentary visual stimuli are typically used. Behavioral experiments proving that such stimuli are capable of reliably eliciting the response in the laboratory are essential to validate the use of those stimuli in the interpretation of neurophysiological results. The description of the role of LG neurons in the visually elicited escape response of Neohelice illustrates the usefulness of this strategy. However, it is very important to realize that animals do not live in the simplified world of the laboratory, but immersed in the complexity of the “real world.” Two examples illustrate this point.
In laboratory experiments, the escape direction of crabs is directly away from an approaching visual stimulus (Medan et al., 2015), but in the natural environment they run toward their burrows, regardless of the stimulus position or direction (Fathala & Maldonado, 2011; Hemmi, 2005a, 2005b; Hemmi & Tomsic, 2015). Remarkably, information about the direction and distance to the burrow is achieved by path integration, i.e., by a mechanism involving step and turn counts (Walls & Layne, 2009; Zeil & Layne, 2002). Such information must be combined at a certain point with visual information (Hemmi & Zeil, 2003). The fact that three LG neuron types respond to visual information as well as to mechanical stimulation of the animal’s legs (Berón de Astrada & Tomsic, 2002; Medan et al., 2007) opens the possibility that these neurons are involved in the visuomotor integration underlying directed homeward run responses.
Recent field studies revealed that the crab Neohelice pursues and captures smaller individuals or a dummy moved at ground level. This behavior observed in the natural environment inspired the search for small-target movement detector neurons (STMD), like those described in some predatory insects (Nordstrom, 2012). Such neurons have been occasionally recorded from the optic lobe of the crab (Tomsic et al., 2017).
These examples highlight the synergistic effect of going back and forth between field and laboratory studies, and the importance of such dialectical interaction for studying the neurobiology of behavior.
Barnes, W. J., & Horridge, G. A. (1969). Interaction of the movements of the two eyecups in the crab Carcinus. Journal of Experimental Biology, 50, 651–671.Find this resource:
Bengochea, M., & Berón de Astrada, M. (2014). Organization of columnar inputs in the third optic ganglion of a highly visual crab. Journal of Physiology Paris, 108, 61–70.Find this resource:
Bengochea, M., Berón de Astrada, M., Tomsic, D., & Sztarker, J. (2018). The crustacean lobula plate: Morphology, connections and retinotopic organization. Journal of Comparative Neurology, 526, 109–119.Find this resource:
Berón de Astrada, M., Bengochea, M., Medan, V., & Tomsic, D. (2012). Regionalization in the eye of the grapsid crab Neohelice granulata (= Chasmagnathus granulatus): Variation of resolution and facet diameters. Journal of Comparative Physiology A, 198, 173–180.Find this resource:
Berón de Astrada, M., Bengochea, M., Sztarker, J., Delorenzi, A., & Tomsic, D. (2013). Behavioral related neural plasticity in the arthropod optic lobes. Current Biology, 23, 1–10.Find this resource:
Berón de Astrada, M., Medan, V., & Tomsic, D. (2011). How visual space maps in the optic neuropils of a crab. Journal of Comparative Neurology, 519, 1631–1639.Find this resource:
Berón de Astrada, M., & Tomsic, D. (2002). Physiology and morphology of visual movement detector neurons in a crab (Decapoda: Brachyura). Journal of Comparative Physiology A, 188, 539–551.Find this resource:
Berón de Astrada, M., Tuthill, J., & Tomsic, D. (2009). Morphology and physiology of sustaining and dimming neurons of the crab Chasmagnathus granulatus (Brachyura: Grapsidae). Journal of Comparative Physiology A, 195, 791–798.Find this resource:
Borst, A. (2014). Fly visual course control: Behaviour, algorithms and circuits. Nature Review Neuroscience, 15, 590–599.Find this resource:
Edwards, D. H., Heitler, W. J., & Krasne, F. B. (1999). Fifty years of a command neuron: The neurobiology of escape behavior in the crayfish. Trends in Neuroscience, 22, 153–161.Find this resource:
Fathala, M. V., & Maldonado, H. (2011). Shelter use during exploratory and escape behaviour of the crab Chasmagnathus granulatus: A field study. Journal of Ethology, 29, 263–273.Find this resource:
Furshpan, E. J., & Potter, D. D. (1959). Transmission at the giant motor synapses of the crayfish. Journal of Physiology, 145, 289–325.Find this resource:
Fustiñana, M. S., de la Fuente, V., Federman, N., Freudenthal, R., & Romano, A. (2014). Protein degradation by ubiquitin-proteasome system in formation and labilization of contextual conditioning memory. Learning and Memory, 21, 478–487.Find this resource:
Glantz, R. (2014). Visual systems of crustaceans. In C. Derby & M. Thiel (Eds.), Crustacean nervous systems and their control of behavior (pp. 206–234). Oxford, U.K.: Oxford University Press.Find this resource:
Glantz, R. M. (2007). The distribution of polarization sensitivity in the crayfish retinula. Journal of Comparative Physiology A, 193, 893–901.Find this resource:
Glantz, R. M., & Bartels, A. (1994). The spatiotemporal transfer function of crayfish lamina monopolar neurons. Journal of Neurophysiology, 71, 2168–2182.Find this resource:
Glantz, R. M., Kirk, M., & Viancour, T. (1981). Interneurons of the crayfish brain: The relationship between dendrite location and afferent input. Journal of Neurobiology, 12, 311–328.Find this resource:
Glantz, R. M., & Miller, C. S. (2002). Signal processing in the crayfish optic lobe: Contrast, motion and polarization vision. In K. Wiese (Ed.), The crustacean nervous system (pp. 471–483). Berlin, Germany: Springer.Find this resource:
Glantz, R. M., & Schroeter, J. P. (2007). Orientation by polarized light in the crayfish dorsal light reflex: Behavioral and neurophysiological studies. Journal of Comparative Physiology A, 193, 371–384.Find this resource:
Geiger, G., & Nässel, D. R. (1981). Visual orientation behaviour of flies after selective laser beam ablation of interneurons. Nature, 293, 398–399.Find this resource:
Hafner, G. S. (1973). The neural organization of the lamina ganglionaris in the crayfish: A Golgi and EM study. Journal of Comparative Neurology, 152, 255–280.Find this resource:
Harzsch, S. (2002). The phylogenetic significance of crustacean optic neuropils and chiasmata: A re-examination. Journal of Comparative Neurology, 453, 10–21.Find this resource:
Heisenberg, M., Wonneberger, R., & Wolf, R. (1978). Optomotor-blind H31-a Drosophila mutant of the lobula plate giant neurons. Journal of Comparative Physiology A, 124, 287–296.Find this resource:
Hemmi, J. M. (2005a). Predator avoidance in fiddler crabs: 1. Escape decisions in relation to the risk of predation. Animal Behaviour, 69, 603–614.Find this resource:
Hemmi, J. M. (2005b). Predator avoidance in fiddler crabs: 2. The visual cues. Animal Behaviour, 69, 615–625.Find this resource:
Hemmi, J. M., & Pfeil, A. (2010). A multi-stage anti-predator response increases information on predation risk. Journal of Experimental Biology, 213, 1484–1489.Find this resource:
Hemmi, J. M., & Tomsic, D. (2012). The neuroethology of escape in crabs: From sensory ecology to neurons and back. Current Opinion in Neurobiology, 22, 194–200.Find this resource:
Hemmi, J. M., & Tomsic, D. (2015). Differences in the escape response of a grapsid crab in the field and in the laboratory. Journal of Experimental Biology, 218, 3499–3507.Find this resource:
Hemmi, J. M., & Zeil, J. (2003). Robust judgement of inter-object distance by an arthropod. Nature, 421, 160–163.Find this resource:
Herberholz, J., & Marquart, G. (2012). Decision making and behavioral choice during predator avoidance. Frontiers in Neuroscience, 6, 1–15.Find this resource:
Hermitte, G., Pedreira, M. E., Tomsic, D., & Maldonado, H. (1999). Context shift and protein synthesis inhibition disrupt long-term habituation after spaced, but not massed, training in the crab Chasmagnathus. Neurobiology of Learning and Memory, 71, 34–49.Find this resource:
Horridge, G. A., & Sandeman, D. C. (1964). Nervous control of optokinetic response in the crab Carcinus. Proceeding of the Royal Society of London B Biological Science, 161, 216–246.Find this resource:
Kirk, M. D., Waldrop, B., & Glantz, R. M. (1983). A quantitative correlation of contour sensitivity with dendritic density in an identified visual neuron. Brain Research, 274, 231–237.Find this resource:
Krapp, H. G., & Hengstenberg, R. (1996). Estimation of self-motion by optic flow processing in single visual interneurons. Nature, 384, 463–466.Find this resource:
Krasne, F. B., Heitler, W. J., & Edwards, D. H. (2014). The escape behavior of crayfish. In C. Derby & M. Thiel (Eds.), Crustacean nervous systems and their control of behavior (pp. 396–427). Oxford, U.K.: Oxford University Press.Find this resource:
Kravitz, E. A., Potter, D. D., & Van Gelder, N. M. (1962). Gamma-aminobutyric acid and other blocking substances extracted from crab muscle. Nature, 194, 382–383.Find this resource:
Land, M., & Layne, J. E. (1995). The visual control of behaviour in fiddler crabs. II. Tracking control systems in courtship and defense. Journal of Comparative Physiology A, 177, 91–103.Find this resource:
Layne, J., Land, M. F., & Zeil, J. (1997). Fiddler crabs use the visual horizon to distinguish predators from conspecifics: a review of the evidence. Journal of Marine Biology, 77, 43–54.Find this resource:
Magani, F., Luppi, T., Nuñez, J., & Tomsic, D. (2016). Predation risk modifies behaviour by shaping the response of identified brain neurons. Journal of Experimental Biology, 219, 1172–1177.Find this resource:
Maza, F. J., Sztarker, J., Shkedy, A., Peszano, V. N., Locatelli, F. F., & Delorenzi, A. (2016). Context-dependent memory traces in the crab’s mushroom bodies: Functional support for a common origin of high-order memory centers. Proceeding of the National Academy of Science U S A, 113, E7957–E7965.Find this resource:
Medan, V., Berón de Astrada, M., Scarano, F., & Tomsic, D. (2015). A network of visual motion-sensitive neurons for computing object position in an arthropod. Journal of Neuroscience, 17, 6654–6666.Find this resource:
Medan, V., Oliva, D., & Tomsic, D. (2007). Characterization of lobula giant neurons responsive to visual stimuli that elicit escape reactions in the crab Chasmagnathus. Journal of Neurophysiology, 98, 2414–2428.Find this resource:
Monk, T., & Paulin, M. G. (2014). Predation and the origin of neurons. Brain Behavior and Evolution, 84, 246–261.Find this resource:
Murai, M., & Backwell, P. R. Y. (2006). A conspicuous courtship signal in the fiddler crab Uca perplexa: Female choice based on display structure. Behavioral Ecology and Sociobiology, 60, 736–741.Find this resource:
Nordstrom, K. L. (2012). Neural specializations for small target detection in insects. Current Opinion in Neurobiology, 22, 272–278.Find this resource:
Nässel, D. R. (1975). The organization of the lamina ganglionaris of the prawn, Pandalus borealis (Kröyer). Cell and Tissue Research, 163, 445–464.Find this resource:
Nässel, D. R. (1977). Types and arrangements of neurons in the crayfish optic lamina. Cell and Tissue Research, 179, 45–75.Find this resource:
Oliva, D., Medan, V., & Tomsic, D. (2007). Escape behavior and neuronal responses to looming stimuli in the crab Chasmagnathus granulatus (Decapoda: Grapsidae). Journal of Experimental Biology, 210, 865–880.Find this resource:
Oliva, D., & Tomsic, D. (2012). Visuo-motor transformations involved in the escape response to looming stimuli in the crab Neohelice (= Chasmagnathus) granulata. Journal of Experimental Biology, 215, 3488–3500.Find this resource:
Oliva, D., & Tomsic, D. (2014). Computation of object approach by a system of visual motion-sensitive neurons in the crab Neohelice. Journal of Neurophysiology, 112, 1477–1490.Find this resource:
Oliva, D., & Tomsic, D. (2016). Object approach computation by a giant neuron and its relationship with the speed of escape in the crab Neohelice. Journal of Experimental Biology, 219, 3339–3352.Find this resource:
Pedreira, M. E., & Maldonado, H. (2003). Protein synthesis subserves reconsolidation or extinction depending on reminder duration. Neuron, 3, 8863–8869.Find this resource:
Sandeman, D. C., Kenning, M., & Harzsch, S. (2014). Adaptive trends in malacostracan brain form and functions related to behavior. In C. Derby & M. Thiel (Eds.), Crustacean nervous systems and their control of behavior (pp. 11–48). Oxford, U.K.: Oxford University Press.Find this resource:
Scarano, F., & Tomsic, D. (2014). Escape response of the crab Neohelice to computer generated looming and translational visual danger stimuli. Journal of Physiology Paris, 108, 141–147.Find this resource:
Scarano, F., Sztarker, J., Medan, V., Berón de Astrada, M., & Tomsic, D. (2018). Binocular neuronal processing of object motion in an arthropod. Journal of Neuroscience, 38, 6933–6948.Find this resource:
Sinakevitch, I., Douglass, J. K., Scholtz, G., Loesel, R., & Strausfeld, N. J. (2003). Conserved and convergent organization in the optic lobes of insects and isopods, with reference to other crustacean taxa. Journal of Comparative Neurology, 467, 150–172.Find this resource:
Smolka, J., & Hemmi, J. M. (2009). Topography of vision and behaviour. Journal of Experimental Biology, 212, 3522–3532.Find this resource:
Stowe, S. (1977). The retina-lamina projection in the crab Leptograpsus variegatus. Cell and Tissue Research, 185, 515–525.Find this resource:
Strausfeld, N. J. (1998). Crustacean-insect relationships: The use of brain characters to derive phylogeny amongst segmented invertebrates. Brain Behaviour and Evolution, 52, 186–206.Find this resource:
Strausfeld, N. J. (2005). The evolution of crustacean and insect optic lobes and the origins of chiasmata. Arthropod Structure and Development, 34, 235–256.Find this resource:
Strausfeld, N. J., Ma, X., & Edgecombe, G. D. (2016). Fossils and the evolution of the arthropod brain. Current Biology, 26, R989–R1000.Find this resource:
Strausfeld, N. J., & Nässel, D. R. (1980). Neuroarchitecture of brain regions that subserve the compound eyes of crustacea and insect. In H. Autrum (Ed.), Handbook of sensory physiology (Vol. VII/6B, pp. 1–132). Berlin, Germany: Springer Verlag.Find this resource:
Sztarker, J., Strausfeld, N. J., & Tomsic, D. (2005). Organization of optic lobes that support motion detection in a semiterrestrial crab. Journal of Comparative Neurology, 493, 396–411.Find this resource:
Sztarker, J., Strausfeld, N., Andrew, D., & Tomsic, D. (2009). Neural organization of first optic neuropils in the littoral crab Hemigrapsus oregonensis and the semiterrestrial species Chasmagnathus granulatus. Journal of Comparative Neurology, 513, 129–150.Find this resource:
Sztarker, J., & Tomsic, D. (2004). Binocular visual integration in the crustacean nervous system. Journal of Comparative Physiology A, 190, 951–962.Find this resource:
Sztarker, J., & Tomsic, D. (2008). Neuronal correlates of the visually elicited escape response of the crab Chasmagnathus upon seasonal variations, stimuli changes and perceptual alterations. Journal of Comparative Physiology A, 194, 587–596.Find this resource:
Sztarker, J., & Tomsic, D. (2011). Brain modularity in arthropods: Individual neurons that support “what” but not “where” memories. Journal of Neuroscience, 31, 8175–8180.Find this resource:
Sztarker, J., & Tomsic, D. (2014). Neural organization of the second optic neuropil, the medulla, in the highly visual semiterrestrial crab Neohelice granulata. Journal of Comparative Neurology, 522, 3177–3193.Find this resource:
Theobald, J. C., Greiner, B., Wcislo, W. T., & Warrant, E. J. (2006). Visual summation in night-flying sweat bees: A theoretical study. Vision Research, 46, 2298–2309.Find this resource:
Tomsic, D. (2016). Visual motion processing subserving behavior in crabs. Current Opinion in Neurobiology, 41, 113–121.Find this resource:
Tomsic, D., Berón de Astrada, M., & Sztarker, J. (2003). Identification of individual neurons reflecting short- and long-term visual memory in an arthropod. Journal of Neuroscience, 23, 8539–8546.Find this resource:
Tomsic, D., Berón de Astrada, M., Sztarker, J., & Maldonado, H. (2009). Behavioral and neuronal attributes of short- and long-term habituation in the crab Chasmagnathus. Neurobiology of Learning and Memory, 92, 176–182.Find this resource:
Tomsic, D., & Maldonado, H. (1990). Central effect of morphine pretreatment on short- and long-term habituation to a danger stimulus in the crab Chasmagnathus. Pharmacology Biochemistry and Behavior, 36, 787–793.Find this resource:
Tomsic, D., & Maldonado, H. (2014). Neurobiology of learning and memory of crustaceans. In C. Derby & M. Thiel (Eds.), Crustacean nervous systems and their control of behavior (pp. 509–534). Oxford, U.K.: Oxford University Press.Find this resource:
Tomsic, D., Pedreira, M. E., Romano, A., Hermite, G., & Maldonado, H. (1998). Context-US association as a determinant of long-term habituation in the crab Chasmagnathus. Animal Learning and Behavior, 26, 196–209.Find this resource:
Tomsic, D., & Romano, A. (2013). A multidisciplinary approach to learning and memory in the crab Neohelice (Chasmagnathus) granulata. In R. Menzel & P. R. Benjamin (Eds.), Invertebrate learning and memory (pp. 335–353). Düsseldorf, Germany: Elsevier/Academic Press.Find this resource:
Tomsic, D., Sztarker, J., Berón de Astrada, M., Oliva, D., & Lanza, E. (2017). The predator and prey behaviors of crabs: From ecology to neural adaptations. Journal of Experimental Biology, 220, 2318–2327.Find this resource:
Walls, M. L., & Layne, J. E. (2009). Direct evidence for distance measurement via flexible stride integration in the fiddler crab. Current Biology, 19, 25–29.Find this resource:
Waterman, T. H., Wiersma, C. A. G., & Bush, B. M. (1964). Afferent visual responses in the optic nerve of the crab Podophthalmus. Journal of Cell Comparative Physiology, 63, 135–155.Find this resource:
Wiersma, C. A. G., Roach, J. L. M., & Glantz, R. M. (1982). Neural integration in the optic system. In D. C. Sandeman & H. L. Atwood (Eds.), The biology of the crustacea (Vol 4, pp. 1–31). New York, NY: Academic Press.Find this resource:
Wood, H. L., & Glantz, R. M. (1980a). Distributed processing by visual interneurons of crayfish brain. I. Response characteristics and synaptic interactions. Journal of Neurophysiology, 43, 729–740.Find this resource:
Wood, H. L., & Glantz, R. M. (1980b). Distributed processing by visual interneurons of crayfish brain. II. Network organization and stimulus modulation of synaptic efficacy. Journal of Neurophysiology, 43, 741–753.Find this resource:
Yeh, S. R., Fricke, R. A., & Edwards, D. H. (1996). The effect of social experience on serotonergic modulation of the escape circuit of crayfish. Science, 271, 366–369.Find this resource:
Zeil, J. (1990). Substratum slope and the alignment of acute zones in semi-terrestrial crabs (Ocypode ceratophthalmus). Journal of Experimental Biology, 152, 573–576.Find this resource:
Zeil, J., & Al-Mutairi, M. (1996). The variation of resolution and of ommatidial dimensions in the compound eyes of the fiddler crab Uca lactea annulipes (Ocypodidae, Brachyura, Decapoda). Journal of Experimental Biology, 199, 1569–1577.Find this resource:
Zeil, J., & Hemmi, J. M. (2006). The visual ecology of fiddler crabs. Journal of Comparative Physiology A, 192, 1–25.Find this resource:
Zeil, J., & Hemmi, J. M. (2014). Path integration, vision, and decision-making in fiddler crabs. In C. Derby & M. Thiel (Eds.), Crustacean nervous systems and their control of behavior (pp. 484–508). Oxford, U.K.: Oxford University Press.Find this resource:
Zeil, J., & Layne, J. E. (2002). Path integration in fiddler crabs and its relation to habitat and social life. In K. Wiese (Ed.), Crustacean experimental systems in neurobiology (pp. 227–246). Berlin, Germany: Springer.Find this resource: