# Sensory Systems/NonPrimates

Primates are animals belonging to the class of mammals. Primates include humans and the nonhuman primates, the apes, monkeys, lemurs, tree-shrews, lorises, bush babies and tarsiers. They are characterized by a voluminous and complicated forebrain. Most have excellent sight and are highly adapted to an arboreal existence, including in some species the possession of a prehensile tail. Non primates on the other hand often posses smaller brains. But as we learn more about the rest of the animal world, it’s becoming clear that non-primates are pretty intelligent too. Some examples include pigs, octopus, and crows.[1]

In many branches of mythology, the crow plays a shrewd trickster, and in the real world, crows are proving to be quite a clever species. Crows have been found to engage in feats such as tool use, the ability to hide and store food from season to season, episodic-like memory, and the ability to use personal experience to predict future conditions.

As it turns out, being piggy is actually a pretty smart tactic. Pigs are probably the most intelligent domesticated animal on the planet. Although their raw intelligence is most likely commensurate with a dog or cat, their problem-solving abilities top those of felines and canine pals.

If pigs are the most intelligent of the domesticated species, octopuses take the cake for invertebrates. Experiments in maze and problem-solving have shown that they have both short-term and long-term memory. Octopuses can open jars, squeeze through tiny openings, and hop from cage to cage for a snack. They can also be trained to distinguish between different shapes and patterns. In a kind of play-like activity (one of the hallmarks of higher intelligence species) octopuses have been observed repeatedly releasing bottles or toys into a circular current in their aquariums and then catching them.

## Zebra Finches: Neural Mechanism for Song Learning

### Introduction

Over the past four decades songbirds have become a widely used model organism for neuroscientists studying complex sequential behaviours and sensory-guided motor learning. Like human babies, young songbirds learn many of the sounds they use for communication by imitating adults. One songbird in particular, the zebra finch (Taeniopygia guttata), has been the focus of much research because of its proclivity to sing and breed in captivity and its rapid maturation. The song of an adult male zebra finch is a stereotyped series of acoustic signals with structure and modulation over a wide range of time scales, from milliseconds to several seconds. The adult zebra finch song comprises a repeated sequence of sounds, called a motif, which lasts about a second. The motif is composed of shorter bursts of sound called syllables, which often contain sequences of simpler acoustic elements called notes as shown in Fig.1. The songbirds learning system is a very good model to study the sensory-motor integration because the juvenile bird actively listens to the tutor and modulates its own song by correcting for errors in the pitch and offset. The neural mechanism and the architecture of the song bird brain which plays a crucial role in learning is similar to the language processing region in frontal cortex of humans. Detailed study of the hierarchical neural network involved in the learning process could provide significant insights into the neural mechanism of speech learning in humans.

Figure 1: Illustration of the typical song structure & learning phases involved in song bird. Upper panel: Phases involved in the song learning process. Middle panel: Structure of a crystallized song a,b,c,d,e denote the various syllable in the song. Lower panel: Evolution of the song dynamics during learning.

### Illustration of the typical song structure & learning phases involved in song bird.

Song-learning proceeds through a series of stages, beginning with sensory phase where the juvenile bird just listens to its tutor (usually its father) vocalizing, often without producing any song-like vocalization itself. The bird uses this phase to memorize a certain structure of the tutor song, forming the neural template of the song. Then it enters the sensorimotor phase, where it starts babbling the song and correcting its errors using auditory feedback. The earliest attempt to recreate the template of the tutor song is highly noisy, unstructured and variable and it is called sub-song. An example is shown in the spectrogram in Fig.1. Through the subsequent days the bird enters a “plastic phase” where there is a significant amount of plasticity in the neural network responsible for generating highly structured syllables and the variability is reduced in the song. By the time they reach sexual maturity, the variability is substantially eliminated—a process called crystallization—and the young bird begins to produce a normal adult song, which can be a striking imitation of the tutor song (Fig.1). Thus, the gradual reduction of song variability from early sub-song to adult song, together with the gradual increase in imitation quality, is an integral aspect of vocal learning in the songbird. In the following sections we will explore several parts of the avian brain and the underlying neural mechanisms that are responsible for this remarkable vocal imitation observed in these birds.

### Hierarchical Neural Network involved in the generation of song sequences

It is important to understand the neuroanatomy of the songbird in detail because it provides significant information about the learning mechanisms involved in various motor and sensory integration pathways. This could ultimately shed light on the language processing and vocal learning in humans. The exact neuroanatomical data about human speech processing system is still unknown and songbird anatomy and physiology will enable us to make plausible hypotheses. The comparison of the mammalian brain and a songbird (avian) brain is made in the final section of this chapter in (Fig. 6). The pathway observed in the avian brain can be broadly divided into motor control and anterior forebrain pathway as shown in (Fig.2). The auditory pathway provides the error feedback signals which leads to potentiation or depression of the synaptic connections involved in motor pathways, which plays a significant role in vocal learning. The motor control pathway includes Hyperstriatum Ventrale, pars Caudalis (HVC), Robust Nucleus of Acropallium (RA), Tracheosyringeal subdivision of the hypoglossal nucleus (nXIIts) and Syrinx. This pathway is necessary for generating the required motor control signals which produce highly structured songs and coordinating breathing with singing. The anterior forebrain pathway includes Lateral magnocellular nucleus of anterior nidopallium (LMAN), Area X (X) and the medial nucleus of dorsolateral thalamus (DLM). This pathway plays a crucial role in song learning in juveniles, song variability in adults and song representation. The auditory pathway includes substantia nigra (SNc) and the ventral tegmental area (VTA), which plays a crucial role in auditory inputs processing and analyzing the feedback error. The muscles of the syrinx are innervated by a subset of motor neurons from nXIIts. A primary projection to the nXIIts descends from neurons in the forebrain nucleus RA. Nucleus RA receives motor-related projections from another cortical analogue, nucleus HVC, which in turn receives direct input from several brain areas, including thalamic nucleus uvaeformis (Uva).

Figure 2. Architecture of the song bird brain & various pathways carrying motor and auditory feed- back signals.

### Neural Mechanism for the generation of highly structured & temporally precise syllable pattern

Nuclei HVC and RA are involved in the motor control of song in a hierarchical manner (Yu and Margoliash 1996). Recordings in singing zebra finches have shown that HVC neurons that project to RA transmit an extremely sparse pattern of bursts: each RA-projecting HVC neuron generates a single highly stereotyped burst of approximately 6 ms duration at one specific time in the song (Hahnloser, Kozhevnikov et al. 2002). During singing, RA neurons generate a complex sequence of high-frequency bursts of spikes, the pattern of which is precisely reproduced each time the bird sings its song motif (Yu and Margoliash 1996). During a motif, each RA neuron produces a fairly unique pattern of roughly 12 bursts, each lasting ~10 ms (Leonardo and Fee 2005). Based on the observations that RA-projecting HVC neurons generate a single burst of spikes during the song motif and that different neurons appear to burst at many different times in the motif, it has been hypothesized that these neurons generate a continuous sequence of activity over time (Fee, Kozhevnikov et al. 2004, Kozhevnikov and Fee 2007). In other words, at each moment in the song, there is a small ensemble of HVC (RA) neurons active at that time and only at that time (Figure 3), and each ensemble transiently activates (for ~10 ms) a subset of RA neurons determined by the synaptic connections of HVC neurons in RA (Leonardo and Fee 2005). Further, in this model the vector of muscle activities, and thus the configuration of the vocal organ, is determined by the convergent input from RA neurons on a short time scale, of about 10 to 20 ms. The view that RA neurons may simply contribute transiently, with some effective weight, to the activity of vocal muscles is consistent with some models of cortical control of arm movement in primates (Todorov 2000). A number of studies suggest that the timing of the song is controlled on a millisecond-by-millisecond basis by a wave, or chain, of activity that propagates sparsely through HVC neurons. This hypothesis is supported by an analysis of timing variability during natural singing (Glaze and Troyer 2007) as well as experiments in which circuit dynamics in HVC were manipulated to observe the effect on song timing. Thus, in this model, song timing is controlled by propagation of activity through a chain in HVC; the generic sequential activation of this HVC chain is translated, by the HVC connections in RA, into a specific precise sequence of vocal configurations.

Figure 3. Mechanisms of sequence generation in the adult song motor pathway. Illustration of the hypothesis that RA-projecting HVC (HVC(RA)) neurons burst and activate each other sequentially in groups of 100 to 200 coactive neurons. Each group of HVC neurons drives a distinct ensemble of RA neurons to burst. The neurons converge with some effective weight at the level of the motor neurons to activate syringeal muscles.

### Synaptic Plasticity in Posterior Forebrain Pathway is a potential substrate for vocal learning

A number of song-related avian brain areas have been discovered (Fig. 4A). Song production areas include HVC (Hyperstriatum Ventrale, pars Caudalis) and RA (robust nucleus of the arcopallium), which generate sequences of neural activity patterns and through motor neurons control the muscles of the vocal apparatus during song (Yu and Margoliash 1996, Hahnloser, Kozhevnikov et al. 2002, Suthers and Margoliash 2002). Lesion of HVC or RA causes immediate loss of song (Vicario and Nottebohm 1988). Other areas in the anterior forebrain pathway (AFP) appear to be important for song learning but not production, at least in adults. The AFP is regarded as an avian homologue of the mammalian basal ganglia thalamocortical loop (Farries 2004). In particular, lesion of area LMAN (lateral magnocellular nucleus of the nidopallium) has little immediate effect on song production in adults, but arrests song learning in juveniles (Doupe 1993, Brainard and Doupe 2000). These facts suggest that LMAN plays a role in driving song learning, but the locus of plasticity is in brain areas related to song production, such as HVC and RA. Doya and Senjowski in 1998 proposed a tripartite schema, in which learning is based on the interactions between actor and a critic (Fig.4B). The critic evaluates the performance of the actor at a desired task. The actor uses this evaluation to change in a way that improves its performance. To learn by trial and error, the actor performs the task differently each time. It generates both good and bad variations, and the critic’s evaluation is used to reinforce the good ones. Ordinarily it is assumed that the actor generates variations by itself. However, the source of variation is external to the actor. We will call this source the experimenter. The actor was identified with HVC, RA, and the motor neurons that control vocalization. The actor learns through plasticity at the synapses from HVC to RA (Fig. 4C). Based on evidence of structural changes like axonal growth and retraction that take place in the HVC to RA projection during song learning, this view is widely regarded as a plausible mechanism. For the experimenter & critic, Doya and Senjowski turned to the anterior forebrain pathway, hypothesizing that the critic is Area X and the experimenter is LMAN.

Figure 4. Plasticity in Specific pathways enabling learning. (A) Avian song pathways and the tripartite hypotheses. A: avian brain areas involved in song production and song learning. Premotor pathway (open) includes areas necessary for song production. Anterior forebrain pathway (ﬁlled) is required for song learning but not for song production. (B) Tripartite reinforcement learning schema: the actor produces behaviour; the experimenter sends ﬂuctuating input to the actor, producing variability in behaviour that is used for trial-and-error learning; the critic evaluates the behaviour of the actor and sends a reinforcement signal to it. For birdsong, the actor includes premotor song production areas HVC and RA. (C) Plastic and empiric synapses. RA receives synaptic input from both HVC and LMAN. We will call the HVC synapses “plastic,” in keeping with the hypothesis that these synapses are the locus of plasticity for song learning.

### Biophysically realistic synaptic plasticity rules underlying song learning mechanism

 Biophysically realistic model The role of LMAN input to RA is to produce a fluctuation that is static over the duration of a song bout, directly in the synaptic strengths from premotor nucleus HVC to RA. From a functional perspective, the model of Doya and Sejnowski is akin to weight perturbation (Dembo and Kailath 1990, Seung 2003) and relatively easy to implement: a temporary but static HVC->RA weight change that lasts the duration of one song causes some change in song performance. If performance is good, the critic sends a reinforcement signal that makes the temporary static perturbation permanent. From a neurobiological perspective this model requires machinery whereby N-methyl-Daspartate (NMDA)-mediated synaptic transmission from LMAN to RA can drive synaptic weight changes that remain static over the 1 to 2 seconds. In short, LMAN appears to drive fast, transient song fluctuations on a subsyllable level, affected by ordinary excitatory transmission that drives dynamic postsynaptic membrane conductance fluctuations in the postsynaptic RA neurons. The goal of this model is to relate the highlevel concept of reinforcement learning by the tripartite schema to a biologically realistic lower level of description in terms of microscopic events at synapses and neurons in the birdsong system. It should demonstrate song learning in a network of realistic spiking neurons, and examine the plausibility of reinforcement algorithms in explaining biological fine motor skill learning with respect to learning time in the birdsong network. The present model is based on many of the same general assumptions that were made by Doya and Sejnowski. We assume a tripartite actor-critic-experimenter schema. The critic is weak, providing only a scalar evaluation signal. The HVC sequence is fixed, and only the map from HVC to the motor neurons is learned, through plasticity at the HVC->RA synapses. LMAN perturbs song through its inputs to the song premotor pathway. However, the structure and dynamics of LMAN inputs, and their influence on learning, are different, with distinct neurobiological implications. In keeping with our hypothesis that the function of LMAN drive to RA is to perform experiments for trial-and-error learning, the connections from LMAN to RA will be called empiric synapses (Fig. 4C). The conductance of the plastic synapse from neuron j in HVC to neuron i in RA is given by ${\displaystyle W_{ij}S_{ij}HVC(t)}$, where the synaptic activation ${\displaystyle S_{ij}HVC(t)}$ determines the time course of conductance changes, and the plastic parameter ${\displaystyle W_{ij}}$ determines their amplitude. Changes in ${\displaystyle W_{ij}}$ are governed by the plasticity rule is given by ${\displaystyle {\frac {\partial {{W}_{ij}}}{\partial t}}=\eta \,R(t)\,{{e}_{ij}}(t)}$ The positive parameter ${\displaystyle \eta }$, called the learning rate, controls the overall amplitude of synaptic changes. The eligibility trace ${\displaystyle e_{ij}(t)}$ is a hypothetical quantity present at every plastic synapse. It signifies whether the synapse is "eligible" for modification by reinforcement and is based on the recent activation of the plastic synapse and the empiric synapse onto the same RA neuron ${\displaystyle {{e}_{ij}}(t)=\int \limits _{0}^{t}{d{t}'}\,G(t-{t}')\left[S_{i}^{LMAN}(t)-\left\langle S_{i}^{LMAN}\right\rangle \right]\,\,S_{ij}^{HVC}(t)}$ Here ${\displaystyle S_{i}^{LMAN}(t)}$ is the conductance of the empiric (LMAN->RA) synapse onto the RA neuron. The temporal filter G(t) is assumed to be nonnegative, and its shape determines how far back in time the eligibility trace can "remember" the past. The instantaneous activation of the empirical synapses is dependent on the average activity ${\displaystyle \left\langle \,S_{i}^{LMAN}(t)\right\rangle }$. The learning principles follows two basic rules shown in (Fig.5). First rule: If coincident activation of a plastic (HVC->RA) synapse and empiric (LMAN->RA) synapse onto the same RA neuron is followed by positive reinforcement, then the plastic synapse is strengthened. Second rule: If activation of a plastic synapse without activation of the empiric synapse onto the same RA neuron is followed by positive reinforcement, then the plastic synapse is weakened. The rules based on dynamic conductance perturbations of the actor neurons perform stochastic gradient ascent on the expected value of the reinforcement signal. This means that song performance as evaluated by the critic is guaranteed to improve on average.

### Comparison between Mammalian & Songbird brain architecture

The avian Area X is homologous to the mammalian basal ganglia (BG) and includes striatal and pallidal cell types. The BG forms part of a highly conserved anatomical loop-through several stations, from cortex to the BG (striatum and pallidum), then to thalamus and back to cortex. Similar loops are seen in the songbird: the cortical analogue nucleus LMAN projects to Area X, the striatal components of which project to the thalamic nucleus DLM, which projects back to LMAN. Striatal components accounts for reward basing learning and reinforcement learning. The neuron types and its functionality are exactly comparable in Area X of birds to basal ganglia in humans as shown (in Fig.6). The close anatomical similarity motivates us to learn the song bird brain in more detail because with this we can finally achieve some significant understanding of the speech learning in humans and treat many speech related disorders with higher precision.

Figure 6. Comparison of mammalian and avian basal ganglia–forebrain circuitry.

### References

Brainard, M. S. and A. J. Doupe (2000). "Auditory feedback in learning and maintenance of vocal behaviour." Nat Rev Neurosci 1(1): 31-40.

Dembo, A. and T. Kailath (1990). "Model-free distributed learning." IEEE Trans Neural Netw 1(1): 58-70.

Doupe, A. J. (1993). "A neural circuit specialized for vocal learning." Curr Opin Neurobiol 3(1): 104-111.

Farries, M. A. (2004). "The avian song system in comparative perspective." Ann N Y Acad Sci 1016: 61-76.

Fee, M. S., A. A. Kozhevnikov and R. H. Hahnloser (2004). "Neural mechanisms of vocal sequence generation in the songbird." Ann N Y Acad Sci 1016: 153-170.Glaze, C. M. and T. W. Troyer (2007). "Behavioral measurements of a temporally precise motor code for birdsong." J Neurosci 27(29): 7631-7639.

Hahnloser, R. H., A. A. Kozhevnikov and M. S. Fee (2002). "An ultra-sparse code underlies the generation of neural sequences in a songbird." Nature 419(6902): 65-70.

Kozhevnikov, A. A. and M. S. Fee (2007). "Singing-related activity of identified HVC neurons in the zebra finch." J Neurophysiol 97(6): 4271-4283.

Leonardo, A. and M. S. Fee (2005). "Ensemble coding of vocal control in birdsong." J Neurosci 25(3): 652-661.

Seung, H. S. (2003). "Learning in spiking neural networks by reinforcement of stochastic synaptic transmission." Neuron 40(6): 1063-1073.

Suthers, R. A. and D. Margoliash (2002). "Motor control of birdsong." Curr Opin Neurobiol 12(6): 684-690.

Todorov, E. (2000). "Direct cortical control of muscle activation in voluntary arm movements: a model." Nat Neurosci 3(4): 391-398.

Vicario, D. S. and F. Nottebohm (1988). "Organization of the zebra finch song control system: I. Representation of syringeal muscles in the hypoglossal nucleus." J Comp Neurol 271(3): 346-354.

Yu, A. C. and D. Margoliash (1996). "Temporal hierarchical control of singing in birds." Science 273(5283): 1871-1875.

## Octopus

### Introduction

One of the most interesting non-primate is the octopus. The most interesting feature about this non-primate is its arm movement. In these invertebrates, the control of the arm is especially complex because the arm can be moved in any direction, with a virtually infinite number of degrees of freedom. In the octopus, the brain only has to send a command to the arm to do the action—the entire recipe of how to do it is embedded in the arm itself. Observations indicate that octopuses reduce the complexity of controlling their arms by keeping their arm movements to set, stereotypical patterns. To find out if octopus arms have minds of their own, the researchers cut off the nerves in an octopus arm from the other nerves in its body, including the brain. They then tickled and stimulated the skin on the arm. The arm behaved in an identical fashion to what it would in a healthy octopus. The implication is that the brain only has to send a single move command to the arm, and the arm will do the rest.

In this chapter we discuss in detail the sensory system of an octopus and focus on the sensory motor system in this non-primate.

#### Octopus - The intelligent non-primate

The Common Octopus, Octopus vulgaris.

Octopuses have two eyes and four pairs of arms, and they are bilaterally symmetric. An octopus has a hard beak, with its mouth at the center point of the arms. Octopuses have no internal or external skeleton (although some species have a vestigial remnant of a shell inside their mantle), allowing them to squeeze through tight places. Octopuses are among the most intelligent and behaviorally flexible of all invertebrates.

The most interesting feature of the octopuses is their arm movements. For goal directed arm movements, the nervous system in octopus generates a sequence of motor commands that brings the arm towards the target. Control of the arm is especially complex because the arm can be moved in any direction, with a virtually infinite number of degrees of freedom. The basic motor program for voluntary movement is embedded within the neural circuitry of the arm itself.[2]

### Arm Movements in Octopus

In the hierarchical organization in octopus, the brain only has to send a command to the arm to do the action. The entire recipe of how to do it is embedded in the arm itself. By the use of the arms octopus walks, seizes its pray, or rejects unwanted objects and also obtains a wide range of mechanical and chemical information about its immediate environment.

Octopus arms, unlike human arms, are not limited in their range of motion by elbow, wrist, and shoulder joints. To accomplish goals such as reaching for a meal or swimming, however, an octopus must be able to control its eight appendages. The octopus arm can move in any direction using virtually infinite degrees of freedom. This ability results from the densely packed flexible muscle fibers along the arm of the octopus.

Observations indicate that octopuses reduce the complexity of controlling their arms by keeping their arm movements to set, stereotypical patterns.[3] For example, the reaching movement always consists of a bend that propagates along the arm toward the tip. Since octopuses always use the same kind of movement to extend their arms, the commands that generate the pattern are stored in the arm itself, not in the central brain. Such a mechanism further reduces the complexity of controlling a flexible arm. These flexible arms are controlled by an elaborate peripheral nervous system containing 5 × 107 neurons distributed along each arm. 4 × 105 of these are motor neurons, which innervate the intrinsic muscles of the arm and locally control muscle action.

Whenever it is required, the nervous system in octopus generates a sequence of motor commands which in turn produces forces and corresponding velocities making the limb reach the target. The movements are simplified by the use of optimal trajectories made through vectorial summation and superposition of basic movements. This requires that the muscles are quite flexible.

### The Nervous System of the Arms

The eight arms of the octopus are elongated, tapering, muscular organs, projecting from the head and regularly arranged around the mouth. The inner surface of each arm bears a double row of suckers, each sucker alternating with that of the opposite row. There are about 300 suckers on each arm.[4]

The arms perform both motor and sensory functions. The nervous system in the arms of the octopus is represented by the nerve ganglia, subserving motor and inter-connecting functions. The peripheral nerve cells represent the sensory systems. There exists a close functional relationship between the nerve ganglia and the peripheral nerve cells.

#### General anatomy of the arm

The muscles of the arm can be divided into three separate groups, each having a certain degree of anatomical and functional independence:

1. Intrinsic muscles of the arm,
2. Intrinsic muscles of the suckers, and
3. Acetabulo-brachial muscles (connects the suckers to the arm muscles).

Each of these three groups of muscles comprises three muscle bundles at right angles to one another. Each bundle is innervated separately from the surrounding units and shows a remarkable autonomy.In spite of the absence of a bony or cartilaginous skeleton, octopus can produce arm movements using the contraction and relaxation of different muscles. Behaviorally, the longitudinal muscles shorten the arm and play major role in seizing objects carrying them to mouth, and the oblique and transverse muscles lengthen the arms and are used by octopus for rejecting unwanted objects.

Cross section of an octopus arm: The lateral roots innervate the intrinsic muscles, the ventral roots the suckers.

Six main nerve centers lie in the arm and are responsible for the performance of these sets of muscles. The axial nerve cord is by far the most important motor and integrative center of the arm. The eight cords one in each arm contains altogether 3.5 × 108 neurons. Each axial cord is linked by means of connective nerve bundles with five sets of more peripheral nerve centers, the four intramuscular nerve cords, lying among the intrinsic muscles of the arm, and the ganglia of the suckers, situated in the peduncle just beneath the acetabular cup of each sucker.

All these small peripheral nerves contain motor neurons and receive sensory fibers from deep muscle receptors which play the role of local reflex centers. The motor innervation of the muscles of the arm is thus provided not only by the motor neurons of the axial nerve cord, which receives pre-ganglionic fibers from the brain, but also by these more peripheral motor centers.

#### Sensory Nervous system

The arms contain a complex and extensive sensory system. Deep receptors in the three main muscle systems of the arms, provide the animal with a widespread sensory apparatus for collecting information from muscles. Many primary receptors lie in the epithelium covering the surface of the arm. The sucker, and particularly its rim, has the greatest number of these sensory cells, while the skin of the arm is rather less sensitive. Several tens of thousands of receptors lie in each sucker.

Three main morphological types of receptors are found in arms of an octopus. These are round cells, irregular multipolar cells, and tapered ciliated cells. All these elements send their processes centripetally towards the ganglia. The functional significance of these three types of receptors is still not very well known and can only be conjectured. It has been suggested that the round and multipolar receptors may record mechanical stimuli, while ciliated receptors are likely to be chemo-receptors.

The ciliated receptors do not send their axons directly to the ganglia but the axons meet encapsulated neurons lying underneath the epithelium and make synaptic contacts with the dendritic processes of these. This linkage helps in reduction of input between primary nerve cells. Round and multipolar receptors on the other hand send their axons directly to the ganglia where the motor neurons lie.

### Functioning of peripheral nervous system in arm movements

Behavioral experiments suggest that information regarding the movement of the muscles does not reach the learning centers of the brain, and morphological observations prove that the deep receptors send their axons to peripheral centers such as the ganglion of the sucker or the intramuscular nerve cords.[5] The information regarding the stretch or movement of the muscles is used in local reflexes only.

When the dorsal part of the axial nerve cord that contains the axonal tracts from the brain is stimulated by electrical signals, movements in entire arm are still noticed. The movements are triggered by the stimulation which is provided and is not directly driven by the stimuli coming from the brain. Thus, arm extensions are evoked by stimulation of the dorsal part of the axial nerve cord. In contrast, the stimulation of the muscles within the same area or the ganglionic part of the cord evokes only local muscular contractions. The implication is that the brain only has to send a single move command to the arm, and the arm will do the rest.

A dorsally oriented bend propagates along the arm causing the suckers to point in the direction of the movement. As the bend propagates, the part of the arm proximal to the bend remains extended. For further conformations that an octopus arm has a mind of its own, the nerves in an octopus arm have been cut off from the other nerves in its body, including the brain. Movements resembling normal arm extensions were initiated in amputated arms by electrical stimulation of the nerve cord or by tactile stimulation of the skin or suckers.

It has been noted that the bend propagations are more readily initiated when a bend is created manually before stimulation. If the fully relaxed arm is stimulated, the initial movement is triggered by the stimuli, which follows the same bend propagation. The nervous system of the arm thus, not only drives local reflexes but controls complex movements involving the entire arm.

These evoked movements are almost kinematically identical to the movements of freely behaving octopus. When stimulated, a severed arm shows an active propagation of the muscle activity as in natural arm extensions. Movements evoked from similar initial arm postures result in similar paths, while different starting postures result in different final paths.

As the extensions evoked in denervated octopus arms are qualitatively and kinematically similar to natural arm extensions, an underlying motor program seems to be controlling the movements which are embedded in the neuromuscular system of the arm, which does not require central control.

## Fish

Fish are aquatic animals with great diversity. There are over 32’000 species of fish, making it the largest group of vertebrates.

The lateral line sensory organ shown on a shark.

Most fish possess highly developed sense organs. The eyes of most daylight dwelling fish are capable of color vision. Some can even see ultra violet light. Fish also have a very good sense of smell. Trout for example have special holes called “nares” in their head that they use to register tiny amounts of chemicals in the water. Migrating salmon coming from the ocean use this sense to find their way back to their home streams, because they remember what they smell like. Especially ground dwelling fish have a very strong tactile sense in their lips and barbels. Their taste buds are also located there. They use these senses to search for food on the ground and in murky waters.

Fish also have a lateral line system, also known as the lateralis system. It is a system of tactile sense organs located in the head and along both sides of the body. It is used to detect movement and vibration in the surrounding water.

### Function

Fish use the lateral line sense organ to sense prey and predators, changes in the current and its orientation and they use it to avoid collision in schooling.

Coombs et al. have shown [1] that the lateral line sensory organ is necessary for fish to detect their prey and orient towards it. The fish detect and orient themselves towards movements created by prey or a vibrating metal sphere even when they are blinded. When signal transduction in the lateral lines is inhibited by cobalt chloride application, the ability to target the prey is greatly diminished.

The dependency of fish on the lateral line organ to avoid collisions in schooling fish was demonstrated by Pitcher et al. in 1976, where they show that optically blinded fish can swim in a school of fish, while those with a disabled lateral line organ cannot [2].

### Anatomy

The lateral lines are visible as two faint lines that run along either side of the fish body, from its head to its tail. They are made up of a series of mechanoreceptor cells called neuromasts. These are either located on the surface of the skin or are, more frequently, embedded within the lateral line canal. The lateral line canal is a mucus filled structure that lies just beneath the skin and transduces the external water displacement through openings from the outside to the neuromasts on the inside. The neuromasts themselves are made up of sensory cells with fine hair cells that are encapsulated by a cylindrical gelatinous cupula. These reach either directly into the open water (common in deep sea fish) or into the lymph fluid of the lateral line canal. The changing water pressures bend the cupula, and in turn the hair cells inside. Similar to the hair cells in all vertebrate ears, a deflection towards the shorter cilia leads to a hyperpolarization (decrease of firing rate) and a deflection in the opposite direction leads to depolarization (increase of firing rate) of the sensory cells. Therefore the pressure information is transduced to digital information using rate coding that is then passed along the lateral line nerve to the brain. By integrating many neuromasts through their afferent and efferent connections, complex circuits can be formed. This can make them respond to different stimulation frequencies and consequently coding for different parameters, like acceleration or velocity [3].

Some scales of the lateral line (center) of a Rutilus rutilus

In sharks and rays, some neuromasts have undergone an interesting evolution. They have evolved into electroreceptors called ampullae of Lorenzini. They are mostly concentrated around the head of the fish and can detect a change of electrical stimuli as small as 0.01 microvolt [4]. With this sensitive instrument these fish are able to detect tiny electrical potentials generated by muscle contractions and can thus find their prey over large distances, in murky waters or even hidden under the sand. It has been suggested that sharks also use this sense for migration and orientation, since the ampullae of Lorenzini are sensitive enough to detect the earth’s electromagnetic field.

### Convergent Evolution

Cephalopods:

Cephalopods such as squids, octopuses and cuttlefish have lines of ciliated epidermal cells on head and arms that resemble the lateral lines of fish. Electrophysiological recordings from these lines in the common cuttlefish (Sepia officinalis) and the brief squid (Lolliguncula brevis) have identified them as an invertebrate analogue to the mechanoreceptive lateral lines of fish and aquatic amphibians [5].

Crustaceans:

Another convergence to the fish lateral line is found in some crustaceans. Contrary to fish, they don’t have the mechanosensory cells on their body, but have them spaced at regular intervals on long trailing antennae. These are held parallel to the body. This forms two ‘lateral lines’ parallel to the body that have similar properties to those of fish lateral lines and are mechanically independent of the body [6].

Mammals:

In aquatic manatees the postcranial body bears tactile hairs. They resemble the mechanosensory hairs of naked mole rats. This arrangement of hair has been compared to the fish lateral line and complement the poor visual capacities of the manatees. Similarly, the whiskers of harbor seals are known to detect minute water movements and serve as a hydrodynamic receptor system. This system is far less sensitive than the fish equivalent. [7]

## Flies

### Introduction

Halteres of the Crane fly

Halteres are sensory organs present in many flying insects. Widely thought to be an evolutionary modifcation of the rear pair of wings on such insects, halteres provide gyroscopic sensory data, vitally important for flight. Although the fly has other relevant systems to aid in flight, the visual system of the fly is too slow to allow for rapid maneuvers. Additionally, to be able to fly adeptly in low light conditions, a requirement to avoid predation, such a sensory system is necessary. Indeed, without halteres, flies are incapable of sustained, controlled flight. Since the 18th century, scientists have been aware of the role halteres play in flight, but it was only recently that the mechanisms by which they operate have been better explored. [6] [7]

### Anatomy

The haltere evolved from the rearmost of two pairs of wings. While the first has maintained its usage for flight, the posterior pair has lost its flight functions and has adopted a slightly different shape. The haltere is visually comprised of three structural components: a knob-shaped end, a thin shaft, and a slightly wider base. The knob contains approximately 13 innervated hairs, while the base contains two chordotonal organs, each innervated by about 20-30 nerves. Chordotonal organs are sense organs thought to be solely responsive to extension, though they remain relatively unknown. The base is also covered by around 340 campaniform sensilla, which are small fibers which respond preferentially to compression in the direction in which they are elongated. Each of these fibers is also innervated. Relative to the stalk of the haltere, both the chordotonal organs and the campaniform sensilla have an orientation of approximately 45 degrees, which is optimal for measuring bending forces on the haltere. The halteres move contrary (anti-phase) to the wings during flight. The sensory components can be categorized into three groups [8]): those sensitive to vertical oscillations of the haltere, including the dorsal and ventral scapal plates, dorsal and ventral Hicks papillae (both the plates and papillae are subcategories of the aforementioned campaniform sensilla), and the small chordotonal organ. The basal plate (another manifestation of the sensilla) and the large chordotonal organ are sensitive to gyroscopic torque acting on the haltere, and there is also a population of undifferentiated papillae which are responsive to all strains acting on the base of the haltere. This provides an additional method for flies to distinguish between the direction of force being applied to the haltere.

#### Genetics

As Homeobox genes were being discovered and explored for the first time, it was found that the deletion or inactivation of the Hox gene Ultrabithorax (Ubx) causes the halteres to develop into a normal pair of wings. This was a very compelling early result as to the nature of Hox genes. Manipulations to the Antennapedia gene can similarly cause legs to become severely deformed, or can cause a set of legs to develop instead of antennae on the head.

### Function

The halteres function by detecting Coriolis forces, sensing the movement of air across the potentially rotating fly body. Studies have indicated that the angular velocity of the body is encoded by the Coriolis forces measured by the halteres [8]. Active halteres can recruit any neighboring units, influencing nearby muscles and causing dramatic changes in the flight dynamics. Halteres have been shown to have extremely fast response times, allowing these flight changes to be performed much more quickly than if the fly were to rely on its visual system. In order to distinguish between different rotational components, such as pitch and roll, the fly must be able to combine signals from the two halteres, which must not be coincident (coincident signals would diminish the ability of the fly to differentiate the rotational axes). The halteres are capable of contributing to image stabilization, as well as in-flight attitude control, which was established by numerous authors noting a reaction from the head and wings to inputs from the components of the rotation rate vector. contributions from halteres to head and neck movements have been noted, explaining their role in gaze stabilization. The fly therefore uses input from the halteres to establish where to fixate its gaze, an interesting integration of the two senses.

### Mathematics

Recordings have indicated that halteres are capable of responding to stimuli at the same (double-wingbeat) frequency as Coriolis forces, the proof of concept that allows further mathematical analysis of how these measurements can occur. The vector cross-product of the halteres' angular velocity and the rotation of the body provide the Coriolis force vector to the fly. This force is at the same frequency as the wingbeat in both the pitch and roll planes, and is doubly fast in the yaw plane. Halteres are capable of providing a rate damping signal to affect rotations. This is because the Coriolis force is proportional to the fly's own rotation rate. By measuring the Coriolis force, the halteres can send an appropriate signal to their affiliated muscles, allowing the fly to properly control its flight. The large amplitude of haltere motion allows for the calculation of the vertical and horizontal rates of rotation. Because of the large disparity in haltere movement between vertical and horizontal movement, Ω1, the vertical component of the rotation rate, generates a force of double the frequency of the horizontal component. It is widely thought that this twofold frequency difference is what allows the fly to distinguish between the vertical and horizontal components. If we assume that the haltere moves sinusoidally, a reasonably accurate approximation of its real-world behavior, the angular position γ can be modeled as: ${\displaystyle \gamma ={\frac {\pi }{2}}\sin(\omega t)}$ where ω is the haltere beat frequency, and the amplitude is 180, a close approximation to the real life range of motion. The body rotational velocities can be computed, given the known rates (the roll, pitch, and yaw components are labeled below with 1, 2, and 3, respectively) from the two halteres' (Ωb being the left and Ωc being the right haltere) reference frames, respective to the body of the fly with the following calculations [7]:

${\displaystyle W_{1}=-{\frac {\Omega _{b3}+\Omega _{c3}}{2\sin(\alpha )}}}$
${\displaystyle W_{2}={\frac {\Omega _{b3}-\Omega _{c3}}{2\cos(\alpha )}}}$

${\displaystyle W_{3}=-{\frac {\Omega _{b1}+\Omega _{c1}}{2}}}$

α represents the haltere angle of rotation from the body plane, and the Ω terms are, as mentioned, the angular velocity of the haltere with respect to the body. Knowing this, one could roughly simulate input to the halteres using the equation for forces on the end knob of a haltere:

${\displaystyle F=mg-ma_{i}-ma_{F}-m{\dot {\Omega _{i}}}\times r_{i}-m\Omega _{i}\times (\Omega _{i}\times r_{i})-2m\Omega _{i}\times v_{i}}$

m is the mass of the knob of the haltere, g is the acceleration due to gravity, ri, vi,} and ai are the position, velocity, and acceleration of the knob relative to the body of the fly in the i direction, aF is the fly's linear acceleration, and Ωi and Ώi are the angular velocity and acceleration components for the direction i, respectively, of the fly in space. The Coriolis force is simulated by the 2mΩ × vi term. Because the sensory signal generated is proportional to the forces exerted on the halteres, this would allow the haltere signal to be simulated. If attempting to reconcile the force equation with the rotational component equations, it is worthwhile to remember that the force equation must be calculated separately for both halteres.

## Butterflies

The sense of balance of butterflies sits at the base of the antennae.

Butterflies and moth keep their balance with Johnston's organ: this is an organ at the base of a butterfly's antennae, and is responsible for maintaining the butterfly's sense of balance and orientation, especially during flight.

## Johnston's organ

### Introduction

The perception of sound for some insects is important for mating behavior, e.g. Drosophila [9]. The ability of hearing in Insecta and Crustacea is given by chordotonal organs: mechanoreceptors, which respond to mechanical deformation [10]. These chordotonal organs are widely distributed throughout the insect’s body and differ in their function: proprioceptors are sensitive to forces generated by the insect itself and exteroreceptors to external forces. These receptors allow detection of sound via the vibrations of particles when sound is transmitted though a medium such as air or water. Far-field sounds refer to the phenomenon when air particles transmit the vibration as a pressure change over a long distance from the source. Near-field sounds refer to sound close to the source, where the velocity of the particles can move lightweight structures. Some insects have visible hearing organs such as the ears of noctuoid moths, whereas other insects lack a visible auditory organ, but are still able to register sound. In these insects the "Johnston's Organ" plays an important role for hearing.

### Johnston's organ

The Johnston’s Organ (JO) is a chordotonal organ present in most insects. Christopher Johnston was the first who described this organ in mosquitoes, thus the name Johnston’s Organs [11]. Quarterly Journal of Microscopical Science. 1855, Vols. s1-3, 10, pp. 97-102.. This organ is located at the stem of the insect’s antenna. It has developed the highest degree of complexity in the Diptera (two-wings), for which hearing is of particular importance [10]. The JO consists of organized base sensory units called scolopidia (SP). The number of scolopidia varies among the different animals. JO has various mechanosensory functions, such as detection of touch, gravity, wind and sound, for example in honeybees JO (≈ 300 SPs) is responsible to detect sound coming from another “dancing” honeybee [12]. In male mosquitoes (≈ 7000 SPs) JO is used to detect and locate female flight sound for mating behavior [13]. . The antenna of these insects is specialized to capture near-field sound. It acts as a physical mechanotransducer.

#### Anatomy of the Johnston’s Organ

A typical insect antenna has three basic segments: the scape (base), the pedicel (stem) and the flagellum [14]. Some insects have a bristle at the third segment called an arista. Figure 1 shows the Drosophila antenna. For the Drosophila the antenna segment a3 fits loosely into the sockets on segment a2 and can rotate when sound energy is absorbed [15]. This leads to stretching or compression of JO neurons of the scolopidia. In Diptera the JO scolopidia are located in the second antennal segment a2 the pedicel (Yack, 2004). JO is not only associated with sound perception (exteroreceptor), it can also function as a proprioceptors giving information on the orientation and position of the flagellum relative to the pedicel [16].

Figure 1: Left: Frontal view of the Drosophila antenna. The scolopidia in the second segment (a2, pedicel) with their neurons are illustrated. Sound energy absorption leads to vibration of the arista and rotation of the third segment a3. The rotation leads to deformation of the scolopidia, leading to activation or deactivation. Right: The antenna located on the head of the Drosophila is shown. (adapted from [15]).

Structure of a Scolopedia

A scolopidia is the base sensory unit of the JO. A scolopidia comprises four cell types [10]: (1) one or more bipolar sensory cell neurons, each with a distal dendrite; (2) a scolopale cell enveloping the dendrite; (3) one or more attachment cells associated with the distal region of the scolopale cell; (4) one or more glial cells surrounding the proximal region of the sensory neuron cell body. The scolopale cell surrounds the sensory dendrite (cilium) and forms with this the scolopale lumen / receptor lymph cavity. The scolopale lumen is tightly sealed. The cavity is filled with a lymph, which is thought to have high potassium content and low sodium content, thus closely resembling the endolymph in the cochlea of mammals. Scolopidia are classified according different criteria. The cap cell produces an extracellular cap, which envelopes the cilia tips and connects them to the third antennal segment a3 [17].

Type 1 and Type 2 scolopidia differ by the type of ciliary segment in the sensory cell. In Type 1 the cilium is of uniform diameter, except for a distal dilation at around 2/3 along its length. The cilium inserts into a cap rather than into a tube. In Type 2 the ciliary segment has an increasing diameter into a distal dilation, which can be densely packed with microtubules. The distal part ends in a tube. Mononematic and amphinematic scolopidia differ by the extracellular structure associated with the scolopale cell and the dendritic cilium. Mononematic scolopidia have the dendritic tip inserted into a cap shape which is an electron dense structure. In amphinematic scolopidia the tip is enveloped by an electron-dense tube. Monodynal and Heterodynal scolopidia are distinguished in their number of sensory neurons. Monodynal scolopidia have a single sensory cell and heterodynal ones have more than one.

### JO studied in the fruit fly (Drosophila melanogaster)

The JO in Drosophila consists of an array of approximately 277 scolopidia located between the a2/a3 joint and the a2 cuticle (a type of an outer tissue layer) [18]. The scolopidia in Drosophila are mononematic [15]. Most are heterodynal and contain two or three neurons, thus the JO comprises around 480 neurons. It is the largest mechanosensory organ of the fruit fly [9]. Perception by JO of male Drosophila courtship songs (produced by their wings) makes females reduce locomotion and males to chase each other forming courtship chains [19]. JO is not only important to perceive sound, but also to gravity [20] and wind [21] sensing. Using GAL4 enhancer trap lines in the JO showed that JO neurons of flies can be categorized anatomically into five subgroups, A-E [18]. Each has a different target area of the antennal mechanosensory and motor centre (AMMC) in the brain (see Figure 2). Kamikouchi et al. showed that the different subgroups are specialized to distinct types of antennal movement [9]. Different groups are used for sound and gravity response.

#### Neural activities in the JO

To study JO neurons activities it is possible to observe intracellular calcium signals in the neurons caused by antenna movement [9]. Furthermore flies should be immobilized (e.g. by mounting on a coverslip and immobilizing the second antennal segment to prevent muscle-caused movements). The antenna can be actuated mechanically using an electrostatic force. The antenna receiver vibrates when sound energy is absorbed and deflects backwards and forwards when the Drosophila walks. Deflecting and vibrating the antenna yields different activity patterns in the JO neurons: deflecting the receiver backwards with a constant force gives negative signals in the anterior region and positive ones in the posterior region of the JO. Forward deflection produces the opposite behavior. Courtship songs (pulse song with a dominant frequency of ≈ 200Hz) evoke broadly distributed signals. The opposite patterns for the forward and backward deflection reflect the opposing arrangements of the JO neurons. Their dendrites connect to anatomically distinct sides of the pedicel: the anterior and posterior sides of the receiver. Deflecting the receiver forwards stretches the JO neurons in the anterior region and compresses neurons in the posterior one. From this is can be concluded that JO neurons are activated (i.e. depolarized) by stretch and deactivated (i.e. hyperpolarized) by compression.

#### Different JO neurons

A JO neuron usually targets only one zone of the AMMC, and neurons targeting the same zone are located in characteristic spatial regions within JO [18]. Similar projecting neurons are organized into concentric rings or paired clusters (see Figure 2A).

#### Vibration sensitive neurons for sound perception

A and B neurons (AB) were activated maximally by receiver vibration between 19 Hz and 952 Hz. This response was frequency dependent. Subgroup B showed larger response to low-frequency vibrations. Thus subgroup A is responsible for the high-frequency responses.

#### Deflection sensitive neurons for gravity and wind perception

C and E showed maximal activity for static receiver deflection. Thus these neurons provide information about the direction of a force. They have a larger displacement threshold of the arista than the neurons of AB [21]. Nevertheless CE neurons can respond to small displacement of the arista (e.g. gravitational force): gravity displaces the arista-tip by 1 µm (see S1 of [9]). They also respond to larger displacement caused by air-flow (e.g. wind) [21]. Zone C and E neurons showed distinct sensitivity to air flow direction, which causes deflection of the arista in different directions. Air flow applied to the front of the head resulted in strong activation in zone E and little activation in zone C. Air flow applied from the rear showed the opposite result. Air flow applied to the side of the head yielded in zone C in ipsilaterally activation and in zone E in contralaterally one. The different activation allows the Drosophila to sense from which direction the wind comes. It is not known whether the same subgroups-CE neurons mediate wind and gravity detection or if there are more sensitive CE neurons for gravity detection and less sensitive CE neurons for wind detection [9]. A proof that wild-type Drosophila melanogaster can perceive gravity is that the flies tend to fly upwards against the force vector of gravitation (negative gravitaxis) after getting shaken in a test tube. When the antennal aristae were ablated this negative gravitaxis behavior vanished, but not the phototaxis behavior (flies fly towards light source). Removing also the second segment, i.e. where the JO is located, the negative gravitaxis behavior came present again. This shows that when JO is lost, Drosophila can still perceive gravitational force through other organs, for example mechanoreceptors on neck or legs. These receptors were shown to be responsible for gravity sensing in other insect species [22].

#### Silencing specific neurons

It is possible to silence selectively subgroups of JO neurons using tetanus toxin combined with subgroup-specific GAL4 drivers and tubulin-GAL80. The latter is a temperature-sensitive GAL4 blocker. With this it could be confirmed that neurons of subgroup CE are responsible for gravitaxis behavior. Elimination of neurons of subgroups CE did not impair the ability of hearing [21]. Silencing subgroup B impaired the male’s response to courtship songs, whereas silencing groups CE or ACE did not [9]. Since subgroup A was found to be involved in hearing (see above) this result was unexpected. From different experiment, in which the sound-evoked compound action potential (sum of action potentials) were investigated the conclusion was drawn that subgroup A is required for nanometer-range receiver vibrations as imposed by faint songs of courting males.

Figure 2: A) left: Neurons of different subgroups A-E are illustrated in the JO. right: The corresponding target zones of the subroups are shown in the AMMC. B) Simplified circuitry of the auditory (zone AB) and deflection sensitive (zone CE) system. These two systems separated similar as in vertebrates. Neurons of zone A have target zones in the AMMC, vlprs and SOG. Vlpr stands for ventrolateral protocerebrum, SOG for suboesophageal ganglion, A for anterior, D for dorsal, M for medial. (adapted from Figure 2.10 of [15]).

Origin of difference of the subgroups

As mentioned above the anatomically different subgroups of JO neurons have different functions [9]. The neurons do attach to the same antennal receiver, but they differ in opposing connection sites on the receiver. Thus for e.g. forward deflection some neurons get stretched whereas others get compressed, which yields different response characteristics (opposing calcium signals). The difference for vibration- and deflection-sensitive neurons may come from distinct molecular machineries for transduction (i.e. adapting or non-adapting channels and NompC-dependent or not). Sound-sensitive neurons express the mechanotransducer channel NompC (no mechanoreceptor potential C, also known as TRPN1) channel whereas subgroups CE are independent of NompC [9]. In addition JO neurons of subgroup AB transduce dynamic receiver vibrations, but adapt fast for static receiver deflection (i.e. they respond phasically) [23]. Neurons of subgroups CE showed a sustained calcium signal response during the static deflection (i.e. they respond tonically). The two distinct behaviors show that there are transduction channels with distinct adaption characteristics, which is also known for the mammalian cochlea or mammalian skin (i.e. tonically activated Merkel calls and rapidly adapting Meissner’s corpuscles) [21].

#### Differences in gravitation and sound perception in the brain

Neurons of subgroups A and B target on one side zones of the primary auditory centre in the AMMC and on the other side the inferior part of ventrolateral protocerebrum (VLP) (see Figure 2B)). These zones show many commissural connections between themselves and with the VLP. For neurons of subgroups CE almost no commissural connection between the target zones were found, nor connections to the VLP. Neurons associated with the zones of subgroup CE descended or ascended from the thoracic ganglia. This difference in the AB and CE neurons projection reminds strongly on the separate vertebrate projection of the auditory and vestibular pathways in mammals [15].

### Johnston’s Organ in honeybees

Solitary bee (Anthidium florentinum): the Johnston's organs on the head are head are clearly visible.

The JO in bees is also located in the pedicel of the antenna and used to detect near field sounds [12]. In a hive some bees perform a waggle dance, which is believed to inform conspecifics about the distance, direction and profitability of a food source. Followers have to decode the message of the dance in the darkness of the hive, i.e. visual perception is not involved in this process. Perception of sound is a possible way to get the information of the dance. The sound of a dancing bee has a carrier frequency of about 260 Hz and is produced by wing vibrations. Bees have various mechanosensors, such as hairs on the cuticle or bristles on the eyes. Dreller et al. found that the mechanosensors in JO are responsible for sound perception in bees [12]. Nevertheless hair sensors could still be involved in detection of further sound-sources, when the amplitude is too low to vibrate the flagellum. Dreller et al. trained bees to associate sound signals with a sucrose reward. After the bees were trained some of the mechanosensors were abolished on different bees. Then the bee’s ability to associate the sound with the reward was tested again. Manipulating the JO yielded loss of the learnt skill. Training could be done with a frequency of 265 Hz, but also of 10 Hz, which shows that JO is also involved in low-frequency hearing. Bees with only one antenna made more mistakes, but were still better than bees that had ablated both antennas. Two JO in each antenna could help followers to calculate the direction of the dancing bee. Hearing could also be used by bees in other contexts, e.g. to keep a swarming colony together. The decoding of the waggle dance is not only done by auditory perception, but also or even more by electric field perception. JO in bees allows detection of electric fields [24]. If body parts are moved together, bees accumulate electric charge in their cuticle. Insects respond to electric fields, e.g. by a modified locomotion (Jackson, 2011). Surface charge is thought to play a role in pollination, because flowers are usually negatively charged and arriving insects have a positive surface charge [24]. This could help bees to take up pollen. By training bees to static and modulated electric fields, Greggers et al. showed that bees can perceive electric fields [24]. Dancing bees produce electric fields, which induce movements of the flagellum 10 times more strongly than the mechanical stimulus of wing vibrations alone. The vibrations of the flagellum in bees are monitored with JO, which responds to displacement amplitudes induced by oscillation of a charged wing. This was proven by recording compound action potential responses from JO axons during electric field stimulation. Electric field reception with JO does not work without antenna. Whether also other non-antennal mechanoreceptors are involved in electric field reception has not been excluded. The results of Greggers et al. suggest that electric fields (and with it JO) are relevant for social communication in bees.

### Importance of JO (and chordotonal organs in general) for research

Chordotonal organs, like JO, are only found in Insecta and Crustacea [10]. Chordotonal neurons are ciliated cells [25]. Genes that encode proteins needed for functional cilia are expressed in chordotonal neurons. Mutations in the human homologues result in genetic diseases. Knowledge of the mechanisms of ciliogenesis can help to understand and treat human diseases which are caused by defects in the formation or function of human cilia. This is because the process of controlling neuronal specification in insects and in vertebrates is based on highly conserved transcription factors, which is shown by the following example: Atonal (Ato), a proneural transcription factor, specifies chordotonal organ formation. The mouse orthologue Atoh1 is necessary for hair cell development in the cochlea. Mice which expressed a mutant Atoh1 phenotype, which are deaf, can be cured by the atonal gene of Drosophila. Studying chordotonal organs in insects can lead to more insights of mechanosensation and cilia construction. Drosophila is a versatile model to study the chordotonal organs [26]. The fruit fly is easy and inexpensive to culture, produces large numbers of embryos, can be genetically modified in numerous ways and has a short life cycle, which allows investigating several generations within a relative short time. In addition comes that most of the fundamental biological mechanisms and pathways that control development and survival are conserved across Drosophila and other species, such as humans.

## Spider´s Visual System

### Introduction

While the highly developed visual systems of some spider species have been subject to extensive studies since many decades, terms like animal intelligence or cognition were not usually used in the context of spider studies. Instead, spiders were traditionally portrayed as rather simple, instinct driven animals (Bristowe 1958, Savory 1928), processing visual input in pre-programmed patterns rather than actively interpreting the information received from their visual apparatus towards appropriate reactions. While Although this still seems to be the case in a majority of spiders, which primarily interact with the world through tactile sensation rather than by visual cues, some spider species have shown surprisingly intelligent use of their eyes. Considering its limited dimensions within the body, a spider´s optical apparatus and visual processing perform extremely well.[27] Recent research points towards a very sophisticated use of visual cues in a spider´s world when investigating topics such as the complex hunting schemes of the vision-guided jumping spiders (Salticidae) taking huge leaps of up to 30 times their own body length onto prey or a wolf spider´s (Lycosidae) ability to visually recognize asymmetries in potential mates. Even in the case of the night-active Cupiennius salei (Ctenidae), relying primarily on other sensory organs, or the ogre-faced Dinopis hunting at night by spinning small webs and throwing them at approaching prey, the visual system is still highly developed. Findings like these are not only fascinating but are also inspiring other scientific and engineering fields such as robotics and computer-guided image analysis.

### General structure of a spider´s visual system

A spider´s anatomy primarily consists of two major body segments, the prosoma and the opisthosoma, which are also known as the cephalothorax and abdomen, respectively. All extremities as well as the sensory organs including the eyes are located in the prosoma. Other than the visual system of arthropods featuring compound eyes, modern arachnid eyes are ocelli (simple eyes consisting of a lens covering a vitreous fluid-filled pit with a retina at the bottom), of which spiders have six or eight, characteristically arranged in three or four rows across the prosoma´s carapace. Overall, 99% of all spiders have eight eyes and of the remaining 1% almost all have six. Spiders with only six eyes lack the “principal eyes”, which are described in detail below.

The pairs of eyes are called anterior median eyes (AME), anterior lateral eyes (ALE), posterior median eyes (PME), and posterior lateral eyes (PLE). The large principal eyes facing forward are the anterior median eyes, which provide the highest spatial resolution to a spider, at the cost of a very narrow field of view. The smaller forward-facing eyes are the anterior lateral eyes with a moderate field of view and medium spatial resolution. The two posterior eye pairs are rather peripheral, secondary eyes with wide field of view. They are extremely sensitive and suitable for low-light conditions. Spiders use their secondary eyes for sensing motion, while their principal eyes allow shape and object recognition. In contrast to insect vision, a visually-based spider´s brain is almost completely devoted to vision, as it receives only the optic nerves and consists of only the optic ganglia and some association centers. The brain is apparently able to recognize object motion, but even more to also classify the counterpart into a potential mate, rival or prey by seeing legs (lines) at a particular angle to the body. Such stimulus will result in a spider displaying either courtship or threatening signs respectively.

### A Spider´s eyes

Although spider eyes may be described as “camera eyes”, they are very different in their details from the “camera eyes” of mammals or any other animals. In order to fit a high-resolution eye into such a small body, neither an insect´s compound eyes nor spherical eyes, as we humans have them, would solve the problem. The ocelli found in spiders are the optically better solution, as their resolution is not limited by refractive effects at the lens which would be the case with compound eyes. When replacing the eye of a spider by a compound eye of the same resolving power, it would simply not fit into the spider´s prosoma. By using ocelli, the spatial acuity of some spiders is more similar to that of a mammal than to that of an insect, with a huge size difference and only a few thousand photocells, e.g. in a jumping spider´s eye, as compared to more than 150 million photocells in the human retina.

#### Principal eyes

The anterior median eyes (AME), which are present in most spider species, are also called the principal eyes. Details about the principal eye´s structure and its components are illustrated in the figure below and are explained in the following by going through the AME of the jumping spider Portia (family Salticidae), which is famous for its high-spatial-acuity eyes and vision-guided behavior despite its very small body size of 4.5-9.5 mm.

When a light beam enters the principal eye it firstly passes a large corneal lens. This lens features a long focal length enabling it to magnify even distant objects. The combined field of view of the two principal eyes´ corneal lenses would cover about 90° in front of the salticid spider, however a retina with the desired acuity would be too large to fit inside a spider´s eye. The surprising solution is a small, elongated retina, which lies behind a long, narrow tube and a second lens (a concave pit) at its end. Such combination of a corneal lens (with a long focal length) and a long eye tube (magnifying the image from the corneal lens) resembles a telephoto system, making the pair of principal eyes similar to a pair of binoculars.

The salticid spider captures light beams successively on four retina layers of receptors, which lie behind each other (in contrast, the human retina is arranged in only one plane). This structure allows not only a larger number of photoreceptors in a confined area but also enables color vision, as the light is split into different colours (chromatic aberration) by the lens system. Different wavelengths of light thus come into focus at different distances, which correspond to the positions of the retina´s layers. While salticids discern green (layer 1 – ~580 nm, layer 2 – ~520-540 nm), blue (layer 3 – ~480-500 nm) and ultraviolet (layer 4 – ~360 nm) using their principal eyes, it is only the two rearmost layers (layers 1 and 2) which allow shape and form detection due to their close receptor spacing.

As in human eyes, there is a central region in layer 1 called the “fovea”, where the inter-receptor spacing was measured to about 1 μm. This was found to be optimal, as the telephoto optical system provides images precise enough to be sampled in this resolution, but any closer spacing would reduce the retina´s sampling quality due to quantum-level interference between adjacent receptors. Equipped with such eyes, Portia exceeds any insect by far when it comes to visual acuity: While the dragonfly Sympetrum striolatus has the highest acuity known for insects (0.4°), the acuity of Portia is ten times higher (0.04°) with much smaller eyes. The human eye with 0.007° acuity is only five times better than Portia´s. With such visual precision, Portia would be technically able to discriminate two objects which are 0.12 mm apart from a distance of 200 mm. The spatial acuity of other salticid eyes is usually not far behind that of Portia.[28][29][30]

#### Principal eye retina movements

Such spectacular visual abilities come at a price within small animals as the jumping spiders: The retina in each of Portia´s principal eyes has only 2-5° field of view, while its fovea even captures only 0.6° field of view. This results from the principal retina having elongated boomerang-like shapes which span about 20° vertically and only 1° horizontally, corresponding to about six receptor rows. This severe limitation is compensated by sweeping the eye tube over the whole image of the scene using eye muscles, of which jumping spiders have six. These are attached to the outside of the principal eye tube and allow the same three degrees of freedom – horizontal, vertical, rotation – as in human eyes. Principal retinae can move by as much as 50° horizontally and vertically and rotate about the optical axis (torsion) by a similar amount.

Spiders making sophisticated use of visual cues move their principal eyes´ retinae either spontaneously, in “saccades” fixating the fovea on a moving visual target (“tracking”), or by “scanning”, which serves presumably for pattern recognition. It seems today, that spiders scan a scene sequentially by moving the eye-tube in complex patterns, allowing it to process high amounts of visual information despite their very limited brain capacities.

The spontaneous retinal movements, so-called “microsaccades”, are a mechanism thought to prevent the photoreceptor cells of the anterior-median eyes from adapting to a motionless visual stimulus. Cupiennius spiders, which feature 4 eye muscles - two dorsal and two ventral ones – continuously perform such microsaccades of 2° to 4° in the dorso-median direction, lasting about 80 ms (when fixed to a holder). The 2-4° of microsaccadic movements match closely to Cupiennius´ angle of about 3° between the receptor cells, supporting the idea of its function preventing adaption. In contrast, retinal movements elicited by mechanical stimulation (directing an air puff onto the tarsus of the second walking leg) can be considerably larger than the spontaneous retinal movements, with deflections up to 15°. Such stimulus increases eye muscle activity from being spontaneously active at 12 ± 1 Hz at the resting level to 80 Hz with the air puff stimulation applied. Active retinal movement of the two principal eyes is however never activated simultaneously during such experiments and no correlation exists between the two eyes regarding their direction either. These two mechanisms, spontaneous microsaccades as well as active “peering” by active retinal movement, seemingly allow spiders to follow and analyze stationary visual targets efficiently using only their principal eyes without reinforcing the saccadic movements by body movements.

However, there is another factor influencing visual capacities of a spider´s eye, which is the problem of keeping objects at different distances in focus. In human eyes, this is solved by accommodation, i.e. changing the shape of the lens, but salticids take a different approach: the receptors in layer 1 of their retina are arranged on a “staircase” at different distances from the lens. Thus, the image of any object, whether a few centimeters or some meters in front of the eye, will be in focus on some part of the layer-1 staircase. Additionally, the salticid can swing the eye tubes side to side without moving the corneal lenses and will thus sweep the staircase of each retina across the image of the corneal lense, sequentially obtaining a sharp image of the object.

The resulting visual performance is impressive: Jumping spiders such as Portia focus accurately on an object at distances between 2 centimeters to infinity, being able to see up to about 75 centimeters in practice. The time needed to recognize objects is however relatively long (seemingly in the range of 10-20 s) because of the complex scanning process needed to capture high-quality images from such tiny eyes. Due to this limitation, it is very difficult for spiders such as Portia to identify much larger predators fast enough because of the predator´s size, making the small spider an easy prey for birds, frogs and other predators.[31][32]

#### Blurry vision for distance estimation

An unexpected finding recently surprised researchers, when it was shown that jumping spiders use a technique called blurry vision to estimate their distance to previously recognized prey before taking a jump. Where humans achieve depth perception using binocular vision and other animals do so by moving their heads around or measuring ultrasound responses, jumping spiders perform this task within their principal eyes. As in other jumping spider species, the principal eyes of Hasarius adansoni feature four retinal layers with the two bottom ones featuring photocells responding to green impulses. However, green light will only ever focus sharply on the bottom one, layer 1, due to its distance from the inner lens. Layer 2 would receive focused blue light, however these photoreceptor cells are not sensitive to blue and receive a fuzzy green image instead. Interestingly, the amount of blur depends on the distance of an object from the spider´s eye – the closer it is, the more out of focus it will appear on the second retina layer. At the same time, the first retina layer 1 always receives a sharp image due to its staircase structure. Jumping spiders are thus able to estimate depth using a single unmoving eye by comparing the images of the two bottom retina layers. This was confirmed by letting spiders jump at prey in an arena flooded with green light versus red light of equal brightness. Without the ability to use the green retina layers, jumping spiders would repeatedly fail to judge distance accurately and miss their jump.

#### Secondary eyes

In contrast to the principal eyes responsible for object analysis and discrimination, a spider´s secondary eyes act as motion detectors and therefore do not feature eye muscles to analyze a scene more extensively. Depending on their arrangement on the spider´s carapace, secondary eyes enable the animal to have panoramic vision detecting moving objects almost 360° around its body. The anterior and posterior lateral eyes (i.e. secondary eyes) only feature a single type of visual cells with a maximum spectral sensitivity for green colored light of ~535-540 nm wavelength. The number and arrangement of secondary eyes differs significantly between or even within different spider families, as does their structure: Large secondary eyes can contain several thousand rhabdomeres (the light-sensitive parts of the retina) and support hunters or nocturnal spiders with their high sensitivity to light, while small secondary eyes contain at most a few hundred rhabdomeres and only providing basic movement detection. Differently from the principal eyes which are everted (the rhabdomeres point towards the light), the secondary eyes of a spider are inverted, i.e. their rhabdomeres point away from the light, as is the case for vertebrates like the human eye. Spatial resolution of the secondary eyes e.g. in the extensively studied Cupiennius salei is greatest in horizontal direction, enabling the spider to analyse horizontal movements well even with the secondary eyes, while vertical movement may not be especially important when living in a “flat world”.

The reaction time of jumping spiders´ lateral eyes is comparably slow and amounts to 80-120 ms, measured with a 3°-sized (inter-receptor angle) square stimulus travelling past the animal´s eyes. The minimum stimulus travel distances, until the spider reacts, are 0.1° at a stimulus velocity of 1°/s, 1° at 9°/s and 2.5° at 27°/s. This means that a jumping spider´s visual system detects motion even if an object is travelling only a tenth of the secondary eyes´ inter-receptor angle at slow speed. If the stimulus gets even smaller to a size of only 0.5°, responds occur only after long delays, indicating that they lie at the spiders´ limit of perceivable motion.

Secondary eyes of (night-active) spiders usually feature a tapetum behind the rhabdomeres, which is a layer of crystals reflecting light back to the receptors to increase visual sensitivity. This allows night-hunting spiders to have eyes with an aperture as large as f/0.58 enabling them to capture visual information even in ultra-low-light conditions. Secondary eyes containing a tapetum thus easily reveal a spider´s location at night when illuminated e.g. by a flashlight.[33][34]

### Central nervous system and visual processing in the brain

As anywhere in neuroscience, we still know very little about a spider´s central nervous system (CNS), especially regarding its functioning in visually controlled behavior. Of all the spiders, the CNS of Cupiennius has been studied most extensively, focusing mainly on the CNS structure. As of today, only little is known about electrophysiological properties of central neurons in Cupiennius, and even less about other spiders in this regard.

The structure of a spider´s nervous system is closely related to its body´s subdivisions, but instead of being spread all over the body, the nervous tissue is enormously concentrated and centralized. The CNS is made up of two paired, rather simple nerve cell clusters (ganglia), which are connected to the spider´s muscles and sensory systems by nerves. The brain is formed by fusion of these ganglia in the head segments ahead of and behind the mouth and fills the prosoma largely with nervous tissue, while no ganglia exist in the abdomen. Looking at the spider´s brain, it receives direct inputs from only one sensory system, the eyes - unlike any insects and crustaceans. The eight optic nerves enter the brain from the front and their signals are processed in two optic lobes in the anterior region of the brain. When a spider´s behavior is especially dependent on vision, as in the case of the jumping spider, the optic ganglia contribute up to 31% of the brain´s volume, indicating the brain to be almost completely devoted to vision. This score still amounts to 20% for Cupiennius, whereas other spiders like Nephila and Ephebopus come in at only 2%.

The distinction between principal and secondary eyes persists in the brain. Both types of eyes have their own visual pathway with two separate neuropil regions fulfilling distinct tasks. Thus spiders evidently process the visual information provided by their two eye types in parallel, with the secondary eyes being specialized for detecting horizontal movement of objects and the principal eyes being used for the detection of shape and texture.

### Two visual systems in one brain

While principal and secondary eyesight seems to be distinct in spiders´ brains, surprising inter-relations between both visual systems in the brain are known as well. In visual experiments principal eye muscle activity of Cupiennius was measured while covering either its principal or secondary eyes. When stimulating the animals in a white arena with short sequences of moving black bars, the principal eyes moved involuntarily whenever a secondary eye detected motion within its visual field. This activity increase of the principal eye muscles, compared to no stimulation presented, would not change when covering the principal eyes with black paint, but would stop with the secondary eyes masked. Thus it is now clear, that only the input received from secondary eyes controls principal eye muscle activity. Also, a spider´s principal eyes do not seem to be involved in motion detection, which is only the secondary eyes´ responsibility.

Other experiments using dual-channel telemetric registration of the eye muscle activities of Cupiennius have shown that the spider actively peers into the walking direction: The ipsilateral retina of the principal eyes was measured to shift with respect to the walking direction before, during and after a turn, while the contralateral retina remained in its resting position. This happened independently from the actual light conditions, suggesting a “voluntary” peering initiated by the spider´s brain.

### Pattern recognition using principal eyes

Recognition of shape and form by jumping spiders is believed to be accomplished through a scanning process of the visual field, which consists of a complex set of rotations (torsional movements) and translations of the anterior-median eyes´ retinae. As described in the section “Principal eye retina movements”, a spider´s retinae are narrow and shaped like boomerangs, which can be matched with straight features by sweeping over the visual scene. When investigating a novel target, the eyes scan it in a stereotyped way: By moving slowly from side to side at speeds of 3-10° per second and rotating through ± 25°, horizontal and torsional retina movement allows the detection of differently positioned and rotated lines. This method can be understood as template matching where the template has elongated shape and produces a strong neural response whenever the retina matches a straight feature in the scene. This identifies a straight line with little or no further processing necessary.

A computer vision algorithm for straight line detection as an optimization problem (da Costa, da F. Costa) was inspired by the jumping spider´s visual system and uses the same approach of scanning a scene sequentially using template matching. While the well-known Hough Transform allows robust detection of straight visual features in an image, its efficiency is limited due to the necessity to calculate a good part or even the whole parameter space while searching for lines. In contrast the alternative approach used in salticid visual systems suggests searching the visual space by using a linear window, which allows adaptive searching schemes during the straight line search process without the need to systematically calculate the parameter space. Also, solving the straight line detection in such a way allows to understand it as an optimization problem, which makes efficient processing by computers possible. While it is necessary to find appropriate parameters controlling the annealing-based scanning experimentally, the approach taking a jumping spider´s path of straight line detection was proven to be very effective, especially with properly set parameters.[35]

### Visually-guided behavior

#### Discernment of visual targets

The ability of discerning between slightly different visual targets has been shown for Cupiennius salei, although this species relies mainly on its mechanosensory systems during prey catching or mating behavior. When presenting two targets at a distance of 2 m to the spider, its walking path depends on their visual appearance: Having to choose between two identical targets such as vertical bars, Cupiennius shows no preference. However the animal strongly prefers a vertical bar to a sloping bar or a V-shaped target.

The discrimination of different targets has been shown to be only possible with the principal eyes uncovered, while the spider is able to detect the targets using any of the eyes. This suggests that many spiders´ anterior-lateral (secondary) eyes are capable of much more than simply object movement detection. With all eyes covered, the spider exhibits totally undirected walking paths.

Placing Cupiennius in total darkness however results not only in undirected walks but also elicits a change of gait: Instead of using all eight legs the spider will only walk with six and employ the first legs as antennae, comparable to a blind person´s cane. In order to feel the surroundings the extended forelegs are moved up and down as well as sideways. This is specific to the first leg pair only, influenced solely by the visual input when the normal room light is switched to the invisible infrared light.

#### Vision-based decision making in jumping spiders

The behavior of jumping spiders after having detected movement with the eyes depends on three factors: the target´s size, speed and distance. If it has more than twice the spider´s size, the object is not approached and the spider tries to escape if it comes towards her. If the target has adequate size, its speed is visually analyzed using the secondary eyes. Fast moving targets with a speed of more than 4°/s are chased by jumping spiders, guided by her anterior-lateral eyes. Slower objects are carefully approached and analyzed with the anterior-median (i.e. principal) eyes to determine whether it is prey or another spider of the same species. This is seemingly achieved by applying the above described straight line detection, to find out whether a visual target features legs or not. While jumping spiders have shown to approach potential prey of appropriate characteristics as long as it moves, males are pickier in deciding whether their current counterpart might be a potential mate.

#### Potential mate detection

Experiments have shown that drawings of a central dot with leg-like appendages on the sides will result in courtship displays, suggesting that visual feature extraction is used by jumping spiders to detect the presence and orientation of linear structures in the target. Additionally, a spider´s behavior towards a considered conspecific spider depends on different factors such as sex and maturity of both involved spiders and whether it is mating time. Female wolf spiders, Schizocosa ocreata, even discern asymmetries in male secondary sexual characters when choosing their mate, possibly to avoid developmental instability in their offspring. Conspicuous tufts of bristles on a male´s forelegs, which are used for visual courtship signaling, appear to influence female mate choice and asymmetry of these body parts in consequence of leg loss and regeneration apparently reduces female receptivity to such male spiders.[36]

#### Secondary eye-guided hunting

A jumping spider´s stalking behavior when hunting insect prey is comparable to a cat stalking birds. If something moves within the visual field of the secondary eyes, they initiate a turn to bring the larger, forward-facing pair of principal eyes into position for classifying the object´s shape into mate, rival or prey. Even very small, low contrast dot stimuli moving at slow or fast speeds elicit such orientation behavior. Like Cupiennius, jumping spiders are also able to use their secondary eyes for more sophisticated tasks than just motion detection: Presenting visual prey cues to salticids with only visual information from the secondary eyes available and both primary eyes covered, results in the animal exhibiting complete hunting sequences. This suggests that the anterior lateral eyes of jumping spiders may be the most versatile components of their visual system. Besides detecting motion, the secondary eyes obviously also feature a spatial acuity which is good enough to direct complete visually-guided hunting sequences.

#### Prey “face recognition”

Visual cues also play an important role for jumping spiders (salticids) when discriminating between salticid and non-salticid prey using principal eyesight. To this end a salticid prey´s large principal eyes provide critical cues, to which the jumping spider Portia fimbriata reacts by exhibiting cryptic stalking tactics before attacking (walking very slowly with palps retracted and freezing when faced). This behavior is only used when identifying a prey as salticid. This was exploited in experiments presenting computer-rendered, realistic three-dimensional lures with modified principal eyes to Portia fimbriata. While intact virtual lures resulted in cryptic stalking, lures without or with smaller principal eyes than usual (as sketched in the figure on the right) elicited different behavior. Presenting virtual salticid prey with only one anterior-median eye or a regular lure with two enlarged secondary eyes elicited cryptic stalking behavior suggesting successful recognition of a salticid, while P. fimbriata froze less often when faced by a Cyclops-like lure (a single principal eye centered between the two secondary eyes). Lures with square-edged principal eyes were usually not classified as a salticid, indicating that the shape of the principal eyes´ edges are an important cue to identify fellow salticids.[37]

#### Jumping decisions from visual features

Spiders in the genus Phidippus have been tested within a study for their willingness to cross inhospitable open space by placing visual targets on the other side of a gap. It was found that whether the spider takes the risk of crossing open ground or not is mainly dependent on factors like distance to target, relative target size compared to distance and the target´s color and shape. In independent test runs, the spider moved to tall, distant targets equally often as to short, close targets, with both objects appearing equally sized on the spider´s retina. When giving the choice of moving to either white or green grass-like targets, the spiders consistently chose the green target irrespective of its contrast with the background, thus proving their ability to use color discernment in hunting situations.[38]

#### Identifying microhabitat traits by visual cues

Presented with manipulated real plants and photos of plants, Psecas chapoda (a bromeliad-dwelling salticid spider) is able to detect a favorable microhabitat by visually analyzing architectural features of the host plant´s leaves and rosette. By using black-and-white photos, any potential influence of other cues, such as color and smell, on host plant selection by the spider could be excluded during a study, leaving only shape and form as discerning characteristics. Even when having to decide solely from photographs, Psecas chapoda consistently preferred rosette-shaped plants (Agavaceae) with narrow and long leaves over differently looking plants, which proves that some spider species are able to evaluate and distinguish physical structure of microhabitats only on the basis of shape from visual cues of plant traits.[39]

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