Sensory Systems/Other Animals

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In Animals

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Birds: Neural Mechanism for Song Learning in Zebra Finches[edit]

Introduction[edit]

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.[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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 , where the synaptic activation determines the time course of conductance changes, and the plastic parameter determines their amplitude. Changes in are governed by the plasticity rule is given by

The positive parameter , called the learning rate, controls the overall amplitude of synaptic changes. The eligibility trace 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

Here 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 . 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[edit]

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[edit]

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.

Birds: Magnetoperception[edit]

Introduction[edit]

Sensory magneto-perception is defined as the sense that allows an organism to detect the Earth's magnetic field and to orient itself according to it. Magneto-perception is present throughout the Bacteria and Animalia kingdoms being observed, among others, in honey bees, salamanders, fishes and frogs. Here we will explain and review the current hypotheses on how birds use the Earth's magnetic field to navigate.

During the last decade many laboratories have focused their attention on how aves (i.e. birds) orient themselves. Twice each year, migratory birds travel thousands of kilometers from their breeding region to the overwintering sites and back, finding their way even across unfamiliar territories.

Studies done especially with robins (Erithacus rubecula) and pigeons (Columba livia) have shown that in addition to inclination, intensity and polarity of the geomagnetic field, birds relies on cues such as the sun or the star map to orient. However, the importance of each cue to avian orientation is still debated.

Robin (Erithacus rubecula): Birds' ability to detect the geomagnetic field and to use it as an orientation instrument have been studied in several species. Those on European robins and pigeons (see next image) have led to the most prominent - and best documented - results.
Pigeon (Columba livia)

While those studies have clarified the main aspects of birds' navigation, there are still many unanswered questions. Here we give a short introduction to magnetic orientation in animals, explaining the physics behind it, and discuss some of the principal hypotheses on how birds sense the geomagnetic field. The sensory structure, the neuronal circuitry and its mechanisms will be discussed.

Magnetic orientation[edit]

Since man cannot consciously sense the geomagnetic field, sensory magneto-reception might appear alien to our understanding. Nonetheless, the ability to sense magnetic fields is common to many animals; among them are mollusks, arthropods and members of all major group of vertebrates . The term magnetic orientation is regarded to the use these animals make of the information coming by a prominent magnetic field, the geomagnetic one, to orient themselves with respect to the Earth in migratory patterns. In this section the Earth's intrinsic magnetic field will be discussed, highlighting the two main classes of information that animals, and birds in particular, may obtain from it.

The geomagnetic field[edit]

In a first approximation, the Earth can be seen as a gigantic magnet dipole, with its poles situated close to the geographic, or rotational, poles. Although the magnetic north pole (Nm in the Figure) coincides today with the rotational north pole (Ng), there is no relationship between the two, since the latter is fixed, while the former can change in time.

An intuitive way to visualize magnetic fields is to consider their field lines. They define the direction of a vector field at different points. In a dipole (the arguably most elementary magnet) the northern and southern poles are the sources of the field. Because of the presented dipole approximation, the field lines associated with the geomagnetic field originate from the southern (magnetic, from now on) pole, run around the globe and reach the northern pole. A schematic visualization of the lines is depicted in Figure [fig:field-lines].

Geomagnetism
Schematic representation of the dipole approximation of the Earth. Only two specular field lines are shown. At the southern pole lines originate with an initial inclination of ; following a fairly distributed gradient, the inclination changes until becoming parallel to the Earth's surface at the magnetic equator and then increasing further up to , where the lines "enter" the Earth at the magnetic northern pole.

The most important aspect to note for the following discussion is that for this reason magnetic field lines point upward on the southern hemisphere, downward on the northern one, while running parallel to the Earth's surface at the magnetic equator (inclined, as the magnetic dipole, about 10 degrees with respect to the geographic equator), showing a fairly regular gradient. The intensity of the field is highest at the poles and lowest at the magnetic equator.

Of course irregularities in the Earth's surface slightly vary the real intensity of the field at different points and the corresponding inclination of its lines. Since these effects are very small, the geomagnetic field represents a reliable and omnipresent source of navigational information. In addition to the orientation of the magnetic field, which acts like a (biological) compass (as is done in human-built tools), the intensity of the field together with the inclination of the associated field line at different points may provide components of a navigational "map" indicating one's position on the globe [1].

Magnetic compass orientation[edit]

A magnetic field can be used as the main source of information to build a magnetic compass. That animals use a biological magnetic compass has been shown in several experiments, mostly involving European robins, Erithacus rubecula. A given migration pattern is in fact chosen by birds, that presents a magnetic characterization which is constant in time. Indeed recreating those fields and just inverting the poles led to analogous behaviors, inverted in direction [2].

What is most interesting is that while human-built compasses are polarity-based, featuring an orientation information based on the polarity (north/south) of the field lines, birds demonstrated to have an inclination-based compass. The gradient above described of inclinations of the filed lines, from the southern pole to the northern one, passing through the magnetic equator, can be used to detect the position of a given magnetic pole. Surprisingly, birds are not actually able to detect the full inclination of a field vector at a given point, but only of its axial component. The vertical component is inferred by simply realizing their up/down flying asset. This result has been obtained with smartly tuned magnetic fields where same axial component and different polarities led to the same outcome, birds not being able to tell the difference [3].

Another interesting aspect of birds' biological compass is that it is strictly tuned to only detect tight windows of certain magnetic fields intensities. Even more interestingly, this window may change but not in a shifting nor in an amplifying fashion. It was indeed observed that only already experienced (and orientation-wise efficient) fields are eligible as future recognizable windows Wiltschko, W. and Wiltschko, R. (1978). Further analysis of the magnetic compass of migratory birds.. Springer. 302-310. </ref>. .

Magnetic navigation[edit]

A biological compass may be enough to direct navigation, exactly as a human-built polarity-based compass is enough to orient. However, even the earliest experiments [4] showed that birds also utilize information on field intensity. Apparently discording theories have then developed throughout time suggesting that either one or the other approach was actually used by birds for navigation and orientation. Currently, it is generally accepted that the two approaches are both valid, one being prominent over the other under different conditions.

In fact, birds know by experience that in the northern hemisphere the geomagnetic field increases towards north. The difference in intensity between an encountered location and a known one lets them infer whether they are north or south of the known site. The first experimental results suggesting this ability of birds were obtained with pigeons, Columba livia f. domestica

[5] .

This is not the only way intensity can be used, though. It could also be used as a "sign-post" [1]. Birds, in fact, may show innate responses, both behavioral

Wiltschko, W. and Wiltschko, R. (1992). "Migratory orientation: magnetic compass orientation of garden warblers (sylvia borin) after a simulated crossing of the magnetic equator.". Ethology 91(1): 70-74. </ref>. and physiological , to locations that present a specific combination of field intensity and inclination of the corresponding field line. In an experiment thrush nightingales, L. luscinia, showed extremely rapid changes in weight that correlated to the geomagnetic conditions at which they originated.

Implications[edit]

In summary, inclination and intensity are both valid mechanisms that help birds in orientation and navigation. Being so different one from the other, it is clear that no single receptor or sensory system in general might be able to perceive, encode and elaborate the information these two elements represent. This is also the main reason why parallel research theories led to apparently different results when trying to solve the questions about birds' ability to orient themselves.

Magnetic sensory system[edit]

Lorenzini ampullae in fishes are electrically sensitive and specialized organs. Previous research investigated a corresponding specialized organ responsible for magnetic detection in birds. However, as it soon turned out birds' detection of the geomagnetic field is more complex than expected and does not rely exclusively on a specialized cell. The difficulty in identifying an underlying physiological mechanisms and a magneto-receptive organ or molecule has been a great obstacle in the study of the avian magnetic perception field.

There are mainly two strong hypotheses on magnetic sensing that are widely accepted in the field and strongly supported by data:

  1. Trigeminal iron-mineral based magneto-reception in the upper beak;
  2. Chemical light-dependent radical-pair based magneto-reception.

Very recently a third hypothesis has been proposed:

  1. Inner ear lagena based magneto-reception.

The review and description of these three hypotheses is the content of the following sections.

Iron-based magneto-reception[edit]

The first proposed hypothesis on how birds sense the geomagnetic field relies on iron rich cells which respond to magnetic fields, providing qualitative (directional) and quantitative (intensity) information. Iron rich cells have been found in bacteria [Blakemore, 1975] and bees [Gould et al., 1978], and have been detected in the upper beak of pigeons, finches, robins, warbler and chickens [Falkenberg et al., 2010, Fleissner et al., 2003]. Iron-mineral rich cells, localized in the sensory dendrites [Fleissner et al., 2003], are believed to exist in all birds.

There are two proposed theories about how birds sense the geomagnetic field using iron-based magneto-reception. The first one suggests that iron-based magneto-reception depends only on magnetite (). Magnetite clusters, according to the orientation of the external magnetic field, will attract or repel each other, deforming the dendrite membrane and possibly opening or closing ion channels. On the other hand, this theory has been proposed fifteen years ago, before the discovery of maghemite () platelets in the upper beak of birds.

The second proposed theory suggests that iron-based magneto-reception depends both on magnetite and maghemite. With X-ray and histology methodologies magnetite and maghemite have been detected in the upper beak of pigeons and have been both shown to be necessary for magneto-detection [Fleissner et al., 2007]. Magnetite forms micro-clusters that are attached to the cell membrane, while maghemite crystals are arranged in chains inside the dendrites, as shown in Figure [fig:magnetite-maghemite]. It is believed that maghemite becomes magnetized thereby enhancing the magnetic field of a cell. The magnetite cluster will then experience an attractive (or repulsive) force inducing their displacement and hence the opening of ion channels.

Maghemite
Schematic drawing that illustrates the localization of magnetite and maghemite in dendrites. Adapted from O'Neill, 2013

Histology has revealed that both magnetite and maghemite are present within the dendrites of the trigeminal nerve, especially in the branch that transmits sensory inputs form the upper beak to the brain. Besides this finding, researchers have shown the existence of three dendrite fields, each being responsible for coding a specific 3D orientation [Fleissner et al., 2007]. It is hypothesized that the magnetization of magnetite and maghemite caused by the geomagnetic field leads to the opening of ion channels. The information derived from magnetic fields is encoded into action potentials that reach the brain to be correctly interpreted.

There are many behavioral experiments that support this first hypothesis of avian magneto-detection. [Heyers et al., 2010] showed that changes in the magnetic field activate neurons in the trigeminal brainstem complex and that the trigeminal nerve is necessary for magneto-perception. They showed that ablation of the trigeminal nerve or removal of an external magnetic field led to reduced neuronal activation in PrV and SpV, two brain areas receiving primary inputs from the trigeminal nerve. In accordance with this study, disruption of trigeminal nerve or the attachment of a magnet to the upper beak area led to impairment of pigeons orientation [Mora et al., 2004]. Altogether, these findings strongly suggest a strict relation between the trigeminal nerve and the magnetic sensory system. However, more detailed findings are still needed.

The validity of the hypothesis here described, on how birds sense the Earth's magnetic field, has been debated by the recent finding that the believed-to-be iron-mineral structures in the trigeminal branch are in truth immune system cells called macrophages [Treiber et al., 2012]. Attempts by [Treiber et al., 2012] in finding supporting approaches to replicate electro-physiological data showing the presence of magnetite and maghemite in dendrites have failed.

The existence of iron-rich neurons in birds upper beak remains controversial. Nevertheless, the iron-based magnetic theory has not been discarded yet as many behavioral experiments, as the above cited, strongly suggest the involvement of the trigeminal nerve in magneto-perception.

Light-dependent radical-pair magneto-reception[edit]

A second hypothesis, also popular in the research field, aims at demonstrating how bird' magnetic orientation system is light dependent. This theory is supported by experiments in which avians' magnetic orientation showed an interesting dependance on a strict range of wavelengths [Wiltschko et al., 2010] . During other experiments in a cage, the utilization of full spectrum light led to birds disorientation [2]. The first research question arising from these experimental results is the origin of a structure in the birds' eye which is able to detect the geomagnetic field. How can visual and magnetic cues be separately processed is also a research topic.

The hypothesis on light-dependent magnetic sensory system states that the direction of a magnetic field is sensed by radical pair forming after photon absorption in photopigments located in the retina. Cryptochrome, a flavoprotein sensitive to blue light, has been suggested as the primary magneto-receptor in birds. Proving the validity of this theory, several cryptochrome family members have been found to be expressed in the retina of migrating birds. Moreover, their activity is highest during migratory behavior.

Light absorption leads to changes in the oxidation state in the cryptochrome pigment flavin adenine dinucleotide (FAD), creating an intermediate state in which the pigment, together with its electron transfer partner (tryptophan), form a radical pair. The electron spin of both radicals makes them sensitive to external magnetic fields. The different states of FAD oxidation are illustrated in the attached Figure. The homeostasis of FAD is extremely important as depending on its reduction state FAD activates different downstreaming signals.

Cryptochrome pigment photocycle: Light absorption reduces to the semiquinone and to the fully reduced . The cycle is closed by the re-oxidation of to by oxidising agents generated by oxidative stress. The magnetic field (MF, here) affects the speed of photoactivation from and the reoxidation step of . Adapted from Ritz et al., 2010

Other important consequences and research questions arising from this hypothesis are how information reaches the brain from the retina and where light-dependent magnetic information are processed in the bird's central nervous system. Ganglion cells are the only ones transmitting information between eyes and brain, therefore magnetic information must pass through them, independently of where in the eye the magnetic cells are active.

Magnetic information gathered in the retina is then transmitted via the thalamus to a forebrain region known as Cluster N, essential to magnetic field processing. Lesions in the Cluster N have been found to affect magnetic compass orientation but not stars and sun compass orientation abilities . This forebrain region is active at night, suggesting that magnetic orientation is a primary nocturnal navigational tool, while during the day other structures are more prominent.

Given that light-dependent magnetic information is detected in the retina, it is central to address how these signals can be separated from normal vision. It is hypothesized that these systems, although in close proximity, are orientated in different directions. Rod and cone cells are oriented approximately perpendicular to the retina, whereas magnetic receptor signals depends on the angular dependence between light, receptor and magnetic field. The maximal signal speed occurs when the receptor is parallel to the geometric field. The reason why birds are able to separate magnetic and visual information is that any magnetic generated pattern moves with half the speed of the surrounding landscape.

It was believed that the magnetic compass senses in birds were strongly lateralized towards the right eye. However recent findings show that cryptochromes are located in both eyes [Mouritsen et al., 2004], Cluster N activation is similar in both brain hemispheres [Zapka et al., 2009] and neuronal pathways between eye and Cluster N are symmetrical [Heyers er al., 2007]. These results then suggest that no lateralization is present.

Despite our far from complete understanding of radical pairs in cryptochromes, they appear to fit the purpose as magneto-receptors, from a theoretical point of view. However, there are still several unresolved issues. First of all, it is unclear which of the four cryptochromes found in the bird's retina is involved in migration, nor it is known if magnetic fields can be detected in vitro by cryptochrome proteins from migratory birds. Lastly, the existence of other brain regions apart from Cluster N, which could be important for signal processing of magnetic information, needs further investigation.

Inner ear lagena[edit]

Findings suggest the existence of a third possible magneto-receptor in birds, located in the inner ear lagena organs. The lagena, found in fishes, amphibians, reptiles, birds and monotremes (but not other mammals) is defined as the third otolith organ. In pigeons the lagena lies at the base of the basilar papilla, the avian equivalent of the organ of Corti, with receptors oriented in a sagittal plane [Wu and Dickman, 2011]. The lagena is similar to its neighboring structures, the utricle and the saccule. All three detect changes in head tilt relative to gravity, translational motion and linear acceleration by means of hair cells deflection. Proving the importance of the lagena for magneto-reception, pigeons which had their lagena removed or small magnetic interference inserted into the inner ear showed compromised navigational abilities [Harada, 2002].

It is believed that detection of the geomagnetic field in the lagena, as in the trigeminal nerve, also relies on ferrimagnetic compounds [Harada et al., 2001]. Hair cells are being speculated to contain iron rich cells that senses changes in the geomagnetic field. According to this speculation a recent study [Lauwers et al., 2013] has detected iron-rich structures in both type I and type II cells in the lagena. This in turn suggests that this iron-rich particles, under the influence of the geomagnetic field, can modify the transduction of input stimuli to the brain through the deflection of hair cells leading to opening or closing of ion channels.

Despite the finding of possible magneto-sensory cells in the inner lagena, the neural pathway activated during magneto-reception is still unknown. A study has been carried out with the c-Fos transcription factor, a marker used to highlight activated neurons along the pattern of activation generated by a magnetic field. As expected, activation was detected in brain areas known to be involved in orientation and spatial memory and navigation function. Supporting the theory here discussed, much of these brain regions received information from the lagena receptor organs, while ablation of lagena led to reduced number of active neurons in those regions [Wu and Dickman, 2011].

Research issues with magnetic systems[edit]

The difficulties in identifying a magneto-receptive organ contribute to the delay in understanding how the magnetic system developed in birds and, consequently, very few is known about the molecular and genetic factors that determine this kind of sensory system.

Progress in understanding the magnetic sense has been hampered by:

  • The availability of only a small number of techniques that are adequate for analyzing animal behavioral response to magnetic fields. For instance, many studies are performed in bound anesthetized animals, in which the influence of anesthesia on perception is still debated.
  • The difficulty in achieving reproducible results. After the finding of
  • iron-rich cells in the upper beak of birds, many electrophysiological data were
  • replicated but led to different results questioning the validity of the proposed theory;
  • The difficulty in implementing and carrying out new theories that might be more powerful than the ones used today. The human difficulty to understand the avian magnetic perception impedes the development of new and more efficient methods to study avian geomagnetic perception.

Redundancy of the system[edit]

It is clearly accepted that birds' orientation relays on the geomagnetic field. Still, as we have seen, no unequivocally magnetic sensitive structure nor a valid explanation to how the brain receives and interpret magnetic field information has yet been found. All the three above discussed hypotheses are plausible and well supported by many behavioral experiments. However, for all of them there are still many open questions and contradictory findings.

Taking into account all the presented evidence, it is difficult to escape the conclusion that birds' magnetoperception does not rely on a single sensory receptor but that it profits from the integration of different ones, presumably one out of the others being more prominent in specific situations.

It is believed that the iron-based magneto-reception provides quantitative or polarity data, such as the intensity of the magnetic field, whereas the cryptochrome receptors detect directional information regarding the Earth's magnetic field. It has also been shown that the iron-based magneto-reception can also control directional behavior when the radical pair process is disrupted [Wiltschko et al., 2010].

Confirming this theory, a study showed that, under blue/green light, birds oriented using cryptochrome-based detection, whereas when green/yellow light was used birds used mainly magnetite based perception [Wiltschko et al., 2012]. Hence, the idea of a possible redundancy in magnetic sensory perception can be proposed, raising a new question on how these two sources of information might be integrated as a single one.

Additionally to the sensory redundancy, studies have also shown that birds orientation does not rely exclusively on the geomagnetic field but also depend on multiple external cues. A hierarchy determining the usage of different orientation strategies has been speculated and showed that the sun or star maps are prioritized over magneto-perception. However, it is believed that information from each system is processed and integrated giving a more detailed, complex and precise representation of the environment. The way none of the elements of this redundancy can be easily shown to be prominent with regard to the other ones shows how magneto-perception can be better explained on an evolutionary basis. Redundant systems are less suitable to outside interference and can overcome situations where one important factor cannot be used or is not fully available. The convergence of multi-sensory cues helps the brain determine precise direction and location information.

References[edit]

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  3. Wiltschko, W. and Wiltschko, R. (1972). "Magnetic compass of european robins.". Science 176 (4030): 62-64. 
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  5. Keeton, W. T.; Larkin, T. S., and Windsor, D. M. (1974). "Normal fluctuations in the earth’s magnetic field influence pigeon orientation.". Journal of comparative physiology 95(2): 95-103. 
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  • Gerta Fleissner, Elke Holtkamp-Rötzler, Marianne Hanzlik, Michael Winklhofer, Günther Fleissner, Nikolai Petersen, andWolfgang Wiltschko. Ultrastructural analysis of a putative magnetoreceptor in the beak of homing pigeons. J Comp Neurol, 458(4):350-60, Apr 2003. doi: 10.1002/cne.10579.
  • Gerta Fleissner, Branko Stahl, Peter Thalau, Gerald Falkenberg, and Günther Fleissner. A novel concept of fe-mineral-based magnetoreception: histological and physicochemical data from the upper beak of homing pigeons. Naturwissenschaften, 94(8):631-42, Aug 2007. doi: 10.1007/ s00114-007-0236-0.
  • Thord Fransson, Sven Jakobsson, Patrik Johansson, Cecilia Kullberg, Johan Lind, and Adrian Vallin. Bird migration: magnetic cues trigger extensive refuelling. Nature, 414(6859):35–36, 2001.
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  • Dominik Heyers, Manuela Zapka, Mara Hoffmeister, John Martin Wild, and Henrik Mouritsen. Magnetic field changes activate the trigeminal brainstem complex in a migratory bird. Proc Natl Acad Sci U S A, 107(20):9394-9, May 2010. doi: 10.1073/pnas.0907068107.
  • Mattias Lauwers, Paul Pichler, Nathaniel Bernard Edelman, Guenter Paul Resch, Lyubov Ushakova, Marion Claudia Salzer, Dominik Heyers, Martin Saunders, Jeremy Shaw, and David Anthony Keays. An iron-rich organelle in the cuticular plate of avian hair cells. Curr Biol, 23(10):924-9, May 2013. doi: 10.1016/j.cub.2013.04.025.
  • Cordula V Mora, Michael Davison, J Martin Wild, and Michael M Walker. Magnetoreception and its trigeminal mediation in the homing pigeon. Nature, 432(7016):508-11, Nov 2004. doi: 10.1038/nature03077.
  • Henrik Mouritsen, Ulrike Janssen-Bienhold, Miriam Liedvogel, Gesa Feenders, Julia Stalleicken, Petra Dirks, and Reto Weiler. Cryptochromes and neuronal-activity markers colocalize in the retina of migratory birds during magnetic orientation. Proc Natl Acad Sci U S A, 101(39): 14294-9, Sep 2004. doi: 10.1073/pnas.0405968101.
  • Paul O’Neill. Magnetoreception and baroreception in birds. Development, growth & differentiation, 55(1):188-197, 2013.
  • Thorsten Ritz, Margaret Ahmad, Henrik Mouritsen, Roswitha Wiltschko, and Wolfgang Wiltschko. Photoreceptor-based magnetoreception: optimal design of receptor molecules, cells, and neuronal processing. Journal of the Royal Society, Interface / the Royal Society, 7 Suppl 2:S135-S146, Apr 2010. ISSN 1742-5662. doi: 10.1098/rsif.2009.0456.focus. URL http://www.ncbi.nlm.nih. gov/pubmed/20129953.
  • Christoph Daniel Treiber, Marion Claudia Salzer, Johannes Riegler, Nathaniel Edelman, Cristina Sugar, Martin Breuss, Paul Pichler, Herve Cadiou, Martin Saunders, Mark Lythgoe, Jeremy Shaw, and David Anthony Keays. Clusters of iron-rich cells in the upper beak of pigeons are macrophages not magnetosensitive neurons. Nature, 484(7394):367-70, Apr 2012. doi: 10.1038/ nature11046.
  • Roswitha Wiltschko, Katrin Stapput, Peter Thalau, and Wolfgang Wiltschko. Directional orientation of birds by the magnetic field under different light conditions. J R Soc Interface, 7 Suppl 2: S163-77, Apr 2010. doi: 10.1098/rsif.2009.0367.focus.
  • Roswitha Wiltschko, Lars Dehe, Dennis Gehring, Peter Thalau, and Wolfgang Wiltschko. Interactions between the visual and the magnetoreception system: Different effects of bichromatic light regimes on the directional behavior of migratory birds. J Physiol Paris, Apr 2012. doi: 10.1016/j.jphysparis.2012.03.003.
  • Le-Qing Wu and J David Dickman. Magnetoreception in an avian brain in part mediated by inner ear lagena. Curr Biol, 21(5):418-23, Mar 2011. doi: 10.1016/j.cub.2011.01.058.
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Fish: Tactile Sensation with Lateral Line Organs[edit]

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[edit]

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[edit]

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

Sketch of the anatomy of the lateral line sensory system.

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[edit]

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]

Sharks: Electroception[edit]

Sharks are some of the most ancient animals on our planet (the earliest known sharks date back more than 420 million years ago). They belong to the elasmobranchii, a subclass of Chondrichthyes (cartilaginous fish) which further includes rays and skates. Among some other features, elasmobranchii are characterized by the fact that they have - in contrast to most other fish - no swim bladder. Another unusual feature is that they are able to perceive electric fields with organs called ampullae of Lorenzini (see 2. Ampullae of Lorenzini) [1]. The number of sensory nerves is comparable with the ones from the eye, ear, nose and lateral line. This sensory perception allows elasmobranchs to detect electric fields of prey, conspecifics and predators. To following paragraphs will only focus on sharks.

Sensory input[edit]

Electric fields can be generated by bioelectric activity in other fish or by induction during the movement of charges in earth’s magnetic field.

Bioelectric Fields[edit]

In the surrounding of fish, three kinds of electric fields, so called bioelectric fields, were detected by Kalmijn [2]:

  • dc fields up to 500 μV in the head and gill region and in proximity to wounds
  • low frequency ac fields (< 20Hz) up to 500 μV, strongest in the head and the gill region, synchronous with the respiratory movements
  • weak high frequency ac fields during trunk and tail muscle contractions

The low frequency ac fields were caused by the periodic fluctuations of the resistance ratio due to the respiratory movement in the existing dc field.

The bioelectric fields of 60 vertebrate and invertebrate were measured which proofs that these fields occur in many animals. In any cases the dc fields were independent of muscle activity and would be a reliable stimulus for sharks to detect their prey. Prey fishes or conspecifics generate a dipole field which can be estimated with following formula [3]:

ε_0 is the permeability coefficient, p ⃗ the dipole vector, r ̂= r ⃗/|r ⃗| a unit vector in direction r ⃗ and |r ⃗ | the distance to the dipole source.

In order to detect electric phenomena sharks can use their electroreceptors in an active (see 1.2. Induced electric potential) or passive mode [5]. In the passive mode the shark detects fields in its environment like the bioelectric fields of prey or geoelectric fields present in the sea water (see 1.2. Induced electric potential). Kalmijn reported that lemon sharks follow straight paths when crossing the wide bay between North and South Bimini in the Bahamas [7]. They could orient on the ambient electric field induced by ocean currents.

Induced electric potential[edit]

Due to earth’s magnetic field a stream of sea water or a shark can induce a dc field [4] (see Figure 1 and Figure 2).

Figure 1: The motion of a shark through earth’s magnetic field will induce an electric current which leads to a dorsoventral potential difference.

A particle with charge q experiences a Lorentz force F perpendicular to the magnetic field B, if it moves with a velocity v through the field:

Free charges in an object get deflected due to their movement through a magnetic field according to the formula above. This leads to a separation of positive and negative charges resulting in an induced electric field.

One liter of sea water contains roughly 35 g dissolved salts (mainly Na+ and Cl-) [22]. Water movements such as ocean currents lead therefore to a movement of electric charges. Positive and negative charges get deflected in opposite directions, which leads to a charge separation. An electric field is induced which is high enough to stimulate the electroreceptors of a shark. Fluids within fish contain many free ions such as Na+, K+, Ca2+, Cl- and HCO3-. By analogy with ocean currents, the movement of a shark itself through earth’s magnetic field induces an electric field.

Figure 2: The electric field induced by an ocean current through earth’s magnetic field.

If a shark uses his electroreceptors in the active mode, electric fields induced by their own activity such as motional induced fields are used [5]. Caray and Scharold [6] observed that migrating blue sharks keep a constant course in the ocean for several days. The only possibility to follow such a long straight path is the orientation on earth’s magnetic field. Sharks use the magnetic field in the active mode for steady compass headings, whereas the ambient electric field is used in the passive mode to orient to the flow of water [8]. The two operation modes guaranty a complete electromagnetic orientation system.

Ampullae of Lorenzini[edit]

Anatomy[edit]

The ampullae of Lorenzini are sensing organs to perceive electric fields, so called electroreceptors. They consist of a system of jelly filled, grouped canals [1]. One end of the canal forms a pore through the dermis and epidermis and can be seen as black dots on the skin of the shark (see Figure 3:Head of a tiger shark. The small black dots are the pores of the ampullae of Lorenzini). The other side of the canal ends in an ampulla, a group of bulges lined by the sensory epithelium (Figure 4). The ampullary nerve is a bundle of afferent nerves leaving each ampulla. There are no efferent nerves entering the ampulla. A group of ampullae is enclosed in capsules of tight connective tissue. The distribution pattern is specific for different species.

Figure 3: Head of a tiger shark. The small black dots are the pores of the ampullae of Lorenzini.

The sensory epithelium consists of pear shaped receptor cells, supporting cells and a basement membrane (see figure 5). The receptor cell reaches the lumen of the ampulla only at one point where the kinocilium is located. The supporting cells fill the space between the different receptor cells. The synapse which contacts nerve endings are placed on the bases of the receptor cells. This side is attached to the basement membrane. The inside walls of the jelly filled canals consist of two layers of flattened pavement epithelium. The cells in the inner layer are connected by tight junctions, which explain the high resistance. Since the resistance of the jelly is very low, the canals therefore act as excellent low-frequency cables [9]. Sharks are therefore only sensitive to dc field gradients or low frequency ac fields. The outside is made of two layers of well-orientated circular and one layer of longitudinal collagenous fibers.

Figure 4: The grouped jelly filled canals of the ampullae of Lorenzini ending in a capsule. an: ampullary nerve, ca: capsule, m: body muscles, sk: skin (epidermis and dermis)
Figure 5: The sensory epithelium of the ampullae of Lorenzini; bm: basement membrane, kc: kinocilium, mv: microvilli, n: nucleus, ne: nerve ending, rec: receptor cell, sc: supporting cell, syn: synapse, t: tight junction

Distribution over the head[edit]

The pores which form the beginning of the jelly-filled canals are predominantly found on the dorsal and ventral surface of the head. The canals point in many different directions. Kim [10] used the original data from Dijkgraaf and Kalmijn [11] to identify 15 ampullary clusters of the small-spotted catshark (see figure 6). 14 of them are symmetrically aligned pairwise on each side. One is located dorsal around the symmetry axis.

Stimulus Transduction[edit]

In rays the ampullary electroreceptors are mapped somatotopically [12], suggesting that sharks use a similar mapping. Different neurons are tuned to one particular orientation of the electric field. The curve firing rate versus the angle of the electric field line is bell-shaped with a maximum at one specific field direction. Therefore each neuron responds strongest to one field direction.

A single canal with an ampulla only answers to changes in the electric fields with a frequency in the range of 0.1-10Hz [13]. A stationary prey emits an electric field (see 1.1. Bioelectric fields) which quickly drops with distance. If the shark approaches the prey he therefore perceives a changing field [14]. The changing field induces a current in the jelly filled canals of the ampullae of Lorenzini which changes the electrical potential in the ampulla [23].The voltage is amplified in the ampulla due to ion-channel mediated interactions between the apical and the basal membrane [15]. The apical membrane is the side of the membrane which confines the lumen and the basal membrane forms the surface that is faced towards the outside of the cell. The ampullary epithelium can be regarded as a linear amplifier within a voltage < 100 μV. The voltage across the ampullary organ gets amplified according to following formula [15]:

R_a, R_b and R_c are the apical, basal and canal resistances. V_b and V_c are the voltages across the basal membrane and the ampullary organs. Due to the voltage dependent negative conductance of the ion channels in the apical membrane〖,R〗_a<0. Therefore V_b>V_c and the output voltage is amplified. The voltage across the basal membrane is a graded receptor potential which changes gradually with the physically adequate stimuli, the bioelectric field of prey for example [16]. The receptor potential is therefore an analogue representation of the received stimuli.

If the voltage in the ampullae is changed, the firing pattern of the afferent nerve is changed [15]. A quick voltage drop within the jelly leads to an increased firing rate whereas an increase leads to a decrease in the firing rate. The voltage gradient in the ampullae of Lorenzini and therefore the firing rate is maximal when the canal axis is parallel to the electric field lines [13]. Sharks are able to detect voltage gradients of 1-2 nV/cm [17].

Neural signal processing[edit]

Combination of the electric an olfactory sense[edit]

Most of the electric fields generated by prey or conspecifics are dipole like (see 1.1. Bioelectric fields) [10]. The electric field lines are curved and the dipole source is not predictable from a local electric field, which is perceived by sharks [10]. Not even the distance to the source can be estimated, since the intensity of the electric field is not a direct measurement of the distance to the dipole source. The electric field of the dipole adds as one over the third power of the distance to the potential difference in the ampullae of Lorenzini. The processing of the electric field information and the sensorimotor mechanism producing the approach style is still unexplained.

Since the electric field decays quickly with distance, the sharks are only able to detect the field of the prey fish, if they are relatively close [18]. More distant signals are detected by pressure (add wikibook link) and smell: for example sharks get attracted by an odor field of injured fish over large distances. The odor fields are easily distorted by local water currents and not suitable to identify the exact location of the wounded fish. If the shark is close enough his electric sense effectively identifies the location of the target fish even if it is burrowed in the sand.

Kalmijn [20] used electrodes to mimic a bioelectric field of a prey fish. The sharks bit only in the electrodes, although there was an odor source close to the electrode. The electrode triggered a feeding response of large dogfish sharks of about 90-120 cm from a distance of at least 40 cm. The electric gradients were around 5 nV/cm at this distance. The sharks must have detected the field from a greater distance than from the location where the attack was triggered. An attack from a large distance might not be the best strategy since it would alert the prey fish and enable an easy escape. The big advantage lies not in attacking distance but in the penetration power of the electric sense which allows detecting prey buried in sand.

Detection of the electric field direction[edit]

In order to be able to detect bioelectric fields of a prey, sharks might subtract, by analogy with other sensory modalities, the anticipated signals produced by environmental fields or fields induced by the movement through earth’s magnetic field [9]. An analysis of the instantaneous potential distribution over the skin or the changes in the field directions over time would be one possibility. The electric field is close to uniform at the distance where the feeding response is triggered and barely distinguishable from the noise in the receptor system. The attack algorithm proposed by Kalmijn (see figure 7) would allow sharks to detect the dipole produced by their prey without knowing the exact location: When the shark first perceives the electric field of prey he keeps a constant angle between his body axis and the local field direction. Every deviation from this angle is nullified by feedback. Following the electric field lines would ultimately guide the shark to the source of the dipole. This algorithm is insensitive to the angle of approach, polarity of the field, temporal changes in the strength or direction of the field and therefore the movement of a prey fish.

In proximity of the dipole the field gets more complicated and difficult to analyze. Sharks might completely ignore that part, since they bit at the original location of the dipole source, which was electrically moved away just after the attack has been initiated [20]. Additional cues from the field nonuniformity, such as curvature or gradient of the field lines could inform sharks to ignore the momentary field information. However in three dimensional situations or if the path of the prey fish is not confined to the plane containing the dipole the algorithm needs further information or leaves some uncertainty.

Distinction between passive and active mode[edit]

The question remains how sharks can distinguish between the ambient electric field (passive mode) and the field induced by their motion through earth’s magnetic field (active mode). Since the electroreceptors operate in a frequency range from less than 0.125 – 8 Hz Kalmijn [16] proposed a possible procedure: The shark might probe its magnetic orientation by temporal acceleration and explore the direction of an ambient field (prey or due to ocean currents) by transiently turning.

Contralateral inhibition[edit]

Kim [10] proposed that each lateral symmetric pair of ampullary clusters (see 2.1.1. Distribution over the head) has a contralateral inhibition to localize the dipole source. It can be modeled by taking the intensity difference between the cluster pairs. The shark may turn its head towards the direction of higher intensity to localize dipole source. The highest sensitivity of the ampullae of Lorenzini lies within a frequency range of 1-8 Hz and covers the normal period of the swaying head movement of a shark. The simulation results of Kim show that the larger the sweeping angle of the head swaying motion the better the direction of the electric field can be estimated since the swaying head cancels out noisy signals. The signal is easier distinguishable from noise. This supports the strategy for the detection of an ambient electric field proposed by Kalmijn [16].

References[edit]

[1] Richard W. Murray, The Ampullae of Lorenzini, Chapter 4 in Handbook of Sensory Physiology Vol. 3, Springer Verlag Berlin, 1974

[2] Adrianus Kalmijn, Bioelectric fields in sea water and the function of the ampullae of Lorenzini in elasmobranch fishes, 1972

[3] Jackson J.D., Classical Electrodynamics, 3rd ed., John Wiley and Sons, New York, 1999

[4] Michael Paulin, Electroreception and the Compass Sense of Sharks, 1995

[5] Adrianus Kalmijn, The Detection of Electric Fields from Inanimate and Animate Sources Other Than Electric Organs, Chapter 5 in Handbook of Sensory Physiology Vol. 3, Springer Verlag Berlin, 1974

[6] E. G. Carey and J.V. Scharold, Movements of blue sharks (Prionace glauca) in depth and course, 1990

[7] Adrianus Kalmijn, Theory of electromagnetic orientation: a further analysis, 1984

[8] Adrianus Kalmijn, Appendix in E. G. Carey and J.V. Scharold, Movements of blue sharks (Prionace glauca) in depth and course, 1990

[9] Adrianus Kalmijn, Detection of Weak Electric Fields, Chapter 6 in Sensory Biology of Aquatic Animals, Springer-Verlag New York Inc., 1988

[10] DaeEun Kim, Prey detection mechanism of elasmobranchs, 2007

[11] S. Dijkglcaaf and A. J. Kalmijn, Untersuchungen über die Funktion Der Lorenzinischen Ampullen an Haifischen, 1963

[12] Jeff Schweitzer, Functional organization of the electroreceptive midbrain in an elasmobranch (Platyrhinoidis triseriata), 1985

[13] R. W. Murray, The Response of the Ampullae of Lorenzini of Elasmobranchs to Electrical Stimulation, 1962

[14] Brandon R. Brown, Modeling an electrosensory landscape: behavioral and orphological optimization in elasmobranch prey capture, 2002

[15] Jin Lu and Harvey M. Fishman, Interaction of Apical and Basal Membrane Ion Channels Underlies Electroreception in Ampullary Epithelia of Skates, 1994

[16] Adrianus Kalmijn, Detection and processing of electromagnetic and near field acoustic signals in elasmobranch fishes, 2000

[17] Adrianus Kalmijn, Electric and Magnetic Field Detection in Elasmobranch Fishes, 1982

[18] Adrianus Kalmijn, The Electric Sense of Sharks and Rays, 1971

[19] Adrianus Kalmijn, Electric and Magnetic Field Detection in Elasmobranch Fishes, 1982

[20] Adrianus J. Kalmijn and Matthew B. Weinger, An Electrical Simulator of Moving Prey for the Study of Feeding Strategies in Sharks, Skates, and Rays, 1981

[21] http://en.wikipedia.org/wiki/Ampullae_of_Lorenzini, 22.07.2014

[22] http://en.wikipedia.org/wiki/Seawater, 11.08.2014

[23] R. Douglas Fields, The shark’s electric sense, 2007

Rodents: Somatosensory Perception of Whiskers[edit]

Introduction[edit]

Figure 1A. Overview of the whisker system in rats
Figure 1B. System level description of the ascending pathways from whiskers to barrel cortex.

The barrel Cortex is a specialized region in somatosensory cortex responsible for processing the tactile information from whiskers. As every other cortical region, the barrel cortex also preserves the columnar organization which plays a crucial role in information processing. Information from each whisker is represented in separate, discrete columns analogous to “barrels”, hence the name barrel cortex. Rodents use whiskers constantly to acquire sensory information from the environment. Given their nocturnal nature, tactile information carried by whisker forms the primary sensory signals to build a perceptual map of the environment. The whiskers on the snouts of mice and rats serve as arrays of highly sensitive detectors for acquiring tactile information as shown in Figure 1 A and B. By using their whiskers, rodents can build spatial representations of their environment, locate objects, and perform fine-grain texture discrimination. Somatosensory whisker-related processing is highly organized into stereotypical maps, which occupy a large portion of the rodent brain. During exploration and palpation of objects, the whiskers are under motor control, often executing rapid large-amplitude rhythmic sweeping movements, and this sensory system is therefore an attractive model for investigating active sensory processing and sensory-motor integration. In these animals, a large part of the neocortex is dedicated to the processing of information from the whiskers. Since rodents are nocturnal, visual information is relatively poor and they rely heavily on the tactile information from whiskers. Perhaps the most remarkable specialization of this sensory system is the primary somatosensory ‘‘barrel’’ cortex, where each whisker is represented by a discrete and well-defined structure in layer 4.

These layer 4 barrels are somatotopically arranged in an almost identical fashion to the layout of the whiskers on the snout i.e. bordering whiskers are represented in adjacent cortical areas [1]. Sensorimotor integration of whisker related activity leads to pattern discrimination and enables rodents to have a reliable map of the environment. This is an interesting model to study because rodents use whisker to “see” and this cross modality sensory information processing could help us to improve the life of humans, who are deprived of one sensory modality. Specifically, blind people can be trained to use somatosensory information to build a spatial map of the environment [2].


Pathways carrying whisker information to Barrel Cortex[edit]

Pathways carrying whisker information to Barrel Cortex
Figure 2. Schematic demonstrating the ascending pathway of rodent whisker-related sensorimotor system.



The tactile information from the whiskers on the snouts is carried through the trigeminal nerves, which terminate at the trigeminal nucleus as shown in Figure 2. The ascending pathway starts with the primary afferents in the trigeminal ganglion (TG) transducing whisker vibrations into neuronal signals, and projecting to the trigeminal brainstem complex (TN). The TN consists of the principal nucleus (PrV), and the spinal sub-nuclei (interpolarisSpVi; caudalisSpVc; the detailed connectivity of the oralis sub-nucleus is unknown and is omitted in the figure). The SpVi falls into a caudal and rostral part (SpVic and SpVir). The classical mono-whisker lemniscal pathway (lemniscal 1) originates in PrV barrelettes, and projects via VPM barreloid cores to primary somatosensory cortex (S1) barrel columns. A second lemniscal pathway originating from PrV has been recently discovered which carries multi-whisker signals via barreloid heads to septa (and dysgranular zone) of S1. The extra-lemniscal pathway originates in SPVic and carries multi-whisker signals via barreloid tails in VPM to the secondary somatosensory area. Finally, the parelemniscal pathway originates in SpVir and carries multi-whisker signals via POm to S1, S2, and primary motor area (M1). The different colours of connections indicate three principal pathways through which associative coupling between the sensorimotor cortical areas may be realized. Black indicates direct cortico-cotical connections. Blue shows cortico-thalamic cascades. Brown represents cortico-sub-cortical loops. Projections of S1 and S2 may open or close the lemniscal gate (i.e. gate signal flow through PrV) by modulating intrinsic TN circuitry.


Figure 3.Processing of whisker-related sensory information in barrel cortex. System level description of the pathways involved in the propagation of information from whiskers to cortex & columnar organization of the barrel cortex which receives information from single whisker.



The sensory neurons make excitatory glutamatergic synapses in the trigeminal nuclei of the brain stem. Trigemino-thalamic neurons in the principal trigeminal nucleus are organized into somatotopically arranged ‘‘barrelettes,’’ each receiving strong input from a single whisker as shown in (Figure 3). The principal trigeminal neurons project to the ventral posterior medial (VPM) nucleus of the thalamus, which is also somatotopically laid out into anatomical units termed ‘‘barreloids’’ VPM neurons respond rapidly and precisely to whisker deflection, with one ‘‘principal’’ whisker evoking stronger responses than all others. The axons of VPM neurons within individual barreloids project to the primary somatosensory neocortex forming discrete clusters in layer 4, which form the basis of the ‘‘barrel’’ map as shown in Figure 3.



Whisker information processing in Barrel Cortex with specialized local microcircuit[edit]

The deflection of a whisker is thought to open mechano-gated ion channels in nerve endings of sensory neurons innervating the hair follicle (although the molecular signalling machinery remains to be identified). The resulting depolarization evokes action potential firing in the sensory neurons of the infraorbital branch of the trigeminal nerve. The transduction through mechanical deformation is similar to the hair cells in the inner ear; in this case the contact of whiskers with the objects causes the mechano-gated ion channels to open. Cation-permeable ion channels let positively charged ions into the cells and causes depolarization, eventually leading to generation of action potentials. A single sensory neuron only fires action potentials to deflection of one specific whisker. The innervation of the hair follicle shows a diversity of nerve endings, which may be specialized for detecting different types of sensory input [3].

The layer 4 barrel map is arranged almost identically to the layout of the whiskers on the snout of the rodent. There are several recurrent connections in layer 4 and it sends axons to layer 2/3 neurons, which integrates information from other cortical regions like primary motor cortex. These intra-cortical and inter-cortical connections enable the rodents to achieve stimulus discrimination capabilities and to extract optimal information from the incoming tactile stimulus. Also, these projections play a crucial role in integrating somatosensory information with motor output. Information from whiskers is processed in the barrel cortex with specialized local microcircuits formed to extract optimal information about the environment. These cortical microcircuits are composed of excitatory and inhibitory neurons as shown in Figure 4.

Figure 4.Local Microcircuit in Barrel cortex. Left: schematic representation of the cortical layers (barrels within L4 in cyan ) with examples of typical dendritic morphologies of excitatory cortical neurons (in red , an L2 neuron; in violet , a spiny stellate L4 cell; in green , an L5B pyramidal neuron). Right: schematic representation of the main excitatory connections between cortical layers within a barrel column (black).


Learning whisker based object discrimination & texture differentiation[edit]

Rodents move their sensors to collect information, and these movements are guided by sensory input. When action sequences are required to achieve success in novel tasks, interactions between movement and sensation underlie motor control [4] and complex learned behaviours [5]. The motor cortex has important roles in learning motor skills [6-9], but its function in learning sensorimotor associations is unknown. The neural circuits underlying sensorimotor integration are beginning to be mapped. Different motor cortex layers harbour excitatory neurons with distinct inputs and projections [10-12]. Outputs to motor centres in the brain stem and spinal cord arise from pyramidal tract-type neurons in layer 5B (L5B). Within motor cortex, excitation descends from L2/3 to L5 [13, 14]. Input from somatosensory cortex impinges preferentially onto L2/3 neurons. L2/3 neurons [10] therefore directly link somatosensation and control of movements. In one of the recent studies [15], mice were trained head fixed in a vibrissa-based object-detection task while imaging populations of neurons [16]. Following a sound, a pole was moved to one of several target positions within reach of the whiskers (the ‘go’ stimulus) or to an out-of-reach position (the ‘no-go’ stimulus). Target and out-of-reach locations were arranged along the anterior–posterior axis; the out-of reach position was most anterior. Mice searched for the pole with one whisker row, the C row, and reported the pole as ‘present’ by licking, or ‘not present’ by withholding licking. Licking on go trials (hit) was rewarded with water, whereas licking on no-go trials (false alarm) was punished with a time-out during which the trial was stopped for 2 seconds. Trials without licking (no-go, correct rejection, go, and miss) were not rewarded or punished. All mice showed learning within the first two or three sessions. Performance reached expert levels after three to six training sessions. Learning the behavioural task was directly dependent on the motor related behaviour. Naive mice whisked occasionally in a manner unrelated to trail structure. Thus, object detection relies on a sequence of actions, linked by sensory cues. An auditory cue triggers whisking during the sampling period. Contact between whisker and object causes licking for a water reward during a response period. Silencing vM1 indicates that this task requires the motor cortex; with vM1 silenced, task-dependent whisking persisted, but was reduced in amplitude and repeatability, and task performance dropped.


Neural Correlates of Sensorimotor learning mechanism[edit]

Coding of touch in the motor cortex is consistent with direct input from vS1 to the imaged neurons. A model based on population coding of individual behavioural features also predicted motor behaviours. Accurate decoding of whisking amplitude, whisking set-point and lick rate suggests that vM1 controls these slowly varying motor parameters, as expected from previous motor cortex and neurophysiological experiments.


References[edit]

1 Feldmeyer D, Brecht M, Helmchen F, Petersen CCH, Poulet JFA, Staiger JF, Luhmann HJ, Schwarz C."Barrel cortex function" Progress in Neurobiology 2013, 103 : 3-27.

2 Lahav O, Mioduser D. "Multisensory virtual environment for supporting blind persons' acquisition of spatial cognitive mapping, orientation, and mobility skills." Academia.edu 2002.

3 Alloway KD. "Information processing streams in rodent barrel cortex: The differential functions of barrel and septal circuits." Cereb Cortex 2008, 18(5):979-989.

4 Scott SH. "Inconvenient truths about neural processing in primary motor cortex." The Journal of physiology 2008, 586(5):1217-1224.

5 Wolpert DM, Diedrichsen J, Flanagan JR. "Principles of sensorimotor learning." Nature reviews Neuroscience 2011, 12(12):739-751.

6 Wise SP, Moody SL, Blomstrom KJ, Mitz AR. "Changes in motor cortical activity during visuomotor adaptation." Experimental brain research Experimentelle Hirnforschung Experimentation cerebrale 1998, 121(3):285-299.

7 Rokni U, Richardson AG, Bizzi E, Seung HS. "Motor learning with unstable neural representations." Neuron 2007, 54(4):653-666.

8 Komiyama T, Sato TR, O'Connor DH, Zhang YX, Huber D, Hooks BM, Gabitto M, Svoboda K. "Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice." Nature 2010, 464(7292):1182-1186.

9 Hosp JA, Pekanovic A, Rioult-Pedotti MS, Luft AR. "Dopaminergic projections from midbrain to primary motor cortex mediate motor skill learning." The Journal of neuroscience : the official journal of the Society for Neuroscience 2011, 31(7):2481-2487.

10 Keller A. "Intrinsic synaptic organization of the motor cortex." Cereb Cortex 1993, 3(5):430-441.

11 Mao T, Kusefoglu D, Hooks BM, Huber D, Petreanu L, Svoboda K. "Long-range neuronal circuits underlying the interaction between sensory and motor cortex." Neuron 2011, 72(1):111-123.

12 Hooks BM, Hires SA, Zhang YX, Huber D, Petreanu L, Svoboda K, Shepherd GM. "Laminar analysis of excitatory local circuits in vibrissal motor and sensory cortical areas." PLoS biology 2011, 9(1):e1000572.

13 Anderson CT, Sheets PL, Kiritani T, Shepherd GM. "Sublayer-specific microcircuits of corticospinal and corticostriatal neurons in motor cortex." Nature neuroscience 2010, 13(6):739-744.

14 Kaneko T, Cho R, Li Y, Nomura S, Mizuno N. "Predominant information transfer from layer III pyramidal neurons to corticospinal neurons." The Journal of comparative neurology 2000, 423(1):52-65.

15 O'Connor DH, Clack NG, Huber D, Komiyama T, Myers EW, Svoboda K. "Vibrissa-based object localization in head-fixed mice." The Journal of neuroscience : the official journal of the Society for Neuroscience 2010, 30(5):1947-1967.

16 O'Connor DH, Peron SP, Huber D, Svoboda K. "Neural activity in barrel cortex underlying vibrissa-based object localization in mice." Neuron 2010, 67(6):1048-1061.

17 Shaner NC, Campbell RE, Steinbach PA, Giepmans BN, Palmer AE, Tsien RY. "Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein." Nature biotechnology 2004, 22(12):1567-1572.

18 Tian L, Hires SA, Mao T, Huber D, Chiappe ME, Chalasani SH, Petreanu L, Akerboom J, McKinney SA, Schreiter ER. "Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators." Nature methods 2009, 6(12):875-881.

Snakes: Sensing of Infrared Radiation[edit]

Introduction[edit]

Location of pit organs. Above: python, below: crotalus.

When seeing or sometimes even thinking of snakes, many people feel uncomfortable or even scared. There is a reason that they are considered being mythical. Snakes are different when compared to other animals: they do not have legs, they are long and move elegantly and without a noise, some of them are venomous and they steadily use their forked tongue to smell. Some of them are fast and effective killers even by night. Something that definitely makes them special is their „sixth sense“: the ability to detect infrared radiation. Similar to night viewers, snakes are capable of detecting heat changes in their surroundings and thus obtaining a detailed picture of it. There are at least two different groups of snakes which have separately developed this ability: in the first are the pit vipers, and in the second boas and pythons (those two are often classified into one group called “boids”). However, snakes are not the only species which have evolved this sense: vampire bats and some groups of insects have also developed it. Even at night pit vipers, boas and pythons can make out rodents due to the heat they emit. It can be detected by a sensory system that allows them to „see“ electromagnetic radiation with long wavelengths ranging from 750 nm to 1 mm. The organs which make that possible are called “pit organs”, and are located under their eyes, inside two hollows of the maxilla bone. They are immensely sensitive as they can even detect changes in temperature of as little as 0.003K.

Anatomy of Sensing Organ[edit]

Structure of the pit organ. The width of the hollow is half the size of the membrane that seperates the two air-filled chambers. The dendrites of the trigenimal lie in the membrane's back. The infrared beams are projected like in a pinhole camera.

The infrared-sensing organs of vipers and boids are similar in their physiological structure but differ in their number, location and morphology. The anatomy is quite simple and will be explained in the example of the crotalus, a venomous pit viper found only in the Americas from southern Canada to northern Argentina. It consists of a hollow space that is seperated into two air-filled chambers by a thin membrane of the thickness of 0.01 mm. It is filled with sensory cells of the trigenimal nerve (TNM). Roughly 7000 in number, they transduce the heat through heat sensitive ion channels, and increase their firing rate when a positive change in temperature occurs and decrease in the opposite case. They are very sensitive due to the spatial proximity of these thermoreceptors to the outside and also because of the air-filled chamber that lies underneath. This air-filled chamber works as an insulator in separating tissues that would otherwise quickly exchange heat energy. Thus, the absorbed thermal energy is used exclusively by the sensory system and is not lost to lower-lying tissues. This simple but sophisticated anatomy is the reason for the unique sensitivity of the pit organs. The pit organs’s physique allows even the detection of the radiation’s direction. The external opening is roughly half as large as the membrane. Thus, the whole organ works according to the optics of a pinhole camera: the position of the irradiated spot provides information about the object’s location. The heat itself is detected by the activation of heat sensitive ion channels called TRPA1. In other animals these channels also exist but have other functions like detecting chemical irritants or cold. Pit vipers and boids seem to have evolved the infrared-sensing independently. Since the heat sensitive ion channels have different thermal thresholds in different snakes, the temperature sensitivity differs among the snakes. Crotalus have the most sensitive channels. Snakes that are not able to detect infrared radiation also possess those channels, but their thermal threshold is too high to detect infrared radiation.

Brain’s Anatomy[edit]

Every sensory organ has a dedicated brain region to process the collected information. Snakes evaluate infrared sensory input from the pit organs in the nucleus of the lateral descending trigeminal tract (“LTTD”), a unique region in their metencephalon which has not been found in other animals. The LTTD is linked to the tectum opticum via the reticularis caloris (“RC”). Its function is still unknown. In the tectum opticum visual and infrared stimuli are connected, in order to provide a detailed idea of the animal’s surrounding.

Physiology[edit]

Experiments have shown that the detection of heat targets must be quite accurate as snakes hit thermal sources with a low error even without the help of vision. Measurements have determined that the opening angle of an infrared beam falling onto the pit organ is 45 to 60 degrees. Depending on where the heat source is relatively to the snake, the beam hits the pit’s membrane on a different spot. The receptive field of the infrared sensing system on the tectum opticum is similarly represented as the visual receptive field. The front-end of the tectum opticum receives its input from the back part of the pit membrane and the retina, and thus processes stimuli from the front part of the visual field. Similarly, the back and the sides of the visual field are represented in the back part of the tectum opticum and the front part of the pit membrane and the retina. The receptive fields of the visual and infrared sensory systems overlap almost perfectly within the tectum opticum, such that the neurons there receive and process sensory information from two senses, from more or less the same direction. While crotalus only have two pit organs, the anatomy of the temperature sensors is much more complicated in boas and pythons. They possess 13 pit organs on each side of the head. Every one of those also works like a pinhole camera that reverses the picture. The information of the front part of the visual field is again processed in the front part of the tectum opticum but now, the receptive field of every pit organ is projected onto a different part of it. The front pit organs are represented in the front part of the tectum opticum and the back parts in the back. In addition, the receptive fields of the different pit organs overlap, and thus provide a more or less continuous projective field that matches the visual one. It is curious that the front part of every pit organ is projected to the back part of the receptive field in the tectum opticum, an organization that is quite complicated and unique. The tectum opticum contains six different kinds of neurons which fire for infrared and/or visual stimuli. Some cell types respond only if there is a visual and an infrared stimulus, while others respond for any kind of stimulus. There are cells that respond for one of the sensory input if it comes alone, but increases its firing rate for simultaneous input from both systems. The last group of cells works the other way around. Some of them respond strongly for visual stimuli and stop firing when stimuli from the pit organs also arrive or vice versa. What do snakes with pit organs need these different kinds of neurons for? The processing in their brain has to help the snakes with different tasks: first of all, the snake should be able to detect and locate stimuli. Second, they have to be identified and reacted to appropriately. The cells that respond to both visual and infrared stimuli independently from each other could be responsible for the first task. Cells that only respond if they get both stimuli at the same time could work as detectors for living, moving objects. Moreover, cells that stop firing as soon as the visual stimuli is completed with an infrared signal could be especially important for detecting the cool surrounding like leaves or trees. The interaction between the different types of cells are important for correctly identifying the stimuli. They are not only used for identifying warm-blooded prey, but also for identifying predators and the snake’s thermoregulation.

Common vampire bat. The infrared sensing organs are located in the nose.

Infrared Sensing in Vampire Bats[edit]

Vampire bats are the only mammals that are able to detect infrared radiation. To do so they have three hollows in their nose which contain the sensing organs. While they also use ion channels to detect heat, it is a different type of ion channels than in snakes. In other mammals and even everywhere in their own body except for the nose this type of molecule is responsible for sensing pain and burning. However, in the nose the threshold is much lower. The channel already detects changes in temperature from 29°C on. This allows vampire bats to locate heat sources at a distance of 20 cm and helps them to find blood-rich spots on their prey.

References[edit]

Newman, E.A., and Hartline, P.H. (1982) Infrared "vision" in snakes. Scientific American 246(3):116-127 (March).

Gracheva et. al.: Molecular Basis of Infrared Detection by Snakes. Nature. 2010 April 15; 464(7291): 1006-1011

Campbell et. Al.: Biological infrared Imaging and sensing. Micron 33 (2002) 211-225

Gracheva et. al.: Ganglion-specific splicing of TRPV1 underlies infrared sensation in vampire bats. Nature.476, 88-91 (04.08.2011)

Octopus: Sensorimotor System[edit]

Introduction[edit]

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[edit]

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.[1]

Arm Movements in Octopus[edit]

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.[2] 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[edit]

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.[3]

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[edit]

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[edit]

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[edit]

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.[4] 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.

References[edit]

  1. G. S. et al., Control of Octopus Arm Extension by a Peripheral Motor Program . Science 293, 1845, 2001.
  2. Y. Gutfreund, Organization of octopus arm movements: a model system for study- ing the control of flexible arms. Journal of Neuroscience 16, 7297, 1996.
  3. P. Graziadei, The anatomy of the nervous system of Octopus vulgaris, J. Z. Young. Clarendon, Oxford, 1971.
  4. M. J. Wells, The orientation of octopus. Ergeb. Biol. 26, 40-54, 1963.

Jellyfish: Visual System of Box Jellyfish[edit]

Introduction[edit]

Nearly all living organisms are capable of light sensing, that is, responding to electromagnetic radiation in the range of 300-800 nm. Studying visual systems is fascinating from the evolutionary point of view because animals which are very distant from each other on the tree of life seem to have developed surprisingly similar, sometimes very complex machinery that allows them to sense light. Of particular notice is the visual system of the box jellyfish (Class Cubozoa, Phylum Cnidaria) (Figure 1): it is the most elaborate cnidarian visual system. The eyes of these beautiful aquatic animals are very similar to our own! The exceptional vision of the members of the Cubozoa class (the smallest class in the phylum Cnidaria) was detected when it was noticed that they demonstrate unexpectedly complex swimming behaviours: they can move very fast in specific direction and avoid dark areas and obstacles.

Figure 1: Tripedalia cystophora, a box jellyfish from the Caribbean Sea.

There is a number of experimental procedures that allow scientists to study how box jellyfish see. For example, by controlling lighting conditions in experimental chambers where animals are tethered one can observe changes in pulse frequency, contractions and structural asymmetry of the bell of the jellyfish which would translate into avoidance and approach swimming behaviours in free animals. In the past decades the nervous system of the box jellyfish including visual system has been studied from anatomical, cellular, molecular and genetic perspectives but the knowledge of the elaborate eyes of these creatures is still incomplete.

Anatomy[edit]

Box jellyfish has its name for the cube-like shape of its bell, which is about 10 mm in diameter in adult animals. On each side of the bell are situated the four rhopalia – sensory structures that accommodate in total 24 eyes of various types. Such positioning of the visual organs allows Cubomedusae to have a nearly 360-degree view of the surrounding! Remarkably, their eyes do not look outside but inside of the medusa (i.e. at each other!) but thanks to the transparency of the bell can still see in all directions. Jellyfish lack the ability to control eye position with muscles, so rhopalia maintain the same natural orientation independent of the orientation of the bell with the help of the crystal structure in the bottom called statolith that acts as a weight and the flexible stalk on top that connects them to the bell. The six eyes in the rhopalia are of four different morphological types (Figure 2): upper and lower complex lens human-type eyes (ULE and LLE) at the vertical midline, and paired simple eyes with light-sensitive pigment only on each side, called pit and slit eyes (PE and SE).

Although cnidarians are radially symmetrical organisms, the nervous system inside their rhopalia is bilaterally symmetrical, except the midline positioning of the lens eyes. The stalk with a rhopalial nerve inside serves as connection between a rhopalium and the ring nerve at the bell margin which in turn is connected to the nerve net, forming together the complete nervous system of the box jellyfish.

Figure 2: Cubozoan visual system in Tripedalia cystophora

Both neuronal and non-neuronal cells have been described in the rhopalia, forming different cell populations.The neuronal cells cluster in two bilaterally symmetrical groups connected to each other and to pit and slit eyes with fiber pathways. All in all, there are over 1000 neuronal cells in the rhopalium, including:

  • retina-associated neurons linked to lens eyes
  • flank neurons
  • giant neurons

Non-neuronal cells include ciliated photoreceptor cells responsible for the initial light sensing, balloon cells of unidentified function and posterior cell sheet - the largest cell population of undifferentiated cells which are possibly associated with the nervous system.

Lens eyes[edit]

The lens eyes of box jellyfish are astonishingly similar to our own due to the presence of the camera with vitreous body separating the lens and the retina (hence the name “camera eye”). The lens eyes are known to have poor spatial resolution because the retina is very close to the lens separated only by thin vitreous space (around 8 μm in the lower lens eye, absent in upper lens eyes) with focal length of the lens falling far beyond the retina (between 400-600 μm). In humans, by contrast, the size of the camera is about 23 mm. Using a special procedure during which an electrode placed in the eye records activity of cells there in response to various visual stimuli called electroretinography, the temporal properties of the upper and lower lens eyes have also been determined. Both eye types have low temporal resolution but their response patterns differ suggesting that they are utilized for different visual tasks. For example, the maximum frequencies that can be resolved from the electroretinograms (also called flicker fusion frequencies) by the upper and lower lens eyes were reported to be 10 and 8 Hz, respectively. Apart from that, the two lens eyes have different visual fields covering different areas of the surroundings. Overall, it seems plausible that eyes of the box jellyfish are fine tuned to perform specific tasks which in turn allows filtering of the visual stimuli already in the rhopalia.

Do box jellyfish have colour vision?[edit]

A curious question is whether members of the Cubomedusae order have colour vision like more advanced vertebrate organisms including us do. There are two types of photoreceptors in the animal kingdom: ciliary, usually present in vertebrates, and rhabdomeric, found in invertebrates. Interestingly, box jellyfish possess vertebrate-like ciliary photoreceptors. Although both types rely on the same chemical conversion of the retinal molecule upon exposure to light (i.e. bleaching), they differ in the mechanism, structure, origin and molecular pathways. The type of receptors in the retinas of both upper and lower lens eyes are normally sensitive to the blue-green light with peak absorbance between 465-508 nm depending on a species. The available data to date therefore suggests that box jellyfish might be sensitive to green light although experiments in green colour-guided obstacle avoidance produced inconclusive results. Colour vision would be a useful adaptation for these animals living in shallow water with lots of flickering light at the surface ripples to discern the luminance (i.e. brightness, intensity) noise from relevant visual stimuli, as colour vision is less sensitive to luminance fluctuations.

Visual processing and control of swimming behaviour[edit]

To date, only the importance of the lower lens eyes for the control of swimming behaviour has been experimentally established, including their role in bell contraction rates which modulate the speed of the moving animal. Notably, the optical power of lens eyes varies between species of the Cubomedusae introducing further variability. The role of slit and pit eyes in pacemaker activity (pulsating movement of the bell) and control of swimming direction remains somewhat unclear and can be elucidated in future experiments where specific eye types are selectively made non-functional.

Alatina alata box jellyfish swimming

It is also not entirely clear how integration of the visual input occurs in the nervous system of the box jellyfish. Response to visual stimulus was detected both in the stalk (an extension of the nerve ring) as well as the nervous system of the rhopalium itself. The association of specific neuronal cells with certain eye types within the rhopalia signifies that some but not all information processing and integration occurs within these structures. Perhaps the speed of swimming which depends on the rate and strength of the contractions of the body and tentacles of the jellyfish is controlled by the pacemaker activity of a distinct neuronal population that is responsible for higher-order processing and integration of the visual information. Flank and giant neurons might serve this function. The fine steering might in turn be controlled through independent signalling and asymmetrical contraction of the different sides of the bell.

Evolutionary perspective[edit]

Genetically, the visual system of the box jellyfish also appears to be more closely related to that of vertebrates rather than invertebrates, because they share several critical components of the molecular pathways underlying light sensing (for example, phosphodiesterases needed for phototransduction and protective pigment-producing machinery in the retina). The bilateral organisation of the rhopalium nervous system (with the exception of the retina associated neurons) in the otherwise radially symmetrical jellyfish could be the evidence that cnidarians evolved from a bilaterally symmetrical ancestor, but the use of the ciliary photoreceptors and melanogenic pathway by both box jellyfish and vertebrates could mean either common ancestry or independent parallel evolution. Further investigation of the extraordinary visual system of the box jellyfish would therefore be helpful in solving the riddle on the evolutionary origin of the advanced camera eyes.

References[edit]

  1. Bielecki J, Høeg JT, Garm A: Fixational eye movements in the earliest stage of metazoan evolution. 2013. PLoS One. 8(6):e66442. pubmed
  2. Ekström P, Garm A, Pålsson J, Vihtelic TS, Nilsson DE: Immunohistochemical evidence for multiple photosystems in box jellyfish. 2008. Cell and Tissue Research. 333(1):115-24. pubmed
  3. Kozmik Z, Ruzickova J, Jonasova K, Matsumoto Y, Vopalensky P, Kozmikova I, Strnad H, Kawamura S, Piatigorsky J, Paces V, Vlcek C: Assembly of the cnidarian camera-type eye from vertebrate-like components. 2008. Proceedings of the National Academy of Sciences of the USA. 105(26):8989-93. pubmed
  4. Kozmik Z: The role of Pax genes in eye evolution. 2008. Brain Research Bulletin. 75(2-4):335-9. pubmed
  5. Nordström K, Wallén R, Seymour J, Nilsson D: A simple visual system without neurons in jellyfish larvae. 2003. Proceedings of Biological Sciences. 270(1531):2349-54. pubmed
  6. O'Connor M, Garm A, Marshall JN, Hart NS, Ekström P, Skogh C, Nilsson DE: Visual pigment in the lens eyes of the box jellyfish Chiropsella bronzie. 2010. Proceedings Biological Sciences. 277(1689):1843-8. pubmed
  7. O'Connor M, Garm A, Nilsson DE: Structure and optics of the eyes of the box jellyfish Chiropsella bronzie. 2009. Journal of Comparative Physiology. 195(6):557-69. pubmed
  8. O'Connor M, Nilsson DE, Garm A: Temporal properties of the lens eyes of the box jellyfish Tripedalia cystophora. 2010. Journal of Comparative Physiology. 196(3):213-20. pubmed
  9. Petie R, Garm A, Nilsson DE: Visual control of steering in the box jellyfish Tripedalia cystophora. 2011. Journal of Experimental Biology. 214(Pt 17):2809-15. pubmed
  10. Piatigorsky J, Kozmik Z: Cubozoan jellyfish: an Evo/Devo model for eyes and other sensory systems. 2004. International Journal of Developmental Biology. 48(8-9):719-29. pubmed
  11. Skogh C, Garm A, Nilsson DE, Ekström P: Bilaterally symmetrical rhopalial nervous system of the box jellyfish Tripedalia cystophora. 2006. Journal of Morphology. 267(12):1391-405. pubmed

Zebrafish: Neuronal Computation in the Zebrafish Olfactory Bulb[edit]

The Zebrafish Olfactory System[edit]

The zebrafish (Danio rerio) is a freshwater teleost native in Southeast Asia [1]. Water flow through the nose is laminar and unidirectional. Even when a zebrafish is not moving, water flow is provided by motile cilia such that a constant odourant supply is provided. Hence, a zebrafish constantly screens the odour space by moving through the environment. The first relay station of odour information is the olfactory bulb. Information passing in the olfactory bulb is an extremely complex process which includes multiple steps of transformation performed by the underlying circuitry. For instance, an odour consisting of different molecules activates a specific set of odourant receptors on olfactory sensory neurons, which terminate in the olfactory bulb in an array of glomeruli. Hence, an odour is encoded in a combinatorial fashion of glomerular activation patterns. An adult zebrafish olfactory bulb contains about 140 stereotyped glomeruli [2]. A glomerulus is a functional unit consisting of synaptic connections within three different cell classes(Figure 1)[3].

  • The incoming olfactory sensory neurons expressing the same odorant receptor. All synaptic connections are excitatory.
  • Inhibitory interneurons responsible for multiple transformations of the odour signal. In Zebrafish, interneurons can be subdivided into periglomerular cells, granule cells and short axon cells, each of which has distinct morphological features.
  • Mitral cells, which relay the signal out of the olfactory bulb to higher brain areas. In adult Zebrafish there are about 1’500 mitral cells. 70% of these mitral cells receive input from one distinct glomerulus [4].
Figure 1: Schematic view of cell types in the Zebrafish olfactory bulb. Short axon cells (SAC), Olfactory sensory neurons (OSN), Granule cells (GC), Periglomerular cells (PGC).

For the olfactory system, the broad concept of receptive fields present in the visual system is only valid in a very general sense. As described above, a glomerulus receives input from olfactory sensory neurons expressing the same odourant receptor. As a result, a rough spatial chemotopic map is spanned across the olfactory bulb. In other words, different classes of natural odours of the Zebrafish (amino acids, bile acids, nucleotides) activate different anatomical domains of the olfactory bulb [1].

Pattern Decorrelation[edit]

A computational step ongoing in the glomeruli of the zebrafish is pattern decorrelation, which reduces the overlap between activity patterns representing similar odours. Think of two similar fragrances such as cumin seed essential and fennel seed essential. Due to the similar molecular composition, both fragrances initially evoke a similar glomerular activation pattern. Initially, these activation patterns are therefore highly correlated. In other words, odourants with similar molecular features activate overlapping combinations of gloremuli. Subsequently, most of the correlations decrease and the glomerular activity gets redistributed and settles to a steady state. From a computational point of view pattern decorrelation is an useful early step in many pattern classification procedures. It does not increase the information content of an odour representation and it does not increase the performance of an optimal classifier. Rather, it can improve the performance of suboptimal classifiers by increasing the tolerance region(Figure 2) [5]. In nervous systems this process could be important for learning odours and subsequent identification of these odours [1].

Figure 2A: Simplified schematic representation of pattern decorrelation. Black points are binary activity patterns evoked by two similar stimuli. Circles represent noise. The black line shows the perfect separation of the two stimuli bz an optimal classifier. The dashed lines define an arbitrary tolerance region. The red line is the separation of the stimuli by an imperfect classifier.
Figure 2B: Simplified schematic representation of pattern decorrelation. Decorrelated activity pattern have the same relative noise overlap. Since the imperfect classifier has a fixed offset from the perfect classifier, the red line is now within the tolerance region.
Figure 3A: Real data of the firing rates of mitral cells before and after exposing odourants to the olfactory bulb. Neurons whose initial firing rates are positioned along the diagonal axis are rearranged near the x and y axis in the later phase (Data from Friedrich, R. W., & Wiechert, M. T. (2014). Neuronal circuits and computations: Pattern decorrelation in the olfactory bulb. Elsevier). Pattern decorrelation between early and late phase is simulated using linear interpolation.
Figure 3B: Change of Pearson's correlation coefficient in time. Correlation between firing rate to stimulus 1 and firing rate to stimulus 2 decreases over time, given interpolated data from early and steady state phases.

Odour evoked glomerular activity patterns can be measured optically by introducing calcium sensors selectively into the olfactory sensory neurons [6]. This was done in Zebrafishes to analyze the glomerular activity pattern evoked by 16 amino acids, which belong to the natural odour space of zebrafishes. To study pattern decorrelation, responses to highly similar amino acids (Phe, Tyr or Trp) were measured across the mitral cells by multiphoton calcium imaging. Multiphoton calcium imaging revealed that the activity patterns in spatially clustered mitral cells initially overlapped. This overlap subsequently decreased because subsets of these mitral cells became less active or silent, resulting in a local, but not a global, sparsening of MC activity. Concomitantly, the activity of inhibitory interneurons increased and became more dense. Figure 3 shows real data of the activity of Mitral cells before and after a Zebrafish olfactory bulb is exposed to two different odourants.

Other Possible Simulation Approaches of Pattern Decorrelation[edit]

  • Recurrence-enhanced threshold-induced decorrelation (reTIDe)

Analytical approaches and simulations showed that generic networks of Stochastic networks of rectifying elements (SNOREs) with uniform synaptic weights decorrelate specific input patterns by a mechanism referred to as reTIDe [1]. Thresholding the input pattern is the first step in reTIDe. SNOREs consist of threshold-linear units that are randomly connected by synapses of uniform weight. For any positively correlated and normally distributed input patterns, this nonlinearity always results in decorrelation and that decorrelation monotonically increases with the threshold level [7]. This decorrelation is then amplified by feeding the thresholded output patterns back into the network through recurrent connections until a steady state is reached [6]. For mathematical proof and analysis, refer to ONLINE METHOD of the referenced paper [7].

  • Optimizing a weight matrix to model activities of interneurons

W is a weight matrix which represents activity of interneurons between Mitral cells. For instance, its element represents connectivity strength from Mitral cell to Mitral cell. X(t) is a matrix representing firing rates to stimulus 1 and 2 of each individual Mitral cell at time t. Given more data set of X in time, it is probable that the weight matrix W can be optimized.

Conclusions[edit]

Pattern decorrelation in the olfactory bulb is a computational step which has been observed in the zebrafish. However, there has not been proposed a mathematical model to explain pattern decorrelation on a mechanistic basis. A model of how excitatory and inhibitory neurons interact together in the olfactory bulb will help to understand how pattern decorrelation is performed on a neurons level. But even so, such a model implies a full connectivtiy map of the olfactory bulb. This goal mainly depends on the achievements of the acquisition of large datasets with scanning electron microscopy techniques and dense EM-based reconstruction of this data in the next years.

Acknowledgement[edit]

We express our special gratitude to Prof. Rainer Friedrich for his advice on this work.

References[edit]

[1] Friedrich, R. W. (2013). Neuronal Computations in the Olfactory System of Zebrafish. The annual Review of Neuroscience.

[2] Braubach, O. R., (2012). Distribution and Functional Organization of Glomeruli in the Olfactory Bulbs of Zebrafish (Danio rerio). The Journal of Comparative Neurology.

[3] Figure 1 is adapted from: Friedrich, R. W., & Wiechert, M. T. (2014). Neuronal circuits and computations: Pattern decorrelation in the olfactory bulb. Elsevier.

[4] Fuller C. L., (2006). Mitral cells in the olfactory bulb of adult zebrafish (Danio rerio): morphology and distribution. J. Neurophysiology.

[5] Figure 2 is adapted from: Friedrich, R. W. (2013). Neuronal Computations in the Olfactory System of Zebrafish. The annual Review of Neuroscience.

[6] Friedrich, R. W., & Wiechert, M. T. (2014). Neuronal circuits and computations: Pattern decorrelation in the olfactory bulb. Elsevier.

[7] Wiechert, M. T. et. al (2010). Mechanism of pattern decorrelation by recurrent neuronal circuits.

Arthropods · TOC