Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition?This paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation – the availability of a very large number of conscious experiences; and integration – the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Φ value of a complex of elements. Φ is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Φ>0 that is not part of a subset of higher Φ. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex.The information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness.The theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts. For example, why is it that certain parts of the brain are important for conscious experience, whereas others, equally rich in neurons and connections, are not? And why are we conscious during wakefulness or dreaming sleep, but much less so during dreamless sleep, even if the brain remains highly active? The second problem is to understand the conditions that determine what kind of consciousness a system has. For example, what determines the specific and seemingly irreducible quality of the different modalities , submodalities , and dimensions (e.g. blue and red) that characterize our conscious experience? Why do colors look the way they do, and different from the way music sounds, or pain feels? Solving the first problem means that we would know to what extent a physical system can generate consciousness – the quantity or level of consciousness. Solving the second problem means that we would know what kind of consciousness it generates – the quality or content of consciousness.Consciousness is everything we experience. Think of it as what abandons us every night when we fall into dreamless sleep and returns the next morning when we wake up . WithoutWe all know that our own consciousness waxes when we awaken and wanes when we fall asleep. We may also know first-hand that we can "lose consciousness" after receiving a blow on the head, or after taking certain drugs, such as general anesthetics. Thus, everyday experience indicates that consciousness has a physical substrate, and that that physical substrate must be working in the proper way for us to be fully conscious. It also prompts us to ask, more generally, what may be the conditions that determine to what extent consciousness is present. For example, are newborn babies conscious, and to what extent? Are animals conscious? If so, are some animals more conscious than others? And can they feel pain? Can a conscious artifact be constructed with non-neural ingredients? Is a person with akinetic mutism – awake with eyes open, but mute, immobile, and nearly unresponsive – conscious or not? And how much consciousness is there during sleepwalking or psychomotor seizures? It would seem that, to address these questions and obtain a genuine understanding of consciousness, empirical studies must be complemented by a theoretical analysis.The theory presented here claims that consciousness has to do with the capacity to integrate information. This claim may not seem self-evident, perhaps because, being endowed with consciousness for most of our existence, we take it for granted. To gain some perspective, it is useful to resort to some thought experiments that illustrate key properties of subjective experience: its informativeness, its unity, and its spatio-temporal scale.Consider the following thought experiment. You are facing a blank screen that is alternately on and off, and you have been instructed to say "light" when the screen turns on and "dark" when it turns off. A photodiode – a very simple light-sensitive device – has also been placed in front of the screen, and is set up to beep when the screen emits light and to stay silent when the screen does not. The first problem of consciousness boils down to this. When you differentiate between the screen being on or off, you have the conscious experience of "seeing" light or dark. The photodiode can also differentiate between the screen being on or off, but presumably it does not consciously "see" light and dark. What is the key difference between you and the photodiode that makes you "see" light consciously? information is generated when that differentiation is made. Information is classically defined as reduction of uncertainty among a number of alternatives outcomes when one of them occurs [ilog2pi. Thus, tossing a fair coin and obtaining heads corresponds to 1 bit of information, because there are just two alternatives; throwing a fair die yields log2(6) ≈ 2.59 bits of information, because there are six equally likely alternatives .According to the theory, the key difference between you and the photodiode has to do with how much m occurs . It can When the blank screen turns on, the photodiode enters one of its two possible alternative states and beeps. As with the coin, this corresponds to 1 bit of information. However, when you see the blank screen turn on, the state you enter, unlike the photodiode, is one out of an extraordinarily large number of possible states. That is, the photodiode's repertoire is minimally differentiated, while yours is immensely so. It is not difficult to see this. For example, imagine that, instead of turning homogeneously on, the screen were to display at random every frame from every movie that was or could ever be produced. Without any effort, each of these frames would cause you to enter a different state and "see" a different image. This means that when you enter the particular state ("seeing light") you rule out not just "dark", but an extraordinarily large number of alternative possibilities. Whether you think or not of the bewildering number of alternatives , this corresponds to an extraordinary amount of information . This point is so simple that its importance has been overlooked.1,000,000 states, an immense number, corresponding to 1,000,000 bits of information. Indeed, the camera would easily enter a different state for every frame from every movie that was or could ever be produced. Yet nobody would believe that the camera is conscious. What is the key difference between you and the camera?While the ability to differentiate among a very large number of states is a major difference between you and the lowly photodiode, by itself it is not enough to account for the presence of conscious experience. To see why, consider an idealized one megapixel digital camera, whose sensor chip is essentially a collection of one million photodiodes. Even if each photodiode in the sensor chip were just binary, the camera as such could differentiate among 2information integration. From the perspective of an external observer, the camera chip can certainly enter a very large number of different states, as could easily be demonstrated by presenting it with all possible input signals. However, the sensor chip can be considered just as well as a collection of one million photodiodes with a repertoire of two states each, rather than as a single integrated system with a repertoire of 21,000,000 states. This is because, due to the absence of interactions among the photodiodes within the sensory chip, the state of each element is causally independent of that of the other elements, and no information can be integrated among them. Indeed, if the sensor chip were literally cut down into its individual photodiodes, the performance of the camera would not change at all.According to the theory, the key difference between you and the camera has to do with By contrast, the repertoire of states available to you cannot be subdivided into the repertoire of states available to independent components. This is because, due to the multitude of causal interactions among the elements of your brain, the state of each element is causally dependent on that of other elements, which is why information can be integrated among them. Indeed, unlike disconnecting the photodiodes in a camera sensor, disconnecting the elements of your brain that underlie consciousness has disastrous effects. The integration of information in conscious experience is evident phenomenologically: when you consciously "see" a certain image, that image is experienced as an integrated whole and cannot be subdivided into component images that are experienced independently. For example, no matter how hard you try, for example, you cannot experience colors independent of shapes, or the left half of the visual field of view independently of the right half. And indeed, the only way to do so is to physically split the brain in two to prevent information integration between the two hemispheres. But then, such split-brain operations yield two separate subjects of conscious experience, each of them having a smaller repertoire of available states and more limited performance .Finally, it is important to appreciate that conscious experience unfolds at a characteristic spatio-temporal scale. For instance, it flows in time at a characteristic speed and cannot be much faster or much slower. No matter how hard you try, you cannot speed up experience to follow a move accelerated a hundred times, not can you slow it down if the movie has decelerated. Studies of how a percept is progressively specified and stabilized – a process called microgenesis – indicate that it takes up to 100–200 milliseconds to develop a fully formed sensory experience, and that the surfacing of a conscious thought may take even longer . In factIf consciousness corresponds to the capacity to integrate information, then a physical system should be able to generate consciousness to the extent that it has a large repertoire of available states (information), yet it cannot be decomposed into a collection of causally independent subsystems (integration). How can one identify such an integrated system, and how can one measure its repertoire of available states ,8?1,000,000 states. To measure information integration, it is essential to know whether a set of elements constitute a causally integrated system, or they can be broken down into a number of independent or quasi-independent subsets among which no information can be integrated.As was mentioned above, to measure the repertoire of states that are available to a system, one can use the entropy function, but this way of measuring information is completely insensitive to whether the information is integrated. Thus, measuring entropy would not allow us to distinguish between one million photodiodes with a repertoire of two states each, and a single integrated system with a repertoire of 2To see how one can achieve this goal, consider an extremely simplified system constituted of a set of elements. To make matters slightly more concrete, assume that we are dealing with a neural system. Each element could represent, for instance, a group of locally interconnected neurons that share inputs and outputs, such as a cortical minicolumn. Assume further that each element can go through discrete activity states, corresponding to different firing levels, each of which lasts for a few hundred milliseconds. Finally, for the present purposes, let us imagine that the system is disconnected from external inputs, just as the brain is virtually disconnected from the environment when it is dreaming.Hmax), i.e. we substitute its elements with independent noise sources, and we determine the entropy of the responses of B that can be induced by inputs from A. Specifically, we define the effective information between A and B as EI(A→B) = MI. Here MI = H(A) + H(B) - H(AB) stands for mutual information, a measure of the entropy or information shared between a source (A) and a target (B). Note that since A is substituted by independent noise sources, there are no causal effects of B on A; therefore the entropy shared by B and A is necessarily due to causal effects of A on B. Moreover, EI(A→B) measures all possible effects of A on B, not just those that are observed if the system were left to itself. Also, EI(A→B) and EI(B→A) in general are not symmetric. Finally, note that the value of EI(A→B) is bounded by AHmax and BHmax, whichever is less. In summary, to measure EI(B→A), one needs to apply maximum entropy to the outputs from B, and determine the entropy of the responses of B that are induced by inputs from A. It should be apparent from the definition that EI(A→B) will be high if the connections between A and B are strong and specialized, such that different outputs from A will induce different firing patterns in B. On the other hand, EI(A→B) will be low or zero if the connections between A and B are such that different outputs from A produce scarce effects, or if the effect is always the same. For a given bipartition of a subset, then, the sum of the effective information for both directions is indicated as EI(A  B) = EI(A→B) + EI(B→A). Thus, EI(A  B) measures the repertoire of possible causal effects of A on B and of B on A.Consider now a subset S of elements taken from such a system, and the diagram of causal interactions among them Fig. . We want B) = 0  is low: the effective information across that bipartition is the limiting factor on the subset's information integration capacity. Therefore in order to measure the information integration capacity of a subset S, we should search for the bipartition(s) of S for which EI(A  B) reaches a minimum ." Since EI(A  B) is necessarily bounded by the maximum entropy available to A or B, min{EI(A  B)}, to be comparable over bipartitions, should be normalized by Hmax(A  B) = min{Hmax(A); Hmax(B)}, the maximum information capacity for each bipartition. The minimum information bipartition MIBA  B of subset S – its 'weakest link' – is its bipartition for which the normalized effective information reaches a minimum, corresponding to min{EI(A  B)/Hmax(A  B)}. The information integration for subset S, or Φ(S), is simply the  value of EI(A  B) for the minimum information bipartition: Φ(S) = EI(MIBA  B). The symbol Φ is meant to indicate that the information  is integrated within a single entity .Based on the notion of effective information for a bipartition, we can assess how much information can be integrated within a system of elements. To this end, we note that a subset S of elements cannot integrate any information (as a subset) if there is a way to partition S in two parts A and B such that EI is called the main complex . Some properties of complexes worth pointing out are, for instance, that a complex can be causally connected to elements that are not part of it . Also, the same element can belong to more than one complex, and complexes can overlap.We are now in a position to establish which subsets are actually capable of integrating information, and how much of it Fig. . To do swithin a complex and not outside its boundaries, consciousness as information integration is necessarily subjective, private, and related to a single point of view or perspective [In summary, a system can be analyzed to identify its complexes – those subsets of elements that can integrate information, and each complex will have an associated value of Φ – the amount of information it can integrate . To the extent that consciousness corresponds to the capacity to integrate information, complexes are the "subjects" of experience, being the locus where information can be integrated. Since information can only be integrated spective ,9. It foThe Φ value of a complex is dependent on both spatial and temporal scales that determine what counts as a state of the underlying system. In general, there will be a "grain size", in both space and time, at which Φ reaches a maximum. In the brain, for example, synchronous firing of heavily interconnected groups of neurons sharing inputs and outputs, such as cortical minicolumns, may produce significant effects in the rest of the brain, while asynchronous firing of various combinations of individual neurons may be less effective. Thus, Φ values may be higher when considering as elements cortical minicolumns rather than individual neurons, even if their number is lower. On the other hand, Φ values would be extremely low with elements the size of brain areas. Time wise, Φ values in the brain are likely to show a maximum between tens and hundreds of milliseconds. It is clear, for example, that if one were to stimulate one half of the brain by inducing many different firing patterns, and examine what effects this produces on the other half, no stimulation pattern would produce any effect whatsoever after just a tenth of a millisecond, and Φ would be equal to zero. After say 100 milliseconds, however, there is enough time for differential effects to be manifested, and Φ would grow. On the other hand, given the duration of conduction delays and of postsynaptic currents, much longer intervals are not going to increase Φ values. Indeed, a neural system will soon settle down into states that become progressively more independent of the stimulation. Thus, the search for complexes of maximum Φ should occur over subsets at critical spatial and temporal scales.quantity of consciousness, is given by the Φ value of a complex. Φ is the amount of effective information that can be exchanged across the minimum information bipartition of a complex. A complex is a subset of elements with Φ>0 and with no inclusive subset of higher Φ. The spatial and temporal scales defining the elements of a complex and the time course of their interactions are those that jointly maximize Φ.To recapitulate, the theory claims that consciousness corresponds to the capacity to integrate information. This capacity, corresponding to the Even if we were reasonably sure that a system is conscious, it is not immediately obvious what kind of consciousness it would have. As was mentioned early on, our own consciousness comes in specific and seemingly irreducible qualities, exemplified by different modalities , submodalities , and dimensions (e.g. blue and red). What determines that colors look the way they do, and different from the way music sounds, or pain feels? And why can we not even imagine what a "sixth" sense would feel like? Or consider the conscious experience of others. Does a gifted musician experience the sound of an orchestra the same way you do, or is his experience richer? And what about bats ? AssuminWhile it may not be obvious how best to address this problem, we do know that, just as the quantity of our consciousness depends on the proper functioning of a physical substrate – the brain, so does the quality of consciousness. Consider for example the acquisition of new discriminatory abilities, such as becoming expert at wine tasting. Careful studies have shown that we do not learn to distinguish among a large number of different wines merely by attaching the appropriate labels to different sensations that we had had all along. Rather, it seems that we actually enlarge and refine the set of sensations triggered by tasting wines. Similar observations have been made by people who, for professional reasons, learn to discriminate among perfumes, colors, sounds, tactile sensations, and so on. Or consider perceptual learning during development. While infants experience more than just a "buzzing confusion", there is no doubt that perceptual abilities undergo considerable refinement – just consider what your favorite red wine must have tasted like when all you had experienced was milk and water.can have. What is this something?These examples indicate that the quality and repertoire of our conscious experience can change as a result of learning. What matters here is that such perceptual learning depends upon specific changes in the physical substrate of our consciousness – notably a refinement and rearranging of connections patterns among neurons in appropriate parts of the thalamocortical system (e.g ). Furthequalia space. The values of effective information among the elements of a complex, by defining the relationships among these dimensions, specify the structure of this space . This relational space is sufficient to specify the quality of conscious experience. Thus, the reason why certain cortical areas contribute to conscious experience of color and other parts to that of visual motion has to do with differences in the informational relationships both within each area and between each area and the rest of the main complex. By contrast, the informational relationships that exist outside the main complex – including those involving sensory afferents – do not contribute either to the quantity or to the quality of consciousness.According to the theory, just as the quantity of consciousness associated with a complex is determined by the amount of information that can be integrated among its elements, the quality of its consciousness is determined by the informational relationships that causally link its elements . That isTo exemplify, consider two very simple linear systems of four elements each Fig. . Fig. 2awithin the respective cortical areas and between each area and the rest of the main complex. For example, areas that are organized topographically and areas that are organized according to a "winner takes all" arrangement should contribute different kinds of experiences. Another prediction is that changes in the quality and repertoire of sensations as a result of perceptual learning would also correspond to a refinement of the informational relationships within and between the appropriate cortical areas belonging to the main complex. By contrast, the theory predicts that informational relationships outside a complex – including those among sensory afferents – should not contribute directly to the quality of conscious experience of that complex. Of course, sensory afferents, sensory organs, and ultimately the nature and statistics of external stimuli, play an essential role in shaping the informational relationships among the elements of the main complex – but such role is an indirect and historical one – played out through evolution, development, and learning [These two examples are purely meant to illustrate how the space of informational relationships within a complex can be captured by the effective information matrix, and how that space can differ for two complexes having similar amounts of Φ and the same number of dimensions. Of course, for a complex having high values of Φ, such as the one underlying our own consciousness, qualia space would be extraordinarily large and intricately structured. Nevertheless, it is a central claim of the theory that the structure of phenomenological relationships should reflect directly that of informational relationships. For example, the conscious experiences of blue and red appear irreducible (red is not simply less of blue). They may therefore correspond to different dimensions of qualia space (different elements of the complex). We also know that, as different as blue and red may be subjectively, they are much closer to each other than they are, say, to the blaring of a trumpet. EI values between the neuronal groups underlying the respective dimensions should behave accordingly, being higher between visual elements than between visual and auditory elements. As to the specific quality of different modalities and submodalities, the theory predicts that they are due to differences in the set of informational relationships learning  . The relevant activity variables are those that mediate the informational relationships among the elements, that is, those that mediate effective information. For example, if the elements are local groups of neurons, then the relevant variables are their firing patterns over tens to hundreds of milliseconds.According to the theory, once the quantity and quality of conscious experience that a complex all the elements of a complex, with no contribution from elements outside the complex. Each conscious state can thus be thought of as a different point in the multidimensional qualia space defined by the effective information matrix of a complex . Therefore, a succession or flow of conscious states over time can be thought of as a trajectory of points in qualia space. The state diagram also illustrates some states that have particular significance (second to fifth column). These are the states with just one active element, and all other elements silent (or active at some baseline level). It is not clear whether such highly selective states can be achieved within a large neural complex of high Φ, such as that one that is postulated to underlie human consciousness. To the extent that this is possible, such highly selective states would represent the closest approximation to experiencing that element's specific contribution to consciousness – its quality or "quale". However, because of the differences in the qualia space between the two complexes, the same state over the four elements would correspond to different experiences (and mean different things) for the two complexes. It should also be emphasized that, in every case, it is the activity state of all elements of the complex that defines a given conscious state, and both active and inactive elements count.The state of a complex at different times can be represented schematically by a state diagram as in Fig. To recapitulate, the theory claims that the quality of consciousness associated with a complex is determined by its effective information matrix. The effective information matrix specifies all informational relationships among the elements of a complex. The values of the variables mediating informational interactions among the elements of a complex specify the particular conscious experience at any given time.Based on a phenomenological analysis, we have argued that consciousness corresponds to the capacity to integrate information. We have then considered how such capacity can be measured, and we have developed a theoretical framework for consciousness as information integration. We will now consider several neuroanatomical or neurophysiological factors that are known to influence consciousness. After briefly discussing the empirical evidence, we will use simplified computer models to illustrate how these neuroanatomical and neurophysiological factors influence information integration. As we shall see, the information integration theory not only fits empirical observations reasonably well, but offers a principled explanation for them.Ancient Greek philosophers disputed whether the seat of consciousness was in the lungs, in the heart, or in the brain. The brain's pre-eminence is now undisputed, and scientists are trying to establish which specific parts of the brain are important. For example, it is well established that the spinal cord is not essential for our conscious experience, as paraplegic individuals with high spinal transactions are fully conscious. Conversely, a well-functioning thalamocortical system is essential for consciousness . OpinionThe fact that consciousness as we know it is generated by the thalamocortical system fits well with the information integration theory, since what we know about its organization appears ideally suited to the integration of information. On the information side, the thalamocortical system comprises a large number of elements that are functionally specialized, becoming activated in different circumstances. ,30. ThusAs shown by computer simulations, systems of neural elements whose connectivity jointly satisfies the requirements for functional specialization and for functional integration are well suited to integrating information. Fig. Consider now the cerebellum. This brain region contains more neurons than the cerebral cortex, has huge numbers of synapses, and receives mapped inputs from the environment and controls several outputs. However, in striking contrast to the thalamocortical system, lesions or ablations indicate that the direct contribution of the cerebellum to conscious experience is minimal. Why is this the case?According to the theory, the reason lies with the organization of cerebellar connections, which is radically different from that of the thalamocortical system and is not well suited to information integration. Specifically, the organization of the connections is such that individual patches of cerebellar cortex tend to be activated independently of one another, with little interaction possible between distant patches ,42. Thismodular network, consisting of three modules of eight strongly interconnected elements each. This network yields Φ = 20 bits for each of its three modules, which form the system's three complexes. This example indicates that, irrespective of how many elements and connections are present in a neural structure, if that structure is organized in a strongly modular manner with little interactions among modules, complex size and Φ values are necessarily low. According to the information integration theory, this is the reason why these systems, although computationally very sophisticated, contribute little to consciousness. It is also the reason why there is no conscious experience associated with hypothalamic and brainstem circuits that regulate important physiological variables, such as blood pressure.This concept is illustrated in Fig. It has been known for a long time that lesions in the reticular formation of the brainstem can produce unconsciousness and coma. Conversely, stimulating the reticular formation can arouse a comatose animal and activate the thalamocortical system, making it ready to respond to stimuli . Groups Such a role can be explained readily in terms of information integration. As shown in Fig. What we see usually depends on the activity patterns that occur in the retina and that are relayed to the brain. However, many observations suggest that retinal activity does not contribute directly to conscious experience. Retinal cells surely can tell light from dark and convey that information to visual cortex, but their rapidly shifting firing patterns do not correspond well with what we perceive. For example, during blinks and eye movements retinal activity changes dramatically, but visual perception does not. The retina has a blind spot at the exit of the optic nerve where there are no photoreceptors, and it has low spatial resolution and no color sensitivity at the periphery of the visual field, but we are not aware of any of this. More importantly, lesioning the retina does not prevent conscious visual experiences. For example, a person who becomes retinally blind as an adult continues to have vivid visual images and dreams. Conversely, stimulating the retina during sleep by keeping the eyes open and presenting various visual inputs does not yield any visual experience and does not affect visual dreams. Why is it that retinal activity usually determines what we see through its action on thalamocortical circuits, but does not contribute directly to conscious experience?As shown in Fig. In neurological practice, as well as in everyday life, we tend to associate consciousness with the presence of a diverse behavioral repertoire. For example, if we ask a lot of different questions and for each of them we obtain an appropriate answer, we generally infer that a person is conscious. Such a criterion is not unreasonable in terms of information integration, given that a wide behavioral repertoire is usually indicative of a large repertoire of internal states that are available to an integrated system. However, it appears that neural activity in motor pathways, which is necessary to bring about such diverse behavioral responses, does not in itself contribute to consciousness. For example, patients with the locked-in syndrome, who are completely paralyzed except for the ability to gaze upwards, are fully conscious. Similarly, while we are completely paralyzed during dreams, consciousness is not impaired by the absence of behavior. Even lesions of central motor areas do not impair consciousness.Why is it that neurons in motor pathways, which can produce a large repertoire of different outputs and thereby relay a large amount of information about different conscious states, do not contribute directly to consciousness? As shown in Fig. Another set of neural structures that may not contribute directly to conscious experience are subcortical structures such as the basal ganglia. The basal ganglia are large nuclei that contain many circuits arranged in parallel, some implicated in motor and oculomotor control, others, such as the dorsolateral prefrontal circuit, in cognitive functions, and others, such as the lateral orbitofrontal and anterior cingulate circuits, in social behavior, motivation, and emotion . Each baAs shown in Fig. see objects, separated from the background and laid out in space, and know what they are, or hear words, nicely separated from each other, and know what they mean. As an example, take binocular rivalry, where the two eyes view two different images, but we perceive consciously just one image at a time, alternating in sequence. Recordings in monkeys have shown that the activity of visual neurons in certain cortical areas, such as the inferotemporal cortex, follows faithfully what the subject perceives consciously. However, in other areas, such as primary visual cortex, there are many neurons that respond to the stimulus presented to the eye, whether or not the subject is perceiving it [Even within the thalamocortical system proper, a substantial proportion of neural activity does not appear to contribute directly to conscious experience. For example, what we see and hear requires elaborate computational processes dealing with figure-ground segregation, depth perception, object recognition, and language parsing, many of which take place in the thalamocortical system. Yet we are not aware of all this diligent buzzing: we just iving it . Neuromaiving it . Why doewithin a cortico-thalamic minicolumn qualify as intrinsic mini-loops that support the main complex without being part of it? Unfortunately, answering these questions and properly testing the predictions of the theory requires a much better understanding of cortical neuroanatomy than is presently available [The situation is similar on the executive side of consciousness. When we plan to do or say something, we are vaguely conscious of what we intend, and presumably these intentions are reflected in specific firing patterns of certain neuronal groups. Our vague intentions are then translated almost miraculously into the right words, and strung together to form a syntactically correct sentence that conveys what we meant to say. And yet again, we are not at all conscious of the complicated processing that is needed to carry out our intentions, much of which takes place in the cortex. What determines whether the firing of neurons within the thalamocortical system contributes directly to consciousness or not? According to the information integration theory, the same considerations that apply to input and output circuits and to cortico-subcortico-cortical loops also apply to circuits and loops contained entirely within the thalamocortical system. Thus, the theory predicts that activity within certain cortical circuits does not contribute to consciousness because such circuits implement informationally insulated loops that remain outside of the main thalamocortical complex. At this stage, however, it is hard to say precisely which cortical circuits may be informationally insulated. Are primary sensory cortices organized like massive afferent pathways to a main complex "higher up" in the cortical hierarchy? Is much of prefrontal cortex organized like a massive efferent pathway? Do certain cortical areas, such as those belonging to the dorsal visual stream, remain partly segregated from the main complex? Do interactions vailable ,51.Studies of split-brain patients, whose corpus callosum was sectioned for therapeutic reasons, show that each hemisphere has its own, private conscious experience. The dominant, linguistically competent hemisphere does not seem to suffer a major impairment of consciousness after the operation. The non-dominant hemisphere, although it loses some important abilities and its residual capacities are harder to assess, also appears to be conscious. . Some inViewing consciousness as information integration suggests straightforward explanations for these puzzling observations. Consider the simplified model in Fig. Until now, we have considered structural aspects of the organization of the nervous system that, according to the information integration theory, explain why certain parts of the brain contribute directly to consciousness and others do not, or much less so. In addition to neuroanatomical factors, neurophysiological factors are also important in determining to what extent a given neural structure can integrate information. For example, anatomical connections between brain regions may or may not be functional, depending on both pathological or physiological factors. Functional disconnections between certain parts of the brain and others are thought to play a role in psychiatric conversion and dissociative disorders, may occur during dreaming, and may be implicated in conditions such as hypnosis. Thus, functional disconnections, just like anatomical disconnections, may lead to a restriction of the neural substrate of consciousness.It is also likely that certain attentional phenomena may correspond to changes in the neural substrate of consciousness. For example, when one is absorbed in thought, or focused exclusively on a given sensory modality, such as vision, the neural substrate of consciousness may not be the same as when we are diffusely monitoring the environment. Phenomena such as the attentional blink, where a fixed sensory input may at times make it to consciousness and at times not, may also be due to changes in functional connectivity: access to the main thalamocortical complex may be enabled or not based on dynamics intrinsic to the complex . Phenomedynamic complex" or "dynamic core" [Figure ic core" ,9.Another example of the importance of neurophysiological parameters is provided by sleep – the most familiar of the alterations of consciousness, and yet one of the most striking. Upon awakening from dreamless sleep, we have the peculiar impression that for a while we were not there at all nor, as far as we are concerned, was the rest of the world. This everyday observation tells us vividly that consciousness can come and go, grow and shrink. Indeed, if we did not sleep, it might be hard to imagine that consciousness is not a given, but depends somehow on the way our brain is functioning. The loss of consciousness between falling asleep and waking up is relative, rather than absolute. . Thus, cWhich neurophysiological parameters are responsible for the remarkable changes in the quantity and quality of conscious experience that occur during sleep? We know for certain that the brain does not simply shut off during sleep. During REM sleep, for example, neural activity is as high, if not higher, than during wakefulness, and EEG recordings show low-voltage fast-activity. This EEG pattern is known as "activated" because cortical neurons, being steadily depolarized and close to their firing threshold, are ready to respond to incoming inputs. Given these similarities, it is perhaps not surprising that consciousness should be present during both states. Changes in the quality of consciousness, however, do occur, and they correspond closely to relative changes in the activation of different brain areas. .During slow wave sleep, average firing rates of cortical neurons are also similar to those observed during quiet wakefulness. However, due to changes in the level of certain neuromodulators, virtually all cortical neurons engage in slow oscillations at around 1 Hz, which are reflected in slow waves in the EEG . Slow osFrom the perspective of information integration, a reduction of consciousness during certain phases of sleep would occur even if the brain remained capable of responding to perturbations, provided its response were to lack differentiation. This prediction is borne out by detailed computer models of a portion of the visual thalamocortical system . According to these simulations, in the waking mode different perturbations of the thalamocortical network yield specific responses. In the sleep mode, instead, the network becomes bistable: specific effects of different perturbations are quickly washed out and their propagation impeded: the whole network transitions into the depolarized or into the hyperpolarized phase of the slow oscillation – a stereotypic response that is observed irrespective of the particular perturbation . And of course, this bistability is also evident in the spontaneous behavior of the network: during each slow oscillation, cortical neurons are either all firing or all silent, with little freedom in between. In summary, these simulations indicate that, even if the anatomical connectivity of a complex stays the same, a change in key parameters governing the readiness of neurons to respond and the differentiation of their responses may alter radically the Φ value of the complex, with corresponding consequences on consciousness.Consciousness not only requires a neural substrate with appropriate anatomical structure and appropriate physiological parameters: it also needs time. As was mentioned earlier, studies of how a percept is progressively specified and stabilized indicate that it takes up to 100–200 milliseconds to develop a fully formed sensory experience, and that the surfacing of a conscious thought may take even longer. Experiments in which the somatosensory areas of the cerebral cortex were stimulated directly indicate that low intensity stimuli must be sustained for up to 500 milliseconds to produce a conscious sensation . Multi-uThe theory predicts that the time requirements for the generation of conscious experience in the brain emerge directly from the time requirements for the build-up of effective interactions among the elements of the main complex. As was mentioned above, if one were to perturb half of the elements of the main complex for less than a millisecond, no perturbations would produce any effect on the other half within this time window, and Φ would be equal to zero. After say 100 milliseconds, however, there is enough time for differential effects to be manifested, and Φ should grow. This prediction is confirmed by results obtained using large-scale computer simulations of the thalamocortical system, where the time course of causal interactions and functional integration can be studied in detail ,58,59, HIn summary, the time scale of neurophysiological interactions needed to integrate information among distant cortical regions appears to be consistent with that required by psychophysical observations (microgenesis), by stimulation experiments, and by recording experiments.The previous examples show that the information integration theory is consistent with several empirical observations concerning the neural substrate of consciousness. Moreover, they show that the theory can provide a principled account of why consciousness is associated with certain parts of the brain rather than with others, and with certain global modes of functioning more than with others. To recapitulate the main tenets of the theory, it may be useful to reconsider the initial thought experiment.Imagine again that you are comfortably facing a blank screen that is alternately on and off. When the screen turns on, you see a homogenous blue field, indeed for the sake of the argument we assume that you are having a "pure" perception of blue, unencumbered by extraneous percepts or thoughts (perhaps as can be achieved in certain meditative states). As you have been instructed, you signal your perception of blue by pushing a button. Now consider an extremely simplified scenario of the neural events that might accompany your seeing blue. When the screen turns on, a volley of activity propagates through the visual afferent pathways, involving successive stages such as retinal short wavelength cones, blue-yellow opponents cells, color constant cells, and so on. Eventually, this volley of activity in the visual afferent pathways leads to the firing of some neuronal groups in color-selective areas of the temporal lobe that, on empirical grounds, are our best bet for the neural correlate of blue: i) their activity correlates well with your perception of blue whether you see, imagine, or dream blue, in a way that is as stable and as susceptible to illusions as your perception of blue; ii) their microstimulation leads to the perception of blue; and iii) their selective lesion makes you unable to perceive blue. Let us assume, then, that these neuronal groups quickly increase their firing, and within a few tens of milliseconds they reach and then maintain increased levels of firing . We also assume that, at the same time, neuronal groups in neighboring cortical areas go on firing at a baseline level, largely unaffected by the blue light. These include neuronal groups in other visual areas that are selective for shape or movement; neuronal groups in auditory area that are selective for tones; and many others. On the other hand, the volley of activity originating in the retina does not exhaust itself by generating sustained activity in the color areas of the temporal lobe. Part of the volley proceeds at great speed and activates efferent motor pathways, which cause you to push the signaling button. Another part activates cortico-subcortico-cortical loops in your prefrontal cortex and basal ganglia, which almost make you speak the word "blue" aloud. In the meantime, many other parts of the brain are buzzing along, unaffected by what is going on in the visual system: cerebellar circuits are actively stabilizing your posture and gaze, and hypothalamic-brainstem circuits are actively stabilizing your blood pressure. What components in this simplified neural scenario are essential for your conscious experience of blue, and why?whether a group of neurons contributes directly to consciousness is a function of its belonging to the main complex or not. In this example, the theory would predict that blue-selective neurons in some high-level color area should be inside the main complex; on the other hand, blue-sensitive neurons in afferent visual pathways, neurons in efferent pathways mediating the button-pressing response, neurons in cortico-subcortico-cortical and intracortical loops mediating subvocalization of the word "blue", neurons in the cerebellum controlling posture and neurons in hypothalamic-brainstem circuits controlling blood pressure should be outside. This even though these neurons may be equally active when you see blue, and even though some of them may be connected to elements of the main complex. In principle, joint microstimulation and recording experiments, and to some extent an analysis of patterns of synchronization, could determine participation in the main complex and test this prediction. The theory also predicts that blue-selective neurons in the main complex contribute to the conscious experience of blue only if their activation is sufficiently strong or sustained that they can make a difference, in informational terms, to the rest of the complex. Additional predictions are that, if a group of neurons that is normally part of the main complex becomes informationally disconnected from it, as could occur through attentional effects or in certain phases of sleep, the same group of neurons, firing in exactly the same way, would not contribute to consciousness. Moreover, according to the theory, the other groups of neurons within the main complex are essential to our conscious experience of blue even if, as in this example, they are not activated. This is not difficult to see. Imagine that, starting from an intact main complex, we were to remove one element after another, except for the active, blue-selective one. If an inactive element contributing to "seeing red" were removed, blue would not be experienced as blue anymore, but as some less differentiated color, perhaps not unlike those experienced by certain dichromats. If further elements of the main complex were removed, including those contributing to shapes, to sounds, to thoughts and so forth, one would soon drop to such a low level of consciousness that "seeing blue" would become meaningless: the "feeling" (and meaning) of the quale "blue" would have been eroded down to nothing. Indeed, while the remaining neural circuits may still be able to discriminate blue from other colors, they would do so very much as a photodiode does .The information integration theory makes several claims that lead to associated predictions. A first claim is that the neural substrate of consciousness as we know it is a complex of high Φ that is capable of integrating a large amount of information – the main complex. Therefore, how a group of neurons contributes to consciousness is a function of its informational relationships inside the complex and not outside of it. In this example, blue-selective neurons within the main complex have become blue-selective no doubt thanks to the inputs received from the appropriate afferent pathways, and ultimately because of some aspects of the statistics of the environment and the resulting plastic changes throughout the brain. However, the theory predicts that their present firing contributes the quale "blue" exclusively because of their informational relationships within the main complex. If connections outside the main complex were to be manipulated, including the afferent color pathways, the experience elicited by activating the blue-selective neurons within the complex would stay the same. Conversely, if the relationships inside the main complex were to change, as could be done by changing the pattern of connections within the color-selective area and with the rest of the complex, so would the conscious experience of blue. That is, activating the same neurons would produce a different conscious experience.A second claim of the theory is that the quality of consciousness is determined by the informational relationships within the main complex. Therefore, To conclude, it is worth mentioning some of the implications that derive from the information integration theory of consciousness. At the most general level, the theory has ontological implications. It takes its start from phenomenology and, by making a critical use of thought experiments, it argues that subjective experience is one and the same thing as a system's capacity to integrate information. In this view, experience, that is, information integration, is a fundamental quantity, just as mass, charge or energy are. It follows that any physical system has subjective experience to the extent that it is capable of integrating information, irrespective of what it is made of. Thus, an intriguing implication of the theory is that it should be possible to construct conscious artifacts by endowing them with a complex of high Φ. Moreover, it should be possible to design the quality of their conscious experience by appropriately structuring their effective information matrix.It also follows that consciousness is not an all-or-none property, but it is graded: to varying degrees, it should exist in most natural  systems. Because the conditions needed to build complexes of high Φ are apparently not easy to achieve, however, correspondingly high levels of experience are probably available to only a few kinds of systems, primarily complex brains containing the right type of architecture for maximizing functional specialization and integration. A related implication is that consciousness should also exist, to varying degrees, at multiple spatial and temporal scales. However, it is likely that, in most systems, there are privileged spatial and temporal scales at which information integration reaches a maximum.potentiality – in this case as the potential differentiation of a system's responses to all possible perturbations, yet it is undeniably actual. Consider another thought experiment: you could be in a coma for days, awaken to consciousness for just one second, and revert to a coma. As long as your thalamocortical system can function well for that one second, you will be conscious. That is, a system does not have to explore its repertoire of states to be conscious, or to know how conscious it is supposed to be: what counts is only that the repertoire is potentially available. While this may sound strange, fundamental quantities associated with physical systems can also be characterized as dispositions or potentialities, yet have actual effects. For example, mass can be characterized as a potentiality – say the resistance that a body would offer to acceleration by a force – yet it exerts undeniable effects, such as attracting other masses. This too has intriguing implications. For example, because in this view consciousness corresponds to the potential of an integrated system to enter a large number of states by way of causal interactions within it, experience is present as long as such potential is present, whether or not the system's elements are activated. Thus, the theory predicts that a brain where no neurons were activated, but were kept ready to respond in a differentiated manner to different perturbations, would be conscious (perhaps that nothing was going on). Also, because consciousness is a property of a system, not of a state, the state the system is in only determines which particular experience becomes actual at any given time, and not whether experience is present. Thus, a brain where each neuron were microstimulated to fire as an exact replica of your brain, but where synaptic interactions had been blocked, would be unconscious.Consciousness is characterized here as a disposition or The theory predicts that consciousness depends exclusively on the ability of a system to integrate information, whether or not it has a strong sense of self, language, emotion, a body, or is immersed in an environment, contrary to some common intuitions. This prediction is consistent with the preservation of consciousness during REM sleep, when both input and output signals from and to the body are markedly reduced. Transient inactivation of brain areas mediating the sense of self, language, and emotion could assess this prediction in a more cogent manner.Nevertheless, the theory recognizes that these same factors are important historically because they favor the development of neural circuits forming a main complex of high Φ. For example, the ability of a system to integrate information grows as that system incorporates statistical regularities from its environment and learns . In thisAnother implication of the theory is that the presence and extent of consciousness can be determined, in principle, also in cases in which we have no verbal report, such as infants or animals, or in neurological conditions such as coma and vegetative states, minimally conscious states, akinetic mutism, psychomotor seizures, and sleepwalking. In practice, of course, measuring Φ accurately in such systems will not be easy, but approximations and informed guesses are certainly conceivable.At present, the validity of this theoretical framework and the plausibility of its implications rest on its ability to account, in a coherent manner, for some basic phenomenological observations and for some elementary but puzzling facts about the relationship between consciousness and the brain. Experimental developments, especially of ways to stimulate and record concurrently the activity of broad regions of the brain, should permit stringent tests of some of the theory's predictions. Equally important will be the development of realistic, large-scale models of the anatomical organization of the brain. These models should allow a more rigorous measurement of how the capacity to integrate information relates to different brain structures and certain neurophysiological parameters ,50,59. Fi. The problem can also be posed in neural terms. When we see light, certain neurons in the retina turn on, as do other neurons higher up in the brain. Based on what we know, the activity of neurons in the retina is not directly associated with conscious experience of light and dark – they behave just like biological photodiodes that signal to higher centers. Somewhere in those higher centers, however, there seem to be some neurons whose activity is indeed tightly correlated with the conscious experience of light and dark. What is special about these higher neurons?ii. Note that this information has nothing to do with how complicated the scene is, or how many different objects it appears to contain, but only with the number of alternative outcomes.MIBcomplexity, for minimum information bipartition complexity. Note that, in most cases, the bipartitions for which the normalized value of EI will be at a minimum, everything else being equal, will be bipartitions that cut the system in two halves, i.e. midpartitions [iii. This quantity is also called rtitions .μ of the minimal ε-machine capable of reproducing the causal structure of a process, i.e. of the ε-machine that cannot be decomposed into a collection of lower Hμ ε-machines.iv. Complexes can also be defined using mutual information instead of effective information, by exploiting the endogenous sources of variance that may exist in an isolated system . A relatv. An elementary description of the qualia space is given by the author in , chaptervi. While the entries in the matrix contain all the relevant informational relationships defining this space, they do not reveal necessarily how the space is organized in an economical and explicit manner. This may be done by employing procedures akin to multidimensional scaling although, since the matrix is asymmetrical and involves high-order terms (among subsets of elements), this may not be easy. Satisfactorily mapping the phenomenological differences between modalities, submodalities and dimensions onto the structure of qualia space will require that we thoroughly characterize and understand the latter.vii. Of course, sensory afferents usually play a role in determining which particular conscious experience we have at any given time . Nevertheless, particular experiences can be triggered even when we are disconnected from the environment, as in dreams.viii. Note also that a "pure" sensation of blue defines a point in this N-dimensional qualia space as much as the experience of a busy city street, full of different objects, of sounds, smells, associations, and reflections defines another point.ix. However, certain areas such as the posterior cingulate cortex and precuneus, some lateral parietal areas, and associated paramedian thalamic nuclei, may constitute strategic crossroads for coordinating the interactions among different sensory maps and frames of reference concerning the body and the environment. Bilateral lesions to such areas may lead to a virtual breakdown of information integration in the thalamocortical system ,24. A glx. Statements about the lack of direct contributions to consciousness of basal ganglia loops need to be qualified due to the difficulty of evaluating the precise effects of their selective inactivation, as well as to the unreliability of introspective assessments about the richness of one's experience, especially after brain lesions. Similar considerations apply to brain structures not discussed here, such as the claustrum, the amygdala, and the basal forebrain.xi. A similar kind of analysis could be applied to other neurological disconnection syndromes.xii. An explanation in terms of reduced degrees of freedom may also apply to loss of consciousness in absence and other seizures, during which neural activity is extremely high and near-synchronous over many cortical regions .xiii. While we do not yet have such a tight case for the neural correlate of blue, we are close to it with motion sensitive cells in area MT and in somatosensory cortex, at least in monkeys .xv. In this sense, a particular conscious experience, its meaning, and the underlying informational relationships within a complex end up being one and the same thing. Such internalistic, relationally defined meanings generally relate to and ultimately derive from entities in the world. To the extent that the brain has a long evolutionary history and is shaped by experience, it is clear that internally specified meanings (and conscious states) bear an adaptive relationship to what is out there.


In general shell-less slugs are considered to be slimy animals with a rather dull appearance and a pest to garden plants. But marine slugs usually are beautifully coloured animals belonging to the less-known Opisthobranchia. They are characterized by a large array of interesting biological phenomena, usually related to foraging and/or defence. In this paper our knowledge of shell reduction, correlated with the evolution of different defensive and foraging strategies is reviewed, and new results on histology of different glandular systems are included.Based on a phylogeny obtained by morphological and histological data, the parallel reduction of the shell within the different groups is outlined. Major food sources are given and glandular structures are described as possible defensive structures in the external epithelia, and as internal glands.According to phylogenetic analyses, the reduction of the shell correlates with the evolution of defensive strategies. Many different kinds of defence structures, like cleptocnides, mantle dermal formations (MDFs), and acid glands, are only present in shell-less slugs. In several cases, it is not clear whether the defensive devices were a prerequisite for the reduction of the shell, or reduction occurred before. Reduction of the shell and acquisition of different defensive structures had an implication on exploration of new food sources and therefore likely enhanced adaptive radiation of several groups. Aplysia californica Cooper, 1863, the sea hare, belonging to the subgroup Anaspidea  than the terrestrial Stylommatophora (about 30000 species), or the shelled marine gastropods, in former times named "Prosobranchia" (60000 species), but they show many biological features that are unique or rare in the animal kingdom and that are often related to foraging or defensive strategies . These iPhyllodesmium Ehrenberg, 1831 and mantle dermal formations in Chromodorididae. Cimino and Ghiselin [Knowledge on the different subgroups of the Opisthobranchia differs according to their availability and spectacular appearance. For example, many more data on feeding strategies and other biological features are available for the beautifully coloured Nudibranchia Fig. , than foGhiselin  and CimiGhiselin  discusseGhiselin . In the Figure Acteon tornatilis . Acteonidae and Hydatinidae are carnivorous and mainly feed on polychaetes. No defensive strategies are known from these animals although histological investigations show a highly glandular area in the mantle cavity and the mantle rim. The mantle rim glands, for example, are very conspicuous. These comprise large epithelial cells that are filled with a non-staining vacuole  is not monophyletic due to the inclusion of the Anaspidea. In our analysis, the gizzard-bearing groups form one clade, representing the Anaspidea Fig.  and the ill Fig. . FeedingBulla Linné, 1758, Haminoea Turton & Kingston, 1830, Retusa Brown, 1827, Cylichna Lovén, 1846, etc., only a few carnivorous cephalaspids have retained a large shell   or have lost it all together . Cephalaspideans have several glandular structures, although their function is hardly understood. The hypobranchial gland is composed of epithelial cells staining violet  or can be reduced . In many Cephalaspidea single glandular cells can be observed that stain bluish and open to the outside by a small duct  Fig. . Many halet Fig. . This gluct Fig. .This group, which is mainly characterized by two pairs of head tentacles Fig. , is closTylodina perversa  fosters the secondary metabolites from its exclusive food, the sponge Aplysina aerophoba Schmidt, 1862 [A. aerophoba from deeper waters, or A. cavernicola) were neglected. Histological investigation show that the Tylodinoidea have several peculiar glands. In the dorsal mantle tissue of Umbraculum umbraculum , many tubules of a highly ramifying gland  are characterized by epithelial glandular cells in which the vacuole is filled with homogenously stained contents  can be undertaken, and will serve as a guideline for further investigations.Buccinum, a "prosobranchiate" caenogastropod, annotation of the authors) is broken away and the soft animal is then offered to a hungry cod, it is eaten readily." (p: 115) [A shell is generally considered to be a protection against predators, such as fish, crabs and other vagile organisms. "If the shell of a whelk (e.g. (p: 115) . ReductiScaphander or Acteon Montfort, 1810, have an elaborate cephalic shield that partially covers the shell and renders them streamlined. Loss of the shell probably enables slugs to search for food in sandy or muddy habitats more easily. This is the case for members of the Cephalaspidea s.str. and Acochlidiacea.Loss of the shell therefore can be assumed to have advantages compared to the presence of a protective but heavy shell. Advantages probably lay in the exploration of new habitats, which are more difficult to reach when being protected by a shell. This can be observed e.g. in a subgroup of the Cladobranchia. The Aeolidoidea are able to graze on fragile hydrozoans Fig. . This kiElysia timida , Placobranchus ocellatus) the chloroplasts are stored in distinct branches of the digestive gland. Here they are stored for a period of several days to months [Phyllodesmium jakobsenae Burghardt & Wägele, 2004, or Melibe bucephala Bergh, 1902) [Elysia chlorotica Gould, 1870, which can survive eight months without food [Pteraeolidia ianthina  and the dendronotoidean Melibe bucephala, both of which survived in our aquaria for 10 months without food (Burghardt & Wägele unpublished data).Basal members of the Sacoglossa have retained a shell, but more derived ones have lost it. Shell loss allowed evolution of a phenomenon that is unique in the animal kingdom. Sacoglossa in general feed on algae by piercing the cells with their tooth and sucking out the contents of the cell. The cytoplasm is digested, but in many species  ,34. Hereh, 1902) -37. Accoh, 1902) . The shoh, 1902) . The mosLoss of a shell as a protective structure led to an array of different defensive structures. Some of these traits can be observed as a combination in one and the same species.Phyllidia flava Aradas, 1847). Cryptic appearance also is achieved by mimicking the same patterns or even outline of the substrate. Corambe pacifica MacFarland & O'Donoghue, 1929 perfectly mimics the colour patterns of its prey, the bryozoan Membranipora de Blainville, 1830. Phyllodesmium jakobsenae mimics the feathered polyps of the soft coral Xenia Lamarck, 1816, on which it lives [P. briareum  are smooth like the tentacles of its prey, the soft coral Briareum Blainville, 1830 ) is very common in spicule-bearing dorids. According to Gosliner [Crypsis can be observed in many groups and is very often achieved by incorporation of the same dyes from the food , have a highly glandular oral tube , which is typical for nearly all members of the cladobranch Aeolidoidea ,9,40. Thube Fig.  that supve tract .3) and brucite (Mg(OH)2) [Navanax Pilsbry, 1895 , who is a ferocious predator on opisthobranchs [Presence of spicules in the notum as a defensive strategy was discussed by several authors ,44. SpicMg(OH)2) . Smallerobranchs . This spde novo synthesis of secondary metabolites that are toxic to possible predators [de novo synthesis is known only from few taxa [Hypselodoris webbi (Chromodorididae) [Another evolutionary trait for defence, and discussed as a prerequisite for shell reduction at least in sacoglossans , is the redators ,46. Uptafew taxa . When difew taxa . Literatfew taxa ,7,46-49.few taxa -7,46. Fefew taxa ,50. Alsofew taxa , or evenfew taxa . Tracingrididae) .Inorganic compounds, like sulphuric acid are produced in few groups. Their function and location is better known due to the extensive work of Thompson -55. He aAplysia kurodai Baba, 1937 [Dolabella auricularia  [), and seems to be one of the most potent anticancer agents.Broad histological investigations of the Opisthobranchia show that many species are characterized by a large array of glandular structures ,11,57-59ba, 1937 . By expeba, 1937 . The rolt, 1786) . This laInformation on other glandular structures are rare, and nearly nothing is known about their contents and their functions. At the moment we are not able to trace the different substances in these glands to find out whether there are any constraints concerning structure (and therefore function) and the stored chemicals.Elysia timida, the slugs switched to a de novo synthesis of polyproprionates. Cimino & Ghiselin also mentioned that handling and utilization of a particular kind of defensive metabolite allowed the switch to food with similar compounds quite easily, and therefore has driven adaptive radiation [Dendrodoris Ehrenberg, 1831, is considered to be the most derived form of defence within the Anthobranchia.Only few hypotheses are formulated concerning acquisition of toxicity and loss of the shell. Faulkner & Ghiselin assumed that chemical defence based on metabolites derived from food preceded the reduction of the shell and that chemical defence has been a driving force behind the evolution of Opisthobranchia ,46,63. CInformation on defensive strategies, as listed above, is available now for several groups of the Opisthobranchia. More and more reliable phylogenies are becoming available, which allow the identification of well-supported branches and stemlines. Combining this knowledge, it becomes evident that several defensive systems evolved before the loss of the shell . Here we would like to extend the hypothesis of Cimino et al. by addressing the problem of excretion . It can But we still have to identify the location of the compounds for a better understanding of the evolutionary history concerning the acquisition of toxicity, which certainly was a driving force in the evolution of these fascinating opisthobranchs. New techniques, e.g., the oligonucleotide aptameres, could help to solve this question . We alsoIn this review it is shown that shell loss led to the evolution of a wide array of defensive strategies in Opisthobranchia. Nevertheless, it is not ruled out that some defensive mechanisms have evolved prior to complete loss of shell. This is evident when analysing the Acteonoidea. Members of this taxon have a rather thick and large shell, but also different glandular structures along the mantle fold, as well as in the mantle cavity. One working hypothesis for future research is that defensive glands evolved from simple storage organs while feeding on toxic prey. Evolution of special structures where toxic substances could be stored without further effect on the body and that functioned as a kind of excretory system could have been the prerequisite for employing these structures as defensive devices. To solve this question, thorough phylogenetic analyses are needed. Tracing toxic substances from the food into the different organs or glands by histochemistry or new analytical methods like aptameres will help to understand the tasks of these glands, and their role as excretory or as defensive organs, or as pheromone-producing organs involved in reproduction and development. Additional field and laboratory experiments with potential predators from the natural surroundings are necessary to understand the functioning of chemicals in the slugs.About 300 different species of Opisthobranchia have been collected and investigated by the authors in the past 20 years. Collecting was performed from the intertidal , to the sublitoral zones  down to depth of 1000 m (Antarctica). Collecting techniques comprised hand collecting in the intertidal and while diving, or using trawls, like the Agassiz trawl in Polar Seas. Specimens were preserved in 4 – 10 % formaldehyde/seawater for histological investigations, or 96% ethanol for molecular investigations.Littorina littorea. Due to some trivial characters, an all-zero outgroup was chosen. The characters are explained in full detail by Wägele & Klussmann-Kolb (in prep). Analyses were performed by PAUP 4.0 beta 3a  [Investigation of morphology and anatomy was performed by macroscopical and histological techniques. For histological investigations, entire animals  or parts of the animals were embedded in hydroxyethylmethacrylate for serial sectioning (2 μm). Sections were stained with toluidine blue and investigated by light-microscopy. Toluidine blue stains acid mucopolysaccharides in various shades of red to violet, whereas neutral mucopolysaccharides are staining in blue colours. Observation of living animals aided the understanding of external characters and life strategies. Data used for the phylogeny comprise 79 taxa and 110 characters based on morphology and histology. Polarity of characters was obtained by outgroup comparison with d, 1999) . ParametHW and AKK together carried out the phylogenetic analysis based on morphological and histological characters published here for the first time. HW drafted the manuscript. AKK helped to draft the manuscript. Both authors read and approved the final manuscript.Data matrix of characters – 79 taxa and 110 characters are included. N = non applicable. This implies that the character is not present and can therefore not be coded. ? = character state not known. Genus with an asterisk indicate that information on this genus is extracted from literature. Detailed information will be given elsewhere in Wägele & Klussmann-Kolb (in prep.).Click here for fileCharacters – Characters and coding of character states used for phylogenetic analysis presented in Figure Click here for file


In hens, the ovarian follicles committed to ovulation are arranged in an ordered follicular hierarchy. In standard broiler breeders hens genetically selected for high growth rate the reproductive function is clearly dysfunctional. Feed restriction is needed during reproductive development to limit the formation of excessive numbers of ovarian yellow follicles arranged in multiple hierarchies. To determine whether leptin is involved in the nutritional and reproductive interactions controlling follicular hierarchy in hens, blood leptin levels and ovarian expression of the leptin receptor mRNA were determined during follicle maturation in three chicken lines; a slow growing broiler "Label" genotype without reproductive dysfunction, a fast growing "Standard" genotype fed ad libitum or restricted and a fast growing "Experimental" line with intermediate reproductive performance levels. Whereas expression of the leptin receptor mRNA did not change in the theca, it clearly decreased with follicular differentiation in the granulosa of slow growing hens. In fast growing standard hens fed ad libitum and presenting significant reproductive dysfunction, the decrease was disrupted and dramatic up-regulation of granulosa cell expression of the leptin receptor was observed. On the other hand, feed restriction decreased the overall level of expression of the leptin receptor mRNA and restored the decrease with follicular growth. The level of expression of the leptin receptor probably modulates the action of leptin on follicular differentiation. Since blood leptin and other metabolic factors were not affected by the genotype or by nutritional state, the factors involved in the regulation of leptin receptor gene expression remain to be determined. This study demonstrates the involvement of leptin in the nutritional control of reproduction in birds. Leptin action on the ovary probably controls follicular hierarchy through the regulation of steroidogenesis. The ovary of the mature hen contains a hierarchy of yellow yolky follicles and several thousand smaller follicles from which the large yolky follicles are recruited. The yellow follicles are arranged in a size hierarchy and are committed to ovulation. In each follicle, the granulosa cells are surrounded by theca tissue and are separated from it by a basement membrane. Each compartment of the largest yellow follicles (theca and granulosa cells) can be anatomically separated to follow the individual functions of these two ovarian compartments in follicle growth and differentiation. At an early stage of follicular development the small ovarian follicles produce estrogens and androgens. As follicles begin to sequester yolk their production of estrogens from theca cells decreases to become very low at ovulation. As follicles are recruited into the yolky follicular hierarchy, estrogen and androgen production by theca cells diminishes and production of progesterone by granulosa cells increases. The largest F1 yellow follicle then attains the highest progesterone production at the time of ovulation.In most domestic animals, reproductive function is considerably affected by nutrition ,2. VarioStandard broiler breeders have been submitted to high selection pressure on growth and feed efficiency. Male traits have been favoured resulting in poorer reproductive performances of the hens. As a result of such selection, broiler breeder hens are subject to metabolic disorders and reproductive dysfunction. Overfeeding during reproductive development is associated with the formation of excessive numbers of ovarian yellow follicles which can be arranged in multiple hierarchies, with increased production of unsettable eggs -21. SeveThis study investigated the potential involvement of leptin and its receptor in ovarian abnormalities observed in broiler breeder hens fed ad libitum. We aimed to determine the evolution of leptin receptor gene expression in both granulosa and theca cells from the four largest yellow follicles of 32 week-old hens during the laying period. The effects of genotype and diet on plasma leptin levels and ovarian expression of the leptin receptor gene were also quantified. For this purpose standard broiler breeder hens fed ad libitum or feed-restricted were compared to a French "Label" genotype and a dwarf "Experimental" line. Compared to fast growing standard broiler breeders, the French "Label" is a dwarf, slow-growing broiler genotype and the "Experimental" line is a dwarf genotype with a growth potential of progeny chicks close to that of the standard broiler chicks. The "Experimental" line is specifically selected for reproductive traits and viability at partial expenses of growth performances. The "Label" line tolerates ad libitum feeding of breeders and does not have reproductive problems under ad libitum feeding whereas the «Experimental» line can be fed ad libitum on a low energy diet during the growing period but presents more reproductive problems than the Label genotype but less than the standard genotype fed ad libitum.Three lines of broiler breeder hens supplied by Hubbard primary breeder  were used in this experiment. The S line is a standard fast growing broiler, the French Label (L) line is a slow growing broiler breeder strain used for the quality market and the experimental (E) line is a broiler breeder strain bearing the "dw" dwarf gene. This strain has a decreased need for rationing. S and L hens were given the same regime in accordance with Hubbard nutritional recommendations  in fine meal form. During the growing period (0–20 weeks), half of the S hens were feed restricted (SR) on the same diet in order to match a reference body weight curve provided par Hubbard primary breeder, the other half (SA) and all the L hens were fed ad libitum. The feed intake was equivalent to 37% of the SA group up to point of lay . A speciAt 32 weeks of age, blood samples were collected from 12 hens of each experimental group  and six hens were sacrificed by an overdose of pentobarbital . The ovaries and liver were immediately removed. Granula and theca compartments from the first (F1), second (F2), third (F3) and fourth (F4) largest ovarian yellow follicles were dissected as previously described . Since S- MMLV reverse transcriptase  and random hexamer primers . cDNA was then diluted to 1:8.Total RNA was extracted from liver, granulosa and theca cells using RNA InstaPure  according to the manufacturer's recommendations. After DNAse treatment using Ambion's DNA-free kit , 2 μg of total RNA were reverse-transcribed (RT) in a final volume of 20 μl using RNAse H2, 0.2 mM dNTPs  and 0.2 μM of each forward and reverse primer. Leptin receptor forward (5'-GTC CAC GAG ATT CAT CCC AG-3') and reverse (5'-CCT GAG ATG CAG AGA TGC TC-3') primers were chosen according to the previously determined sequence of the chicken leptin receptor cDNA [For normal PCR amplification, five microliters of the RT reaction were amplified for 35 cycles in a 50 μl reaction volume containing 2.5 units of Taq DNA Polymerase , 2.5 mM MgCltor cDNA . This paReal-time RT-PCR was performed as previously described . BrieflyTotal cholesterol, phospholipid, and triglyceride plasma concentrations were calculated with "Cholesterol RTU", "Phospholipides Enzymatique PAP 150", and "Triglycérides Enzymatique PAP 150" kits  according to the manufacturer's recommendations. Plasma glucose levels were measured by the glucose oxidase method using an automated analyzer. Plasma insulin levels were determined by a radioimmunoassay with a guinea pig anti-porcine insulin antibody using chicken insulin as the standard . Plasma P < 0.05.The results of plasma lipid, glucose and hormone analyses as well as leptin receptor mRNA expression in the liver were analyzed by one-way ANOVA and means were compared by Student Newman Keuls multiple comparison test. The effects of the groups of birds , follicular rank and possible interaction on the logarithm of leptin receptor mRNA levels were tested by two-way ANOVA using the General Linear Model (GLM) procedure of SAS . An additional effect of the subject was introduced into the model in order to take into account the fact that measurements of leptin receptor mRNA expression for the different follicular ranks were performed on the same animal. Pairwise comparisons of means for each significant effect of the ANOVA were performed by Scheffe test with the least means squares statement of the GLM procedure. The level of significance was set at We demonstrated the expression of leptin receptor mRNA in granulosa and theca cells from the three genotypes fed ad libitum or restricted for the S line. The expression of leptin receptor mRNA for both ovarian cells was detected in each hierarchical yellow follicle studied F1 to F4) , they were log transformed. The resulting distribution was closer to the normality with skewness of 0.87 and kurtosis of 0.69. Variance analysis was performed on transformed data.As shown in Table Expression of leptin receptor mRNA in the theca cells remained stable during yellow follicle development, whatever the group of birds considered. Statistical analysis did not reveal any difference in leptin receptor mRNA expression between the 4 groups of birds Figure .In the liver, the expression of leptin receptor mRNA was up-regulated in S birds fed ad libitum. Feed restriction of S birds restored the level of expression of leptin receptor mRNA similar to that measured in the E and L birds Figure .At 32 weeks of age plasma leptin and insulin concentrations were found to be similar in the three genotypes. Food restriction of the standard hens did not alter plasma leptin, glucose or insulin levels (Table Several studies conducted on theca and granulosa cells have shown that leptin may have direct negative effects on ovarian steroidogenesis in various mammalian species. Leptin inhibits insulin-induced progesterone and 17β-estradiol production by isolated bovine granulosa cells  and impaUnlike mammals, progesterone in chickens is synthesized and secreted mainly by granulosa cells whereas theca cells generate estradiol . ProgestThe erratic pattern of oviposition in standard broiler breeder hens fed ad libitum has been previously demonstrated to be related to abnormal maturation of steroidogenesis, particularly in the two largest yellow follicles . Since FSince expression of its receptor was dramatically up-regulated in SA hens, leptin probably played an important role in the increased number of large yellow follicles and abnormal follicle hierarchy. However the factors involved in regulation of the expression of the leptin receptor within the hen ovary remains to be determined. Among the plasma hormones and lipids analyzed in this study only triglycerides were found to be different between strains, with a lower level in the restricted standard broiler breeder hens that were also the leanest birds. Down regulation of the expression of the leptin receptor by homologous and heterologous signals have previously been demonstrated in both mammals ,41 and cEvidence of the regulation of expression of the leptin receptor gene in the granulosa related to follicle maturation and nutritional state strongly suggest that leptin played an important local and sequential role in the dysfunction of the follicular hierarchy observed in standard broiler breeder hens fed ad libitum. This study suggests that the level of expression of the leptin receptor regulates the action of its ligand at the level of the ovary. This provides an interesting perspective to understanding the physiological role of leptin in the ovary.


There is a well known relationship between short sleep duration and high body mass index. A new study suggests that the missing link could be the appetite regulating hormones leptin and ghrelin. PLoS Medicine [There is a well-documented relationship between short sleep duration and high body mass index (BMI). In the largest study, a survey on sleep duration and frequency of insomnia in more than 1.1 million participants, increasing BMI occurred for habitual sleep amounts below 7–8 hours . The mecMedicine  adds to Leptin, a peptide hormone secreted from white adipocytes, is implicated in the regulation of food intake and energy balance. The hormone acts on the central nervous system, in particular the hypothalamus, suppressing food intake and stimulating energy expenditure. Leptin production is primarily regulated by insulin-induced changes in adipocyte metabolism—its secretion levels correlate with adipocyte mass and lipid loads.Leptin promotes inflammation. The hormone provides an interesting link between obesity and pathophysiological processes such as insulin resistance and atherosclerosis, and disorders such as autoimmune and cardiovascular diseases and the metabolic syndrome. Increased serum leptin levels in obesity and metabolic syndrome support the view that these disorders are in fact low-grade systemic inflammatory diseases, characterized by increased concentrations of proinflammatory cytokines like interleukin-6, tumor necrosis factor-α and leptin. Leptin's proinflammatory role suggests that it may link energy homeostasis to the immune system ,5.GhreliIn this study of 1,024 participants in the population-based Wisconsin Sleep Cohort Study , Mignot These findings confirm earlier clinical reports on the effects of sleep deprivation and extend them to include naturalistic sleep in a large, community-based population. The study provides an exciting addition to the growing literature showing relationships between sleep curtailment, metabolic hormones, and metabolic disorders (including obesity). The data have important implications for our understanding of obesity and related disorders in the general population, with one caveat: the study population was enriched with snorers, making the results less applicable to a general population.Mignot and colleagues' data are in accord with human and animal studies that show that experimental curtailment of sleep leads to lower levels of leptin ,9,10,11 But there is also evidence of opposite effects—that is, that administration of leptin  and ghreOverall, the available studies suggest the presence of reciprocal interactions between metabolic hormones and sleep, relationships that are poorly understood at present. Does sleep interact with metabolic hormones directly or via intervening factors such as sleep-related breathing disorders? Patients with obstructive sleep apnea have impaired sleep and higher ghrelin levels than BMI-matched controls, and treatment with continuous positive airway pressure reduces ghrelin to control levels . AlthougThere is a clear need for well-controlled, population-based studies that allow us to examine multiple relevant factors simultaneously. The present study highlights the importance of shortened sleep in relation to obesity, leptin, and ghrelin, a good start toward this goal.Many other important questions remain, such as the roles that other hormones, cytokines, and SDB play in obesity. Many of the unanswered questions have important implications for public health. For example, diabetes, visceral obesity, hypertension, and hyperinsulinemia commonly aggregate together in large populations, and are considered a “metabolic syndrome” that has been linked to SDB  and to iThe possible role of sleep restriction in autoimmune and inflammatory disorders is of particular interest in light of recent findings linking immune function with ghrelin and leptin. Ghrelin and its receptor are expressed in human T-lymphocytes, where they can inhibit cytokine activation, including interleukins, tumor necrosis factor-α and leptin . Convers