Summary

Dreams have been known to mankind from time immemorial, while rapid eye movement sleep (REMS) has been objectively defined by characteristic electrophysiological signals since the mid twentieth century only. In the absence of better objective criteria, modern experimental sleep neurobiologists have objectively identified the dream state of a subject with REMS; thus, the dream state and REMS have often been used synonymously. There are reasons to believe that those states are not exclusively correlated to each other, rather they are independent phenomena that are often expressed simultaneously; however, neurobiological explanations are still lacking. In an attempt to better understand the relationship between them, we combined findings from objective science such as non-locality in physics with that of subjective science such as the phenomenon of consciousness. We explored the wisdom in the Upanishads, especially those instances where these ancient writings refer to sleep, dream, and states of consciousness, and attempted to offer an explanation based on modern experimental science. Our search led us towards a conceptual novelty in proposing the existence of an all-inclusive basal ground state (T or Turiya), which possibly equates to very slow waves in the electroencephalogram (EEG), during which waking, dream, non-REMS (NREMS), and REMS express apparently as independent phenomena, though overlapping to various degrees on many occasions. The proposed hypothesis and model, unlike several other earlier ones, is based on known and rational physiological principles, and hence is amenable to experimental verification.

Summary

Cognitive neuroscience continues to build meaningful connections between affective behavior and human brain function. Within the biological sciences, a similar renaissance has taken place, focusing on the role of sleep in various neurocognitive processes and, most recently, the interaction between sleep and emotional regulation. In this chapter, I survey an array of diverse findings across basic and clinical research domains, resulting in a convergent view of sleep-dependent emotional brain processing. Based on the unique neurobiology of sleep, I outline a model describing the overnight modulation of affective neural systems and the (re)processing of recent emotional experiences, both of which appear to redress the appropriate next-day reactivity of limbic and associated autonomic networks. Furthermore, an REM sleep hypothesis of emotional-memory processing is proposed, the implications of which may provide brain-based insights into the association between sleep abnormalities and the initiation and maintenance of mood disturbances.

Summary

Neuroanatomical, neurochemical, genetic, and neuropharmacological evidence presently indicates a role for histamine (HA) in the control of behavioral states. The known neuroanatomical connections of the HA-ergic pathways resemble those of the ascending noradrenergic and serotonergic components of the reticular activating system. Also, the arousing effect of intracerebroventricular (icv) HA administration indicates an important role for HA in this system as a major determinant of the waking state. This is further supported by findings in which 2-(3-trifluoromethylphenyl)histamine, the selective H1 receptor agonist, and thioperamide, the H3 receptor antagonist, increase waking while the HA synthesis inhibitor α-FMH, the H1 receptor antagonists mepyramine, diphenhydramine, chlorpheniramine, and promethazine, and the H3 receptor agonist AMH produce the opposite effects.

It has been proposed that HA may act to modulate REM sleep, such that inhibition of HA functional activity would be followed by increased amounts of REM sleep (permissive role). Accordingly, during REM sleep HA-containing neurons become silent. Moreover, rats treated with α-FMH and HD-KO mice show a significant increase of REM sleep. However, stimulation or blockade of the H1 or H3 receptor suppresses REM sleep. These seemingly conflicting sets of data could be partly related to the lack of specificity of drugs that modify HA transmission. However, experimental manipulations involving direct interactions with receptors may not necessarily have the same consequences for REM sleep as would manipulations that result in reduced HA availability. In this respect, the suppression of REM sleep after stimulation of H1 receptors could be related to the activation of GABAergic interneurons located within and around the LDT/PPT that express these receptors. On the other hand, the reduction of REM sleep after activation of H3 heteroreceptors located in cholinergic and glutamatergic neurons of the LDT/PPT and the mPRF involved in the induction and maintenance of REM sleep could be related to the inhibition of the release of ACh and GLU.

Summary

Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the adult mammalian brain. GABA receptors are ubiquitous and are highly expressed in many brain areas modulating states of sleep and wakefulness. The consistent finding that drugs that enhance GABAergic transmission also enhance sleep supports the conclusion that endogenous GABA promotes sleep. The effects of GABA on sleep, however, vary as a function of brain region. GABAergic transmission in the pontine reticular formation, the tuberomammillary region of the posterior hypothalamus, and the ventrolateral part of the periaqueductal gray has been shown to promote wakefulness, non-rapid eye movement (NREM) sleep, or rapid eye movement (REM) sleep, respectively. The finding that hypothalamic GABA-containing neurons project to the dorsal raphe nucleus, locus coeruleus, and pontine reticular formation encourages future studies aiming to determine the extent to which these GABAergic neurons play a causal role in the generation and maintenance of REM sleep. Functional neuroanatomical studies have identified neural pathways that contribute to REM-sleep generation. Simultaneous, in vivo single-cell recordings of identified GABAergic neurons combined with direct measures of endogenous GABA offer a productive approach for gaining future insights.

Summary

There have been many studies of the atonia of REM sleep and of its effects on the respiratory system. In contrast, excitatory processes that affect the respiratory system in REM sleep are poorly understood. Nevertheless, these processes may be the main determinants of respiratory behavior in REM sleep (e.g., the higher rate of breathing). In this chapter, findings relevant to excitation of the respiratory system in REM sleep are presented and discussed.

1. Most medullary respiratory neurons are more active in REM sleep than in NREM sleep, and both diaphragmatic and hypoglossal motor neurons reportedly have greater overall activity in REM sleep than in NREM sleep.

2. The source of the excitation of respiratory neurons and motor neurons in REM sleep is internal because excitation is seen even when mechanical and chemical respiratory stimuli are removed or held constant and respiratory drive is eliminated.

3. […]

Summary

In this chapter, we will review the recent developments relevant to understanding the neural systems that regulate REM sleep. We will review the initial discovery of REM sleep, followed by a brief description of the polysomnographic characterization of REM sleep. Our discussion will continue with a review of the principal brain-stem executive neurons responsible for REM generation. Pontine reticular formation neurons are involved in the expression of the majority of REM-sleep phenomena, including low-amplitude/high-frequency cortical EEG, the hippocampal theta rhythm, PGO waves/P-waves, and muscle atonia. Cholinergic brain-stem neurons are REM-on, promoting REM sleep; and serotonergic and noradrenergic brain-stem neurons are REM-off, suppressing REM sleep. GABAergic and glutamatergic mechanisms are also integral to REM sleep control. We will also survey the prominent nuclei of the midbrain and forebrain that promote, but do not generate, REM-sleep expression. The conclusion of this chapter will provide a review of three prominent models of REM-sleep regulation: the reciprocal-interaction model; the REM sleep “flip-flop” circuit model; and the revised model of paradoxical (REM) sleep control proposed by Luppi and colleagues.

Summary

Rapid eye movement (REM) sleep is a behavioral state initiated and maintained by the interaction of multiple neurotransmitters, including acetylcholine. Numerous studies confirm that cholinergic transmission contributes to the regulation of REM sleep. Cholinergic signaling in the basal forebrain modulates the cortical activation that occurs during REM sleep. It is also well documented that cholinergic transmission in the pontine reticular formation plays a role in REM-sleep generation and maintenance. This chapter highlights evidence supporting that acetylcholine regulates REM sleep and focuses on the mechanisms that regulate cholinergic transmission within arousal-regulating brain regions. The chapter also considers how other arousal-regulating neurotransmitters, such as hypocretin, GABA, adenosine, and opioids modulate both cholinergic signaling and REM sleep. A greater understanding of how neurotransmitter interactions regulate REM sleep will further clarify the role of cholinergic transmission in REM-sleep generation. Employing new analytical techniques will facilitate understanding the effects of multiple neurotransmitter interactions on physiologically relevant time scales. Capillary electrophoresis and biosensors, which can quantify neurochemical changes on the order of seconds, will allow insights that could not be achieved with more conventional sampling techniques.

Summary

The need to study dreaming is the promise that it will unlock the mystery of psychosis and perhaps contribute to resolving the mind–body problem. A number of questions arise in the study of dreams. Can dreams be reliably measured? Do dreams reflect differences where we know psychological differences exist? Do dreams change when there is a change in the state of the dreamer? Do dreams change across the night and across the REM period? Are the dreams of individuals different from one another? And are dreams of an individual different from night to night? Are dreams related to the waking life of the dreamer? Are dreams random or orderly? These are the questions this chapter undertakes to address.

Summary

The brain-stem cholinergic neurons, having higher activity during rapid eye movement (REM) sleep, located in several isolated nuclei are known as REM-on neurons. In contrast, the monoaminergic neurons in the brain stem and in the forebrain areas exhibit higher activity during wakefulness, almost completely cease their firing during REM sleep and have been termed as REM-off neurons. The norepinephrin (NE)-ergic neurons located in the locus coeruleus (LC) could be the negative REM sleep-executive neurons and their cessation during REM sleep seems to be obligatory for its occurrence. Our findings that the wakefulness-promoting neurons are inhibitory to REM-on neurons and excitatory to the REM-off neurons led us to suggest that the wakefulness-related neurons do not allow REM sleep to occur and cessation of REM-off neurons is a necessity for the generation of REM sleep. The caudal brain-stem reticular formation (CRF), which induces cortical synchronization, facilitates the activity of REM-on neurons. However, the hypothalamic non-REM sleep-related neurons do not seem to have significant effect on the spontaneous activity of the REM-on neurons, although they may be indirectly modulating REM sleep. Taken together these findings suggest that normally waking neurons do not allow REM sleep to appear; at a certain depth of non-REM sleep the CRF facilitates the onset of REM sleep and re-activation of the wake-active neurons in the brain stem is requisite for its termination.

The light field

The spectral power distribution of the light emitted by the Sun is almost constant. The variations in daylight (Figure 13.1) that we experience over the course of a day and with changes in seasons are due to the interaction of sunlight with the Earth's atmosphere (Henderson, 1977). The resulting spectral distribution of daylight across the sky is typically spatially inhomogeneous and constantly changing (Lee and Hernández-Andrés, 2005a,b). The light arriving at each small patch of surface in the scene depends in general on the patch's location and orientation in the scene. Furthermore, objects in the scene create shadows or act as secondary light sources, adding further complexity to the light field (Gershun, 1936/1939; see also Adelson and Bergen, 1991) that describes the spectral power distribution of the light arriving from every direction at every point in the scene. The light field captures what a radiospectrophotometer placed at each point in the scene, pointing in all possible directions, would record (Figure 13.2).

When the light field is inhomogeneous, the light absorbed and reradiated by a matte smooth surface patch can vary markedly with the orientation or location of the patch in the scene.

In Figure 13.3, for example, we illustrate the wide range of the light emitted by identical rectangular achromatic matte surfaces at many orientations, illuminated by a distant neutral, collimated light source.

Introduction

In 1997, we were designing experiments to assess the stability of correspondence between points in the two retinas and the phenomenon of stereoscopic hysteresis (Diner and Fender, 1987; Fender and Julesz, 1967). As part of these experiments, we presented binocularly uncorrelated random-dot images to the two eyes in a stereoscope. Binocularly uncorrelated images produce a percept of noisy, incoherent depth, since there is no consistent disparity signal. However, when we moved the images in the two eyes laterally in opposing directions we obtained a compelling sense of coherent motion in depth. When the display was stopped, the stimulus again appeared as noisy depth. We quickly realized that the motion-in-depth percept was consistent with dichoptic motion cues in the stimulus. Thus, a compelling sense of changing depth can be supported by a stimulus that produces no coherent static depth. This was quite surprising, since experiments several years earlier had suggested that stereoscopic motion-in-depth perception could be fully explained by changes in disparity between correlated images. Unknown to us, Shioiri and colleagues had made similar findings, which they reported at the same Association for Research in Vision and Opthalmology (ARVO) meeting where we first presented our findings (Shioiri et al., 1998, 2000), although we found out they had also presented them earlier at a meeting in Japan. We performed a number of experiments on this phenomenon, reported as conference abstracts (Allison et al., 1998; Howard et al., 1998) that were subsequently cited.

The concept of illusion

When is an illusion not an illusion? If the definition of an illusion is something like “a lack of correspondence between the ‘input’ and what we perceive,” then it necessarily depends on how we define the “input.” Clearly, this shouldn't refer to the characteristics of the proximal image, because if it did, we would have to treat all perceptual constancies as illusions since there is invariably a lack of correspondence between the retinal size, shape, or wavelength and what we see. Helmholtz (1910) was well aware of this when he wrote: “I am myself disposed to think that neither the size, form and position of the real retina nor the distortions of the image projected on it matter at all, as long as the image is sharply defined all over.” If the “input” is not to be defined in terms of the proximal image, then perhaps it should be defined in terms of the properties of the distal image – the real-world situation that creates a particular retinal input. This sounds better, but a problem arises when the patterns of light reaching the eyes have not been created by the real world but instead by some device that precisely mimics those patterns of light. For example, our perception of structure-from-motion that has been created by patterns of motion on a flat computer screen (a kinetic depth effect, or KDE) would have to regarded as an illusion, as would the perception of depth created by a pair of 2D images in a stereoscope.

Introduction

Navigation tasks, and particularly robot navigation, are tasks that are closely associated with data collection. Even a tourist on holiday devotes extensive effort to reportage: the collection of images, narratives or recollections that provide a synopsis of the journey. Several years ago, the term vacation snapshot problem was coined to refer to the challenge of generating a sampling and navigation strategy (Bourque and Dudek, 2000). The notion of a navigation summary refers to a class of solutions to this problem that capture the diversity of sensor readings, and in particular images, experienced during an excursion without allowing for active alteration to the path being followed.

An ideal navigation summary consists of a small set of images which are characteristic of the visual appearance of a robot's trajectory and capture the essence of what was observed. These images represent not only the mean appearance of the trajectory but also its surprises.

In the context of this chapter, we define a navigation summary to be a set of images (Figure 11.1) which minimizes surprise in the observation of the world. In Section 11.3, we present an information-theory-based formulation of surprise, suitable for the purpose of generating summaries. The decisions of selecting summary images can be made either offline or online. In this chapter we will discuss both versions of the problem, and present experimental results which highlight differences between the corresponding methods.

In Section 11.4, we present two different offline strategies (Figure 11.2) for picking the summary images.

Early observations by Leonardo da Vinci (c. 1508) noted that the two eyes can see different parts of the background at the edges of occluding surfaces. This is illustrated in Leonardo's drawing (Figure 3.1) and for two cases in Figure 3.2. In Figure 3.2a, an occluder hides the dotted region of background from both eyes, but there is a region on the right which only the right eye can see and a region on the left which only the left eye can see. Figure 3.2b shows a similar effect of looking through an aperture. In this case, a region on the left of the background seen through the aperture is visible to the right eye and vice versa. It is only since the early 1990s or so that there has been any serious investigation of the perceptual effects of such monocular occlusions, and a whole new set of binocular phenomena, involving the interaction of binocular and monocular elements in determining spatial layout, have been demonstrated and investigated (Harris and Wilcox, 2009). In this chapter, I shall concentrate on four novel phenomena that exemplify different ways in which unpaired regions influence binocular spatial layout.

1. (1) Da Vinci stereopsis. This will be defined as the perception of monocular targets in depth behind (or camouflaged against) a binocular surface according to constraints such as those shown in Figure 3.2.

2. (2) Monocular-gap stereopsis. In this case, monocular regions of background influence the perceived depth of binocular surfaces.

3. […]

Introduction

Estimating the three-dimensional (3D) structure of the environment is challenging because the third dimension – depth – is not directly available in the retinal images. This “inverse optics problem” has for years been a core area in vision science (Berkeley, 1709; Helmholtz, 1867). The traditional approach to studying depth perception defines “cues” – identifiable sources of depth information – that could in principle provide useful information. This approach can be summarized with a depth-cue taxonomy, a categorization of potential cues and the sort of depth information they provide (Palmer, 1999). The categorization is usually based on a geometric analysis of the relationship between scene properties and the retinal images they produce.

The relationship between the values of depth cues and the 3D structure of the viewed scene is always uncertain, and the uncertainty has two general causes. The first is noise in the measurements by the visual system. For example, the estimation of depth from disparity is uncertain because of internal errors in measuring retinal disparity (Cormack et al., 1997) and eye position (Backus et al., 1999). The second cause is the uncertain relationship between the properties of the external environment and retinal images. For example, the estimation of depth from aerial perspective is uncertain because various external properties – for example, the current properties of the atmosphere and the illumination and reflectance properties of the object – affect the contrast, saturation, and hue of the retinal image (Fry et al., 1949).