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Neuroscience 4

Neuroscience 4

1.) The World in Your Head: A Gestalt View of the Mechanism of Quantum Experience - Steven Lehar

2.) The Quantum Brain - Jeffrey Satinover - Useful for discussion of quantum coherence in protein

3.) Brain Chat (Studies relevant to global workspace theory)

4.) The secret power of the cell - Brain J. Ford - Argues that the capacities of the brain are based on the processing within individual neurons.

5.) Neural mechanisms of autonomic, affective and cognitive integration - Hugo D. Critchley - Discusses basis of subjective experience of emotions

6.) Explaining the Brain - Carl F. Craver - Argues that much of 20th century philosophy has the wrong approach to interpreting neuroscience.

1.)

The World in Your Head: A Gestalt View of the Mechanism of Conscious Experience

Steven Lehar, Schepens Eye Research Institute

Lawrence Erlbaum (2003)

INTRODUCTION: Lehar makes a good case against the computer/AI model of the brain, by highlighting the inability of computers to differentiate the edges needed to construct a model of the world, from the mass of less important input. He contrasts this with the ability of biological vision to deduce information from very flimsy inputs. The Gestalt methods suggested for achieving what the brain can do are not entirely convincing, as a means of sorting the mass of data input, and thus avoiding the combinatorial explosions implied by the requirements of visual perceptions. In this respect, a quantum computing approach might look to have a greater chance of success. Further to this, a weakness of the book is the lack of much attempt to relate what is proposed to the physical components and processing of the brain.

Lehar approaches consciousness from the angle of the relationship between visual image processing and artificial intelligence (AI). A computer has all the data relative to an image in the form of numerical data. However, turning this into usable information in AI/robotics has proved an intractable problem. Computers can detect features such as edges, but the problem is that they can detect too many of such features. Their edge detection includes details of texture, surface fragmentation and shadows, but fails to pick out those edges that are relevant for the outlines or volumes of an object. Further, there is no apparent algorithm to deal with occluded objects, where a small object obstructs the view of part of a larger object, but it can be deduced that the larger object continues behind the smaller object. This is taken to mean that the information of global significance for understanding the image is not available in the local edges.

Computers have problems with the spatial structure of visual scenes, and as a result difficulty in navigating in an environment of irregular forms, which, by contrast, present little problem for biological vision. Lehar points out that the retinal image is two-dimensional, but is perceived as three-dimensional, and that therefore the three-dimensional depth of the image must be the result of cortical processing. A basic function of visual perception is argued to be the transformation from a two-dimensional retinal image to a three-dimensional perception in the brain. Apart from inserting spatial structure into an initially two-dimensional image, the brain must also decompose this image into coherent objects with volume within the spatial structure. From this it is argued that the brain must operate a spatial algorithm, in order to produce this three-dimensional image. What computers have had difficulty in achieving is not receiving the visual data, but in developing the sort of processing that allows the brain to turn this data into a conscious image.

The literature relative to these problems concentrates on restricted domains, with separate algorithms for extracting shape from shading, for motion or for lines. However, the problem of dealing with shape of the conformation of objects that reflect light has remained largely unresolved. This divergence in relative performance is argued to show that the basis of biological and computer vision are very different from one another.

Conscious images: Lehar takes the view that the conscious image is assembled in the brain, in response to data from the external world. This is described as 'indirect realism,' in contrast to 'direct realism' or 'naive realism', in which it is believed that we perceive the external world as it actually is. The author thinks that discussions in neuroscience are often implicitly based on direct realism, but he argues that this view is based on false assumptions. The visual experience is at odds with scientific reality, because the subjective world is experienced, as if it were outside the brain, whereas visual processing occurs inside the brain. The causal chain of vision is one, in which the brain can only process material that has already been picked up by the sensory organs. Consciousness is therefore necessarily confined to the experience of internally constructed models. Lehar goes back to Kant, who distinguishes between the 'nouminal' world of light signals etc. and the phenomenal world of internal conscious perception. The 'nouminal' world is only perceived within the phenomenal world.

The author argues that the properties of subjective experience are inconsistent with the present neuroscientific thinking, based on the semi-independent sequential operation of billions of individual neurons. In contrast, our experience is mainly of stable and solid volumes, rather than billions of abstract features. The author accuses the neuroscientific community of evading this problem by assuming the 'naive realism' view, and ignoring subjective experience. This attitude is partly blamed on the mid-twentieth century advent of single-cell recording, which shifted the emphasis from assembly-wide features towards single-cell features. In the same period, the digital computer became a major part of technology, and was seen as an analogy of the brain. At this stage, AI researchers thought that they had the problem of vision solved, and that they could implement robotic vision without paying any attention to biological systems.

Famous Dalmatian: The author discusses the well-known picture of a Dalmatian dog against a speckled background. Much of the dog is missing, and some of the edges that are there are locally indistinguishable from the background. Much of the edge of the dog is missing and some of the edges that are there are locally indistinguishable from the background. The main point about this is that the local information does not allow the observer to distinguish the dog from the background, but when the picture is viewed as a whole, the dog is clearly distinguishable. Lehar argues that this indicates that perception is based on global brain activity, rather than the sequential processing of individual neurons. He claims that no algorithm has ever come close to handling the ambiguity of the Dalmatian dog picture. Furthermore, the picture is viewed as demonstrating, in exaggerated fashion, the principles that underlie biological visual processing. One argument tries to evade this conclusion, by suggesting that an image such as this is a special case that does not apply to normal visual processing. However, Lehar counters that studies that restrict the view of pictures to just a few edges show that humans cannot distinguish between edges that are important to the outline or form of objects, and edges that are just texture or shadows.

Kaniza triangle: Lehar discusses visual figures, such as the Kaniza triangle, where the mind automatically perceives a triangle, although all that is physically there on the printed paper is three black Pacman features. Thus, the observer perceives edges and a brighter white ground than the surrounding area, where neither exists on the paper. Again, this is argued to be a global processing of the image, rather than derived from the examination of individual edges.

Rubin vase/faces: The same is true of other well-known examples such as the Rubin face/vase illusion. A black figure on a page may be perceived, as either a vase or the profiles of two faces opposite one another. The brain jumps from one perception to the other, without ever offering a hybrid picture, and can as quickly reverse its perception. It is argued from this that visual recognition is not the result of feed-forward processing of a visual input leading to a perceptual output, as is often assumed in computer models of the brain, but instead involves a dynamic process that is not completely stable.

Invariant perception: Lehar also discusses the problem of the invariance of our perception of objects, in that they can be recognised from different angles and in different lights, as the same objects, in a way that is not easily achievable by the analysis of individual edges. Conventional computing could only manage this by having a detector for each possible position, which could produce a combinatorial explosion or NP hard problem, where classical computing might only resolve the problem in a time that was longer than the life of the universe. There have been suggestions that local elements of the object are first recognised, and later put together, but this does not take into account instances, where what are actually different elements may form an image of the same object.

Visual agnosia: The distinction between being able to detect individual features, and gaining a practically useful model of the world can also be demonstrated from human pathology in the form of visual agnosia. There are two forms of this; in a condition known as apperceptive agnosia, the patient can see individual objects, but cannot integrate these features into a spatially coherent three-dimensional whole. The opposite condition is associative agnosia agnosia, where the patient perceives a coherent world, but cannot identify individual objects. This medical finding is argued to contradict the 'naive realism' claim that the brain is just seeing what is out in the world, in which case the whole spatial environment should be perceived.

Gestalt theory attempts to solve the problem of visual recognition by parallel processing, in which the solutions to each part of the visual recognition problem depend on one another, and thus constrain the possible solutions for one another, thus closing in on a single solution. Lehar also proposes the idea of 'harmonic resonance'. This involves resonance between different modules in the brain, with resonance ultimately being communicated to all the relevant systems in the brain. This is seen as a solution to the 'binding problem' or an explanation of the unity of different modalities in conscious experience. This of course relates to the EEG recordings of gamma frequency synchrony in the brain.

Conclusion: It is not clear that these Gestalt proposals involve sufficient processing capacity to overcome the likely combinatorial explosions/NP hard problems implied by perception. Lehar does relatively little to link his ideas to the physical components and processing of the brain. From the look of it, a quantum computing process would have more chance of bridging the gap between classical computing capacity and the requirements of visual perception as highlighted by Lehar.

2.)

The Quantum Brain

Jeffrey Satinover

John Wiley & Sons (2001)

INTRODUCTION: This book is mainly of interest for its discussion of quantum features and particularly quantum tunnelling in protein, an area which more mainstream science popularisations are not often keen to discuss. Since Satinover wrote this book, the discovery of functional room-temperature, quantum coherence in photosynthetic protein has brought the importance of quantum activity in protein more to the fore. Apart from this discussion about protein, Satinover is mainly interested in developing the idea of quantum ramdomness driving chaos-based patterns of macroscopic neural processing. Although, he appears to derive a good part of his material from Penrose and Hameroff, he is more concerned with information processing than consciousness, and chooses to dismiss the Penrose/Hameroff consciousness theory without a proper discussion of the matter.

The researcher, John Hopfield, demonstrated that a type of neural net, now known as a Hopfield network, has an identical mathematical description to magnetic systems called spin glasses. These are magnetic substances that demonstrate collective behaviour, without the need for external orchestration.

Satinover discusses a stable arrangement of magnets, in which opposite poles are holding the magnets apart. If the system is vigorously disturbed, this stable arrangement breaks down, but after a time, the system will settle into a new stable arrangement, to which it can always return after minor disturbances, although now there are more magnets than previously that are not aligned in parallel.

Ferromagnetic materials such as iron have many small areas or domains, in which electron spins (effectively magnetic charges) are aligned. But the domains have many different alignments, and these electrons are in a precarious position, where they can easily be flipped into a new alignment. P. These ferromagnetic groups of neighbouring spins are mathematically similar to the excitatory (mainly glutamate) connections between neurons. However, in addition to spins that try to align in the same direction, there are antiferromagnetic systems that align in alternate directions, and these turn out to be mathematically similar to the inhibitory (mainly GABA) connections between neurons.

Materials that have ferromagnetic and antiferromagnetic domains mixed are referred to as spin glasses. This is a random mix of ferromagnetic and antiferromagnetic material, where adjacent electrons competing to align, or flip one another, are always on the edge of change, and are argued to resemble the analogous excitatory and inhibitory mix of neurons. A spin glass system has more than one 'best arrangement' and is similar to a brain, in that it can store new data without erasing existing material.

The brain and chaos: The brain is here regarded as a self-organising system that mathematically resembles the spin glass structure discussed above. However, it is pointed out that self-governing ensembles have a tendency towards chaos, meaning not actual disorder but deterministic chaos. The development of the system could in principle be described by an algorithm, but because this would require such a vast amount of information, the system is in practice unpredictable. The system does repeat patterns or behaviour, but they are similar, rather than exactly the same. It is suggested here that quantum randomness in areas of the brain might be amplified by chaos.

Microtubules: Satinover is interested in the possible involvement of microtubules in brain processing. The cytoskeleton, of which microtubules are the most important component, is considered to be uniquely suited to carry signals, because it spans the whole cell. The cytoskeleton used to be viewed, mainly as a support structure, but more recent studies (1&2) show that they are also signalling mechanisms. The self-organised activity of microtubules and associated proteins and filaments, is seen in recent visualisation studies, to control the mobility of cells and the configuration of dendrites, through which signals enter the cell. This structure is likened to the update rules governing interaction between neighbouring units which drives the evolution of so-called cellular automata from simplicity to complexity. Within the hexagonal tubulin grid that makes up the microtubule, each tubulin has six immediate neighbours, an arrangement of the same type as those conjectured by cellular automatons. The microtubule network as a whole is said to be harmonious and suitable for the transmission of vibrations. It is suggested that the neuron network of the brain is linked to the internal microtubule processing within neurons. The microtubule network is viewed as analagous to the Hopfield network and spin glass systems discussed above.

Quantum aspects of protein: The best section of this book is the discussion of the quantum aspects of protein, the basic building blocks of organic matter. A protein is a string of a hundred or more amino acid molecules. The amino acids are attached to one another by bridges called peptides, so that the protein is a macromolecule. Each amino acid has a unique shape, and a unique distribution of electric charge. For a protein to carry out its necessary functions within an organism, it must fold in a precise manner, at or very close to, the energy minima.

The problem with this system is that there can be trillions of similar ways for a protein to fold. Proteins can assume a very large number of conformational states, with a large number of energy minima. Despite this huge number of possible states, proteins can, within seconds, find the correct conformations and energy minima, which are also the most functional configurations.

There is, as yet, no clear indication as to how this is to be achieved. Random searching for a minimum energy conformation would take longer than the life of the universe to reach a solution. The position is not much better for supercomputers, where despite years of generous funding, it has proved impossible to calculate the minimum energy configuration for even a short chain of amino acids. This is known as the protein-folding problem. DNA encodes the primary structure of the protein, which is the sequence of the amino acids. At a secondary stage, the amino acid chains are formed into particular shapes, such as helices. At the tertiary stage, sections of helices and other shapes are brought together, and folded into a particular configuration of electric charges. It is this last stage of folding that constitutes the protein-folding problem. Satinover argues that the problem of protein folding is similar to the means, by which spin glasses reach alignment, with a huge number of axes, along which protein must flip.

Satinover explains that to achieve what they do proteins use quantum features. Some of the electrons in the protein are in a wave or superposed state, with the wave extending over a considerable distance through the protein. This is referred to as tunnelling, with the wave form of the electron able to penetrate into regions that the point-particle form of the electron cannot reach. This electron tunnelling can be exceptionally sensitive to minor couplings. In helical structures in particular, the influence of quantum tunnelling falls off only slowly with distance. The tunnelling of electrons triggers conformational changes in protein, and further to this, conformational changes in protein trigger yet more quantum tunnelling. Water is vital to living organisms, and it also exhibits tunnelling between molecules. The tunnelling process orders water into chiral (left and right-handed) clusters, which play an important role in protein folding. Tunnelling makes low-energy states more accessible within protein, and this probably proved to be an adaptive advantage, from an early stage in evolution. Studies by Peter Wolynes at the Centre of Biophysics and Computational Biology and also at the National Centre for Supercomputing Applications have simulated the tunnelling process in protein, showing that theories of spin glasses can be applied to the protein-folding problem, and also showing that tunnelling makes systems more efficient, particularly in the search for minimum energy levels. The advantage of quantum processing is that an electron can simultaneously search many routes for the most efficient route.

The existence of quantum tunnelling in protein raises the question of the vulnerability of quantum processes to decoherence. In general, the movement of molecules as a function of heat serves to disrupt quantum tunnelling. However, it is claimed that the opposite is true in the case of protein. Proteins also exhibit phonons that represent travelling, classical, mechanical coherence in protein. These are claimed to enhance tunnelling distance. This represents a mutually reinforcing relationship between classical, mechanical vibrations and quantum activity, so as to enhance short-lived coherences. Decoherence of superpositions may happen rapidly, but may collapse to just the right classical state, which also puts the protein into the right condition for the next burst of quantum coherence. Studies performed a number of years after Satinover's book look to have demonstrated just such a pattern of decline and resurgence in coherence, where quantum coherence has been demonstrated in photosynthetic proteins.

Tunnelling by hydrogen protons has been found to be essential for enzymatic action. Here again, there is an interaction between tunnelling protein conformation and more tunnelling, and here too, studies show that classical vibrations, rather than disrupting tunnelling, are actually required for tunnelling. Thus proteins, merely be absorbing heat from the environment, can initiate computational processing. Life here seems to use quantum effects to extract order from disorder. A study by Judith Klinman (3.) at Berkeley showed that hydrogen proton tunnelling in protons can occur at room temperature.

Subsequent to its discussion of quantum effects in protein, this book becomes less interesting. Ultimately, it is commited to 'the brain's a deterministic computer doctrine', albeit a computer driven by quantum randomness feeding into deterministic chaos. In essence the writer is concerned with quantum/chaotic information processing rather than consciousness.

Satinover appears to derive quite a lot from Penrose and Hameroff, but as is often the case, intellectual rigour goes out of the window, when discussing this theory. The whole theory appears to be dismissed solely on the basis of the Hameroff side of the theory, which is to do with implementation in the brain, rather than Penrose's original reasons for looking to quantum theory. Furthermore, if one is to argue against this theory on the basis of decoherence, as happens here, it is necessary to discuss the possibility of shielding of quantum processes, or the possible involvement in consciousness of the shorter lived coherences discussed by Satinover. This discussion is lacking in this book.

References:-
1.) Tuszynski, J. et al (1998) - Information processing and quantum computation in microtubules - Philosophical Transactions of the Royal Society 2.) Brown, J. & Tuszynski, J. (1997) - Dipole interactions in axonal microtubules as a mechanism of signal perception - Physical Review E 56, pp. 5834-40
3.) Bahnson, B. & Klinman, J. (1995) - Hydrogen Tunnelling in Enzymes Catalysis - Methods in Enzymology, 249, pp. 373-397
4.) Wolynes, P. (1992) - Spin glass ideas and the protein folding problem - In: Spin Glasses and Biology, pp. 225-6 - Ed. Stein, D. - World Scientific Publishing
5.) Farid, R. et al (1993) - Electron transfer in proteins - Current Opinion in Structural Biology, 3, p.225
6.) Stuchebrukov, A. (1996) - Tunnelling currents in electron transfer reactions in proteins - Journal of Chemical Physics, 105, pp. 10819-10829
7.) Balabin, I. & Onuchic, J. (1998) - A new framework for electron transfer calculation - Journal of Physical Chemical B, 102, pp. 7497-7596
8.) Ogawa, M. et al (1993) - Distance dependence of intramolecular electron transfer rates across oligoprolines - Journal of Physical Chemistry, 97, pp. 11456-11463
9.) Balabin, I. & Onuchic, J. (1996) - Connection between simple models and quantum mechanical models for electron transfer tunnelling - Journal of Physical Chemistry, 100, pp. 11573-11580
10.) Basran, J., Sutcliffe, J. & Scrutton, N. (1999) - Enzymatic H-transfers requires vibration driven exteme tunnelling - Biochemistry, 38, pp. 3218-3222
11.) Wolynes, P. & Kuki, A. - Electron transfer paths in protein - National Center for Supercomputing Applications

3.)

Brain Chat (Studies relevant to global workspace theory)

Anil Ananthaswamy

New Scientist, 20 March 2010 (and references to Nature Neuroscience and PLOS Biology)

Baars global workspace theory proposed that unconscious processing occurs locally in the brain, for instance just in the visual cortex or some comparable region. Conscious processing is suggested to always involve a neuronal assembly distributed across more than one region of the brain. Baars refers to this arrangement as the global workspace. The coordination across different brain regions is also seen as a solution to the binding problem.

Ananthaswamy argues that recent neuroscience has been supportive of global workspace theory. Stanislas Dehaerne's group at the French National Institute of Health and Medical Research have demonstrated particularly dense connections between the prefrontal, parietal and cingulate regions, and suggest that the links between these are consistent with the global workspace idea. The areas with the very dense connections are viewed as prime candidates for being part of the global workspace.

Dehaerne's team have made a useful study of the difference between conscious and unconscious perception. Volunteers viewed two sets of stimuli, but in some tests the second set was presented in such a way that it was only registered unconsciously. The study showed that neurons in some brain regions, such as the prefrontal and parietal, stopped firing when the processing was not conscious, whereas they had a high level of activity when it was conscious. Other tests by the group produced similar results. When neurons were active in response to conscious processing there was a 300 ms delay between presenting a stimuli and the neurons becoming active.

In fact, the main interest of these studies might appear to be the demonstration that conscious and unconscious brain processing are physically different, a position which is highlighted by the phenomena of blindsight. This contradicts the neuroscience/philosophical position evolved during the 1990s according to which consciousness was simply what it was like to have brain processing. The studies thus require an explanation of what is special about certain brain processing that causes it to produce consciousness, while other areas do not. Extremely convoluted and unconvincing arguments have been produced to get round the blindsight evidence, but even if these were substantiated, it would now also be necessary to explain Dehaene's studies.

Neither these studies, nor orginal global workspace theory, get us any nearer to an actual theory of consciousness. The theory might provide a solution to the binding problem, but it does not propose any reason why particular neurons or combinations of them are able to move from the unconscious to the conscious. This is why Baars theory has always seemed of only limited interest.

The attitude of neuroscientists towards consciousness is often hard to fathom. No mechanism by which consciousness could arise is proposed in the original global workspace or in this article, but Dehaene has, for reasons not explained, indicated that he expects that consciousness will somehow fall out of a more comprehensive workspace theory.

References:-
1.) Nature Neuroscience, vol. 8, p. 1391
2.) PLOS Biology, vol. 5, p. 260

4.)

The secret power of the cell

Brian, J. Ford, Gonville and Caius, Cambridge University

New Scientist, 24 April 2010 (based on a paper in Interdisciplinary Science Reviews, vol. 34, p. 350)

INTRODUCTION: Ford argues that neuroscience has made a big mistake in viewing the neuron as a simple on/off switch, and concentrating its attention on the relationship between neurons, rather than understanding the neurons themselves. He points to the autonomous and intelligent-type behaviour of single cell organisms, and suggests that the capacities of the brain are based on the processing of individual neurons. This is reminiscent of Penrose's 1994 discussion of the abilities of single cell organisms, which was seen as an argument for quantum computing, within the complex quantum bonds of protein and water that make up individual cells.

Ford discusses the extent to which single cell organisms manifest autonomous and intelligent-type behaviour. He points out how some species of algae display a problem solving capacity, while other single cell organisms build symmetrical shells out of grains of sand. He also indicates the degree to which individual cells in the body are autonomous, responding to current conditions, without needing instructions from the brain.

In looking at the brain and neurons, Ford is critical of the strongly entrenched orthodoxy of modelling the immensely complex neurons as simple on/off switches. This is the more curious, in that much modern research is directed at the complex proteins of the cell interior. Ford wonders why neurons are allotted such a simplistic role, when single cells demonstrate the capacity for such complex activity. He points out how neuroscience is interested in the relationship between neurons, rather than neurons themselves.

He further hypothesises that the effectiveness of the brain will eventually be discovered to derive more from processing within neurons than from the relationship between them. He views the action potentials that set off the movement of neurotransmitters from one neuron to the next, as a language that neurons use to transmit data that they have already processed. He regards the brain not as a supercomputer, but as a community of microscopic computers. Attempts to develop artificial intelligence and robotics based on the neuron as a switch are therefore seen as a grandiose failure, a view that seems to be supported by a half century of disappointment with attempts to develop autonomous robots.

Finally, Ford recalls an eerie experiment in which the 40 Hz gamma synchrony, viewed as a correlate of consciousness in much conventional neuroscience, was adjusted to a frequency compatible with the human ear. He relates how this produced a sound with the hypnotic quality of the calls of seabirds, and a sense that each axon spike was modulating a discrete signal within itself.

To some, the discussion of capacities of single cells may sound familiar. In fact, the point was made by Penrose as far back as 1994, when he remarked on the sophisticated autonomous abilities of the single-cell paramecium. The proposal here was that single-cell organisms can achieve sophisticated behaviour, without the help of brains or nervous systems. This is suggested to be because individual cells utilise quantum computing based on cytoskeletal structures that are suited to information processing and on the complex web of quantum bonds within the protein and water that comprises the cells.

5.)

Neural mechanisms of autonomic, affective and cognitive integration

Hugo D. Critchley, Wellcome and UCL

Journal of Comparitive Neurology, 493, pp. 154-6

INTRODUCTION: This paper discusses evidence for the involvement of bodily responses in brain processes, particularly those related to emotional experience. There does, however, seem to be 'a dog that doesn't bark in the night' somewhere in this paper. There seems to be an unspoken assumption that there is an important distinction between volitional or motivational actions and unconscious activity, and also an assumption that subjective emotions are somehow important to the former. This of course flies in the face of the rigid orthodoxy of psychology and most neuroscience to the effect this distinction is an illusion, and that subjective emotions and other experience are of little scientific relevance. An additional problem in reading this paper is that it is not clear whether the author thinks that all emotional experience is derived from bodily sensations or only some. While the studies discussed in the paper certainly support the latter, the former looks less plausible.

This paper discusses studies that support the view that bodily processes act on brain processes, and are important in the generation of the subjective experience of emotions. Specific brain areas are highlighted in respect to brain-body interaction and emotion. The anterior cingulate cortex is seen as being involved in generating responses by the autonomic (involuntary) parts of the nervous system, while the insula and orbitofrontal cortex are thought likely to map the visceral (internal organs) responses. The ventromedial prefrontal cortex supports states of rest that may serve as a benchmark for more dynamic activity. The interaction of the anterior cingulate, the insula and the orbitofrontal are suggested as possibly being the basis for emotional experience and motivated behaviour. Generation of and subsequent feedback from autonomic processes is suggested to be linked to subjective emotions.

The autonomic nervous system is the mainly regulator of bodily functions, and allows responses to environmental changes. The autonomic system is divided into the sympathetic and the parasympathetic system. The sympathetic system relates to motor action and changes such as heart rate that relate to motor action, and it is thus associated with 'fight and flight reactions'. The parasympathetic system is involved with recuperative processes, such as reducing the heart rate. The sympathetic system originates in the brain stem, and extends down down the spine and utilises adrenaline and noradrenaline as transmitters. The parasympathetic also originates from the brain stem and uses acetylcholine as a transmitter. The sympathetic system is also acted on by nuclei in the hypothalamus. Experiments have demonstrated influences on the autonomic system from the cingulate, the insula and the medial temporal lobe via both the hypothalamus and the brain stem. However, the brain stem's autonomic centres require feedback from the body to maintain homeostasis (stable conditions in the body). This feedback also influences motivational behaviour by conveying information on levels of comfort or discomfort.

In support of Damasio's somatic marker theory, the experience of feedback from bodily states is hypothesised to be the basis of the subjective experience of emotion. This argument seems sound up to a point, but it is difficult to think that external stimuli, especially the more urgent ones, for instances phobic fear reactions, cannot occur without being laboriously processed through internal organs. The same qualification could apply to emotions arising from cognitive activity. Again it seems laborious and maladaptive in terms of use of energy for everything to have to go via the internal organs, before it can be assessed in terms of emotional experience. Another objection dating back to the 1920s is that bodily arousal is too limited in its range to account for all the variations in subjective emotional experience. The impairment of judgment, decision taking and behaviour in patients with orbitofrontal and ventral prefrontal damage is seen as supportive of the somatic marker idea, but at least some of the deficits here can also be viewed as a consequence of impaired communication between the frontal and limbic areas of the brain. The finding that autonomic arousal is reduced in patients with lesions does not seem that surprising, as outward as well as inward signaling is likely to be impaired by the lesions in the brain. In particular, this does not seem enough to support Damasio's rather vague notion of the self arising from representations of the body state. This is not to say that the body plays no part in it, but it would seem to require considerably more evidence to suggest that the body by itself creates the self.

The evidence of studies indicates that the hypothalamus monitors the body, and that sensory information from the body projects to viscosensory cortex in the insula and the orbitofrontal. In primates this information does not go via a structure in the pons area of the brain stem, and this would seem to be indicative of a less automatic system than in other animals. Similarly, sympathetic arousal of skin conductance has been shown to enhance activity in the ventromedial prefrontal, the right anterior insula and the dorsal anterior cingulate.

The dorsal anterior cingulate is seen as a brain area of particular interest because it is involved in attention and cognitively demanding activities. Researchers have considered that it may have a role in executive control and possibly consciousness. There is a correlation between dorsal anterior cingulate activity and task difficulty, and this type of mental stress enhances sympathetic activity. It is claimed that various studies argue in favour of the dorsal anterior cingulate having control over the autonomic system during volitional behaviour, including difficult cognitive activity.

The amygdala and particularly the central nucleus of the amygdala is also seen to produce autonomic arousal when it is receiving emotional stimuli. The dorsal anterior cingulate and the amygdala are often active at the same time. The activity of the dorsal anterior cingulate during volitional behaviour may provide control over the autonomic system. Also visceral and pain stimuli are associated with enhanced activity in the anterior cingulate, insula and thalamus. The dorsal pons in the brain stem and the anterior cingulate are both sensitive to any absence of feed back from the body. The mid-insula and the amygdala are sensitive to autonomic arousal as a result of emotional stimuli, and this is taken to suggest a role for the right insula in the experience of emotion. Activity in the right anterior insula predicts subjective emotional experience and is also connected to visceral activity.

Generation of autonomic responses may originate in the anterior cingulate during volitional and cognitive activity, while the conscious experience of visceral responses and subjective emotion may arise in the anterior insula, especially the right anterior insula. The insula and the anterior cingulate are often active at the same time in the event of pain, threat or attention. The activity of the dorsal anterior cingulate predicts autonomic arousal. Other research emphasises the importance of the anterior cingulate in cognitive control. The anterior cingulate is important for monitoring actions and their consequences and also performance errors. The anterior cingulate and the amygdala generate and map bodily responses that are interconnected with the orbitomedial region. The intensity of bodily responses is suggested to have an influence on subjective emotion.

6.)

Explaining the Brain

Carl F. Craver

Oxford University Press (2009)

INTRODUCTION: Craver's work could be viewed as an attempt to clear away some of the undergrowth of 20^th century philosophy that has tended to constrain, both attempts to interpret neuroscience's discoveries, and ultimately the attempt to understand the physical basis of consciousness. Craver is critical of those philosophers who have interpreted neuroscience in terms of simple and predictable laws deriving purely from the neuron level. He suggests that this approach is lacking in evidential support. Instead, he favours causal mechanisms. Thus complete explanations in neuroscience are argued to be those that capture all the causal relations between the components of a mechanism. Explanations in practical neuroscience are seen to describe mechanisms, and show how components make something work, rather than relating to the effect of general laws. Particular components arranged in a particular system is what is seen as necessary for explanation. The author also argues against any absolute concept of 'levels' that cannot interact with one another. Levels are only seen as a constraint within a particular mechanism. So the hippocampus and the pyramidal cells might be at different levels in a particular mechanism, but this should not be seen as a general rule that must apply to these components in all instances. The levels in neuroscience are argued to be levels within a particular mechanism, rather than levels applying as a general law.

At the beginning of this book, Craver states that the general form of mechanistic explanation is his focus. He says that he is concerned with constitutive or componential causal-mechanical explanations, which means that the explanation of some occurrence is based on the organisation and activity of component entities.

As an example relevant to neuroscience, he quotes the opening of a Ca²⁺ ion channel that happens as a result of the components of a system. Craver is critical of some other philosophers such as Patricia Churchland, who seek to place all neuroscientific explanation at the neuron level, in accordance with the so-called neuron doctrine. The neuron doctrine is the most widespread form of interpretation in both neuroscience and philosophy, but Craver suggests that this approach is lacking in evidential support. He argues instead that neuroscientific explanations are multilevel in form with component parts specified at the brain system, cellular and molecular levels. He claims that practical neuroscience both past and present has relied on such descriptions.

This book argues for the causal and explanatory relevance of phenomena at multiple levels. The role of the Ca²⁺ ion in neurotransmitter release is taken as an example of a neuroscientific system that is explained in terms of a particular mechanism, in this case the mechanism allowing the inflow of calcium ions, rather than in terms of any particular fundamental law. The classic Hodgkin and Huxley study that led to the understanding of the action potential is viewed in similar terms.

Craver also warns that correlations may not be explanatory, an elementary caveat, but one that is on the verge of being forgotten in modern consciousness studies. He reminds us that it is necessary to distinguish between what is a cause and what is a correlation, and to be careful in distinguishing which amongst a choice of variables explains a phenomena. Craver argues against explaining neuroscience in terms of the unfolding of simple predictable laws. Explanations in practical neuroscience describe mechanisms, and show how components make something work, rather than relating the effect to the fundamental laws.

He points out instances such as the unpredictable relation between action potentials and synaptic firing, with only about 15% of potentials leading to a firing, and similarly the fact that Ca²⁺ channels can open under conditions where such an opening has a low probability. Thus, effects do not necessarily have to have high probability for them to have been caused by something. Complete explanations are described as those that capture all the causal relations between the components of a mechanism. Temporal sequence, i.e. one thing happening before another is not sufficient explanation by itself. Similarly, effects that derive from a common cause do not as such explain one another.

Generalisations that hold true about organisms, may hold true only some of the time. Craver takes the example of long term potentiation, which is a general truth but may be defeated by individual circumstances, and is not as such a law of nature. Thus, Craver argues that in the past, there was a time when no LTP existed, because it had not evolved, whereas the laws of nature are assumed to have existed from the beginning. What is true of LTP in this respect is regarded as applying to large areas of neuroscience.

The author distinguishes between 'how possibly' and 'how actually' models of explanation. How-possibly models might produce the effect that they are trying to explain, but there is little evidence that they actually do. Mainstream consciousness theories such as functionalism could be argued to fall into this category, although some might argue that they fail even the 'possibly' test. Functionalism certainly fails to come up with any specific mechanism, by which consciousness could arise either in brains or machines. Thus the particular components arranged in a particular physical system are what the 'how-actually' explanatory models require. These models show how the neuroscience works, not just how it might work.

Daniel Dennett's model in particular is criticised for lacking the input to distinguish 'how-possibly' from 'how-actually'. It is necessary to know not only how mechanisms such as action potentials occur, but also how they may be inhibited or altered by variations in conditions. The author criticises so-called 'box and arrow' explanations because they only work if the boxes and arrows correspond to physically active components, which is often the case. He stresses it is necessary to distinguish real components from fictions in order to distinguish good explanations from bad explanations.

As an example, he discusses how channels in cell membranes were initially not much more than a convenient fiction, but gradually became physically substantiated as proteins, with a description of their amino acid chains and secondary and tertiary structure. This process was a function of a convergence of independent evidence rather than any particular fundamental law.

The mechanical explanations also depend on the spatial and temporal organisation of the component parts, and a different spatial and temporal organisation of these parts can result in a different outcome. Thus the activities of the components have to happen in a particular order. The author criticises the practise in much artificial intelligence of ignoring such temporal constraints on processing. The concept of a mechanistic explanation involves the behaviour of the mechanism as a whole. Parts have to be arranged in a particular spatial and temporal order. It matters how the components are organised with respect to one another. The higher-level property is not a simple sum of the components, but is a function of how they are organised.

Levels: The fact that parts are smaller than the whole is not because they are on a particular scale as such, but simply because they need to fit inside the whole, for instance a hippocampus involved in spatial memory needs to fit inside a brain. Similarly, it makes no sense to ask whether two components are at the same level, if they are not part of the same system. It thus makes no sense to ask whether a water pump and a heart are at the same level, because they are not part of the same system. The hippocampus and a pyramidal cell might be at a different level in a particular system, but this does not mean that they have to be at a different level in every system.

This local view of levels is seen as providing a more solid basis for neuroscientific explanations. The author sees no difficulty in things of different size interacting if they are part of the same mechanism, and the understanding of neuroscience can suffer if there are attempts to force explanations into preconceived size hierarchies. Parts plus organisation are what is causal in any mechanism. Further to this mechanisms can do things that individual parts cannot. The actual structure and organisation of the mechanism mean that it can be influenced by the environment in a way that the parts cannot. Thus the causal power of the whole mechanism is greater than the causal power of its parts, and therefore the ability to be causal cannot be confined to the lowest level of the hierarchy, but also exists at the level of the whole mechanism.

Explanations in neuroscience are viewed as spanning multiple levels. For neuroscience, the lowest level is viewed as the molecular level of electrical and chemical activity in cells. The author argues that levels in neuroscience are best seen as levels of mechanism. Lower-level components are organised to make up higher- level mechanisms. The author accuses some 20^th century philosophers of creating an over rigid hierarchy of levels that could neither cope with the complexity of the real world, nor respond to changes in scientific knowledge. In the authors view, levels of processing are ranked according to their place in a temporal and causal sequence. For instance, processing in the retina occurs previous to and is causal of processing in the thalamus. It is a components level in a causal chain rather than their actual size or scale than determines their level in the hierarchy.