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Neuroscience 2Neuroscience papers etc with relevance to quantum consciousness. Includes Max Tegmark's 2000 paper and Hagan, Hameroff, Tuszynski reply to it. 1.) Organism and Machine - Michael Denton - Emphasises differences between machines and organisms based on protein 2.) A neural mechanism that randomises behaviour - R.H.S. Carpenter - Vision research suggesting possibility of quantum mechanisms in the brain. 3.) The importance of quantum decoherence in brain processes - Max Tegmark - Landmark article for objections to quantum consciousness 4.) Quantum computation in brain microtubules: Decoherence and biological feasibility - Hagan, Hameroff, & Tuszynski 5.) Connexin 36 - From the site author - Relative to mistaken idea that studies have shown that mice can be conscious without gap junctions. 6.) Disorderly Genius - David Robson - Revival of the idea that chaos theory has a role in brain function 7.) The water melon story - Based on notes from a lecture by Anthony Dickinson on consciousness and desires 1.) Organism and Machine Michael Denton In: Are We Spiritual Machines? Ed. Jay Richards Michael Denton’s chapter is critical of the prevailing brain as a machine paradigm. He claims that the underlying design of organic systems is not at all analogous to the design of machines. All the parts of an organism influence each other, in a way which is not true of machines. Denton says that this principle is well illustrated by the structure of protein, the basic building block of life. The arrangement of the atoms in protein is unlike anything found in machines. In contrast to machines built up out of modular parts that can be replaced by like parts, proteins are characterised by the chaotic nature of their arrangement. This was the impression gained by the first researchers to detect the molecular structure of protein. A later paper says:- ‘Perhaps the most remarkable features of the molecule (protein) are its complexity and its lack of symmetry. The arrangement seems to be almost totally lacking in the kind of regularities which one instinctively anticipates, and it is more complicated than had been predicted by any theory of protein structure.’ _____ M. Perutz - European Journal of Biochemistry The structure of protein began to be disclosed in the late 1950s. In the early stages, it was assumed that each amino acid made an individual contribution to the three-dimensional structure of the protein. This assumption was based on the concept of proteins as machines, molecular machines, that were expected to be built up of independent parts that all made a contribution to the whole, but was quite distinct from the contribution of other parts. This idea of a molecular machine is still advanced in text books, but Denton regards it as false. Research progressively showed that in protein, the contribution of each amino acid was influenced by interactions with many of the other amino acids in the protein. It was discovered that the spatial conformation of each part of the amino acid chain of a protein was the product of a complex web of van der Waal force between electrical dipoles and electro-chemical interactions. These involved almost every section of the amino acid chain. Almost every one of thousands of atoms in the protein macromolecule contributes to the shape of the molecule via interactions with most of the other atoms. The impression is sometimes given that protein components such as the alpha helix can be treated as separate modules, much like components of a machine. In fact, the stability and form of these elements is dependent on van der Waal and microchemical interactions, in turn dependent on larger scale interactions within the protein. The properties of each component within the protein are not fixed, but are dependent on the local conditions within the protein. While a module in a machine, such as a wheel on a car, is still a wheel when it is removed from the car, the same is not true of the component of a protein. The components of proteins are only components when they are interacting with other components. The form and function of each part is determined by the whole and vice versa, in a manner that is alien to human technology. What is true of proteins is true of other important macromolecules. RNA molecules, like proteins, fold into three-dimensional forms in which all parts are shaped by reciprocal interactions. The constituent parts of these only hold their shape when they are part of the whole molecule. Removed from it they take on another shape, or disassemble into a random chain. The proteins form into multiprotein complexes, such as the ribosomes that manufacture the proteins, and the cytoskeleton that comprises microtubules, microfilaments and intermediate fibres. The same principle applies as with the component proteins, that the parts have a reciprocal formative influence on one another, and change and no longer exist in their previous form if removed from the whole. The same principle applies to the cell as a whole, the parts only existing as part of the whole, and disintegrating if they are outside the cell for any length of time. This view of proteins, RNA, cell components and the cells themselves suggests that attempts to understand organism in terms of fixed organic components or parts of something like a machine are likely to fail. At a more general level, this view of organic matter emphasises the superficiality of mainstream consciousness studies in regarding the neuron as a simple switch, and refusing to look at the possible functions of microtubules, other proteins and the quantum forces that bind them. 2.) A neural mechanism that randomises behaviour R.H.S. Carpenter Physiology Laboratory, University of Cambridge Journal of Consciousness Studies, vol. 6, No. 1, 1999, pp. 13-22 The abstract starts by pointing out that the time taken to react voluntarily to stimulus is far longer than can be accounted for by known nervous system processing. The strength of response is shown to rise in proportion to the incoming sensory data, until a critical level at which action is taken is reached. However, the rate of rise fluctuates randomly from trial to trial. This claim is based on studies of neurons in the frontal eye field and the time taken between presenting a visual stimulus and making a saccade (an eye movement). The saccade itself is very quick, lasting only 20-30ms, but the system is not designed for speed in other respects. The average gap between presentation of the stimulus and the saccade is 200ms. Normal processing in the nervous system is claimed to account for at most one third of this time. The shortest route from the retinal receptors to the eye muscles passes through the superior colliculus and should take only 60ms. However, the colliculus receives input that comes ultimately from the parietal cortex and the frontal eye fields. The control is inhibitory, otherwise the eyes would be constantly darting towards each and every stimulus. The blanket inhibition has to be lifted for a saccade to be made. The colliculus lacks the information to make useful decisions, because it registers only where things are in space, but not what they are. The biggest problem is seen to be in a series of trials the response time varies over a surprisingly large range. While the average saccadic latency is 200ms, on some 5% of trials the latency is either less than 150ms or more than 300ms. In the first stage of the latency period neurons distinguish between a target stimulus and distractors. This takes about the same period of time, about 70ms, whether the eventual latency period is short or long, so the whole of the variability is concentrated in the latter part of the latency period. The article suggests that this means that the variability is not due to noise in the sensory pathways, but to something introduced by the brain. The randomness of the reaction times is seen as a function of deliberate randomisation by neural processes in the brain. Carpenter says that the underlying process is obscure, although he points out that its is consistent with the Penrose/Hameroff model, and that the delay periods involved are similar to those seen in Libet’s experiments. Carpenter goes on to speculate as to what evolutionary advantage would favour randomisation. He argues that there would be an adaptive advantage in the resulting unpredictability, as opposed to deterministic responses that would be easier for a predator or prey to predict. 3.) The importance of quantum decoherence in brain processes Max Tegmark Phys. Rev. E. (1999) This paper is often quoted as the definitive refutation of the Penrose/Hameroff model. Less frequently quoted is the response of Hameroff et al pointing out a number of deficiencies in its arguments. Tegmark stresses that the crucial factor for quantum theories of consciousness is the ability to sustain quantum coherence in the conditions of the brain. This much is accepted by Hameroff. However, Tegmark says that the purpose of his paper is to calculate the rate of decoherence in the brain, which he boldly states as something that will settle the whole matter. An unexpectedly long section in the paper is devoted to discussing the speed of decoherence at the level of neurons, which is not the basis for any of the main theories of quantum consciousness. However, Tegmark finally goes on to examine the more rellevant matter of decoherence on the scale of microtubules, which is central to the Penrose/Hameroff model. Tegmark arrived at a decoherence time of 10-13 seconds, which would be too short to be of use in neural processes. However, Tegmark assumed a model involving a superposition of solitons 24 nm apart, whereas Penrose/Hameroff are working on the basis of the much smaller separation of nuclei within the tubulin protein subunits of the microtubules. It remains a mystery as to why Tegmark selected a model that is not only different from the Penrose-Hameroff model, but does not resemble any of the principal modern quantum consciousness models. Whatever the reason, it certainly makes his particular calculation irrelevant, although it remains true that decoherence would obliterate quantum coherence, unless such coherence is shielded from the environment in some way. In fact, the other main complaint against Tegmark’s paper is that he does not adequately discuss Hameroff’s proposals for how microtubules might be shielded from decoherence, or the proposals of other theorists for how their models might also evade decoherence. Hameroff accepts that even without Tegmark’s soliton model, decoherence would happen too quickly to be neurally useful. He suggests that shielding for microtubules could be provided by ordered water, that is water molecule dipoles aligned with the biomolecule dipoles of the microtubule tubulin protein, particularly during the gel part of the cytoplasmic cycle. This might be supplemented by energy pumping given the lack of thermal equilibrium in biological tissue and also by quantum error correction facilitated by the design of the microtubule lattice. In the discussion section towards the end of his paper, Tegmark is drawn back to the idea of decoherence at the neuron level, an irrelevance in terms of modern quantum consciousness. Here he tries to argue that even if it turns out that there is quantum coherence in the brain, it must be irrelevant to consciousness, because decoherence would occur as soon as there was communication at the scale of a neuron. However, this fails to discuss suggestion that structures such as microtubules or ions in ion channels could carry out quantum processing, collapse their wave functions, and afterwards communicate classically with the synapses. At the distance of nearly a decade, some aspects of Tegmark’s paper appear slightly old fashioned. Considerable faith is attached to work on computational neural networks, which boomed in the 1990s as a way of modelling brain processes on classical computers. The same faith is apparent in some of Patricia Churchland’s work. However, in the intervening years the neural network story appears to have fallen silent. To judge by the lack of striking progress on the artificial intelligence front, nothing has emerged from the neural network activities to allow any close artificial simulation of the brain. Bear in mind that in the late 1990s, some serious writers were forecasting a robot takeover of our planet in the early years of the present decade. 4.) Quantum computation in brain microtubules: Decoherence and Biological Feasibility Hagan, S., Hameroff, S., Tuszynski, J. Physical Review, vol. 65, 10 June 2002 This
article is the authors reply to Tegmark’s claim that the speed of
decoherence makes the Penrose/Hameroff or Orch OR model for
consciousness implausible. Tegmark’s main criticism was that coherence
would collapse in 10-13 seconds in the conditions of the brain, and this meant it could have no useful involvement in brain function. The authors’ reply is that Tegmark did not look at the Penrose/Hameroff model involving protein superpositions, but at another model, apparently proposed by Sataric that involves a soliton in superposition along the whole length of the microtubule. Tegmark also seems to have thought that the suggested superposition must cover the whole 24 nm of the microtubule, whereas Penrose/Hameroff are thinking in terms of separation at the level of atomic nuclei within the tubulins. Thus there is a seven orders of magnitude difference between the Tegmark model and the Penrose/Hameroff model.The article sees the microtubules as mediating between the quantum computation of the tubulins and the classical behaviour of the rest of the neuron. The article sees the microtubule superposition as needing to survive for tens of milliseconds in order to usefully interact with brain functions. The Penrose/Hameroff model suggests that the cytoplasm around the microtubules alternates between a type of gel and a liquid. During the former stage the microtubule is screened from the environment and contains superpositions and quantum computing. During the latter there are classical events such as attachment of microtubule associated proteins, membrane activities and synaptic functions. On the inward route, synaptic activity is suggested as affecting the cytoskeleton. The arrangement of the MAPs following synaptic activity is suggested to have an impact on the subsequent microtubule states. The article goes on to discuss the existence of quantum behaviour in protein. It quotes A. Roitberg et al in Science 268 (1), who reports substantial quantum effects. It also quotes J. Tejada in Science 272 (2), who criticises Gidia et Al. The latter’s work claims to detect macroscopic quantum coherence in the protein ferritin. Tejada criticises their procedures, but Gidia defends the original conclusion in a response to Tejada. They also refer to a series of experiments involving brain scanning, by W.S. Warren et al, (3), R.R. Rizi et al (4) and W. Richter et al (5), which showed that quantum coherence between proton spins up to a micrometer apart could be artificially induced for tens of milliseconds. The length of the coherence periods allows it to be seen as possibly connected to the so-called 40Hz oscillation between the thalamus and the cortex and between other regions of the brain. These experiments are seen as mainly important in demonstrating the possibility of quantum coherence within the brain. The argument that the brain could not sustain quantum coherence for a useful period has always been the most cogent argument against theories of quantum consciousness, and that argument is weakened by these experiments. However, it is stressed that the experiments did not involve entanglement, and the particular processes induced are not thought likely to be useful in brain function. 5.) Connexin 36 From the site author Connexin 36 is a protein found in the gap junctions that play a key role in the Penrose-Hameroff quantum consciousness model. For the purposes of neuroscientific studies, it has proved possible to breed mice that lack connexin 36. These mice, in which the gene for connexin 36 has been deleted, have certain deficits in motor, learning, memory and reproductive capacity. Gamma synchrony in the brain persists, although at reduced amplitude. Otherwise, they appear to observers to by just as conscious as normal mice. This fact has been seized on by critics of the Penrose-Hameroff model claiming that these mice remain conscious either despite having no gap junctions, or despite connexin 36 being the main component of gap junctions. However, both these claims are dubious. Most animal cells have gap junctions with neighbouring cells. The cells are only 2-4 nm apart at these junctions, with the space bridged by gap junctions that are formed out of proteins called connexins. Six connexin proteins are assembled in each cell to form a connexon, and two such structures, one in each cell, join together to form a gap junction between the two cells. Humans have at least 14 different types of connexin, each found in a different range of tissues, with at least 10 present in the central nervous system. Most cells have more than one type of connexin present. Connexons may be formed from one type of connexin or from several different types. Moreover, the set of connexins in one cell may be different from the set of connexins in another cell to which the gap junction provides a link. Connexin 36 is present in certain subpopulations of neurons throughout the mammalian brain. The connexon channels have a width of 1.5nm, which allows ions and smaller molecules to pass from one cell to the other, coupling the cells both electrically and metabolically. The permeability of gap junctions by different sizes of molecules or ions of particular charge can be a function of the connexins that comprise it. Gap junctions flip between open and closed states in response to conditions in the cell. The junctions are viewed as allowing adjacent cells to coordinate their activity, although the specifics of this process remain unknown. Existing knowledge of connexin distribution and function appears to be a good way from being complete. At the moment, it seems far from clear how important connexin 36 actually is relative to other connexins, and whether the absence of this particular connexin would result in the failure of gap junctions to form or for any that did form to carry out at least some of their normal activities. Some studies suggest the existence of normal neural gap junctions without any connexin 36. References:- Molecular Biology of the Cell – Taylor & Francis Group – ISBN 0-8153-4072-9 Psyche-B Archives – January 2005 Visual Neuroscience – Sitaramayya, A. et al – Connexin 36 in bovine retina - 2003, 20, pp.385-95 – Cambridge University Press Histochemistry and Cell Biology - Carola, M. et al - Immunohistochemical detection of the neuronal connexin 36 - 2002, vol. 117, No. 6, pp. 461-471 6.) Disorderly Genius David Robson New Scientist, 27 June 2009 The 1980s idea that chaos, in the technical sense of chaos theory, plays a role in brain function has seen a revival as a result of studies performed since 2006. Chaos theory demonstrates that tiny changes in a system can amplify up into very large divergences, as in the classic example of the flight of a butterfly in China leading to a later storm in America. Robson uses some less complex examples to draw an analogy with conditions in the brain, such as an extra grain of sand added to a sand pile causing an avalanche. However, when models of the brain based on chaos theory were attempted, the results did not resemble real brains. At a later stage, it was discovered that the brain generated random noise. When this factor was incorporated into brain models, they were found to support systems at the edge-of-chaos, or at the point of 'self-organised criticality'. This type of system can flip to-and-fro between an ordered state and a chaotic state. A 2006 study by Meyer-Lindenburg of the Mannheim Central Institute of Mental Health showed that the connections between different regions of the brain formed a 'small world pattern' in which neurons had many connections to their neighbours, but also a smaller number of long-range connections. The study confirmed that this sort of arrangement existed in the brain, and that this put it in an ideal position to sit at the edge-of-chaos. Further to this, in early 2009, Ed Bulmore and his tean at Cambridge University found that the duration of stable periods in the brain in which large groups of neurons synchronise to the same frequency or phase-lock, and unstable periods of intense activity in which they resynchronise, varies according to a power law distribution, which is itself a sign of chaos. In trying to model what had been observed in actual brain studies, this team found that it was necessary to place their models at the edge-of-chaos. It is suggested that such a system is adaptive, because it allows transmission of information to distant parts of the brain, and because resynchronisation to new frequencies may allow a quick reaction to new and potentially dangerous situations. The article does not have anything specific to say say about consciousness. However, in contrast to the rather clunky emphasis of much neuroscience and neuroscience-influenced philosophical writing, these studies suggest a structure in which apparently low-level physical influences might play a decisive role. 7.) The Water Melon Story Based on notes from a lecture on consciousness and desires by Anthony Dickinson, Dept. of Experimental Psychology, Cambridge University The highlight of Dickinson's lecture was the 'water melon story', which recounts a real incident in Dickinson's life. During a holiday in Sicily, Dickinson and his friends became thirsty. They went to the town square and bought water melons. They tasted good. Water melons were a novelty, as they had not been widely available in the UK in the mid 20th century. That evening Dickinson drank too much and became ill. A few days later, he went to the town square for more water melons. He was not aware of any aversion to water melons, but when he ate one it tasted foul, quite in contrast to his previous experience. He had developed an aversion to water melon as a result of being ill soon after eating his first ever water melon. He was not averse to the idea of water melon, but to the actual experience of eating it. Dickinson claims that this sequence of avoidance as a result of an experience that has no actual physical connection with the thing avoided cannot be replicated by computers. A computer can store a one-to-one connection of water melon as the cause of illness as a fact, but it cannot store the experience of illness and then connect it to prior events that may or may not be the physical cause of the illness. Conscious of an experience such as illness is therefore argued by Dickinson to give the brain greater flexibility there a computer that associates facts on a direct cause and effect basis. |
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