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Penrose & Hameroff 4Penrose/Hameroff: 4 Contains further articles related to Penrose/Hameroff quantum consciousness theory 1.) The path ahead - Jack Tuszynski & Nancy Woolf 2.) Microtubules in the cortex - Nancy Woolf 3.) Dendritic skeleton as a computational device - Priel, Tuszynski 4.) Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems - Gregory Engel et al 5.) Photon echo experiments at Starlab 6.) The brain is both neurocomputer and quantum computer - Stuart Hameroff 7.) The geometry of π electron resonance clouds - Stuart Hameroff 8.) Connexin 36: Discussion of the effects of the absence of this protein from the brain The Path Ahead Jack Tuszynski & Nancy Woolf Dept. of Physics, University of Alberta & Dept. of Behavioural Neurosceince, UCLA In: Tuszynski, J. Ed. The Emerging Physcics of Consciousness Springer ISBN-13 978-3-540-23890-4 The Path Ahead is Jack Tuszynski's and Nancy Woolf's introduction to 'Emerging Physics of Consciousness' a very useful book dealing with recent and proposed experimentation and new ideas relative to quantum consciousness. The introductory section contains overview material about the nature of the different approaches to quantum theory and the differing proposals for quantum consciousness. It is stressed that over the last decade experimental evidence has shown that there is signalling along microtubules, and several types of interaction between microtubules and membranes. From this it is reasonable to suggest that tubulin conformation in microtubules may be linked to ion channel opening and closing, enzyme catalysation, motor protein activity, and propagation of ionic waves along filaments. Tubulin itself is only marginally stable and must strike a balance between countervailing forces. Microtubules are also shown to be relevant to neurocognition. Neurons in rat visual cortex produce massive amounts of tubulin in the period after infant rats open their eyes ( 4. Cronley Dillon and Perry). Tubulin is thus implicated in a key period of visual learning. This introductory chapter also draws attention to the Beck & Eccles model (1), where neurotransmitter release and the resulting consciousness states are dependent on quantum tunnelling between vesicles in the synapses. This relates to the fact that even after receiving an axon signal it is probablistic as to whether or not a synapse will fire, and quantum mechanics is one way of dealing with this situation. The nanometer scale of proteins and other biomolecules in the synaptic area make a quantum solution look likely. Beck and Eccles relied on a theory worked out by Marcus and Jortner ( 2 ), who modelled quantum transfer between biomolecules. ( 3. Woolf ). References:- 1.) Beck, F. & Eccles, J. (2003) In: Osaka, N. Ed. Neural Basis of Consciousness Benjamins 2.) Jortner, J. (1976) Journal of Chemical Physics, 64, pp. 4860-4867 & Marcus, R. (1956) Journal of Chemical Physics, 24, pp. 966-978 3.) Woolf, Nancy & Hameroff, S. (2001) Trends in Cognitive Science, 5 pp. 472-478 4.) Cronley-Dillon, J. & Perry, G. (1979) Journal of Physiology, 293, pp. 469-484 Flohr, H. (1995) Behavioural Brain Research, 71, pp. 157-61 Flohr, H (2000) In: Metzinger, T. Ed. Neural Correlates of Consciousness MIT Press Kaech, S. (1999) Proceedings of the National Academy of Sciences, USA, 96, pp. 10433-10437 Khuchua, Z. et al (2003) Neuroscience, 119, pp. 101-111 Mershin, A. et al (2004a) Biosystems, 77, pp. 73-85 Squires, E. (1990) Conscious Mind in the Physical World Adam Hilger Tusznski, J. et al (2004) Biophysics Journal, 86, p. 1890 Microtubules in the Cerebral Cortex: Role in Memory & Consciousness Nancy Woolf Behavioural Neuroscience, Dept. of Psychology, UCLA In: Tuszynski, J. ED. The Emerging Physics of Consciousness Springer ISBN-13 978-3-540-23890-4 The author starts by querying whether the standard model of synaptic connections in the cerebral cortex adequately accounts for cognition, especially if this is multi-modal. A novel approach to brain mechanics is described. Neurotransmitters are indicated to act on microtubules polymerisation and transport, as well as membrane receptors. It is argued that this action on microtubules is involved in the physical basis of memory and consciousness. It is also suggested that learning alters microtubules lying beneath the synapse, and that these form the basis of long-term memory storage. The storage microtubules determine the synapse strength by directing actin filaments and transport of synaptic proteins. The paper argues that storage in microtubules is more plausible than in the synapse itself. Neurons are filled with microtubules, actin filaments and neurofilaments, all of which are components of the cytoskeleton. The microtubules comprise alpha and beta tubulin dimers and have plus and minus ends. The plus ends of microtubules undergo polymerisation/depolymerisation cycles to a much greater extent than the minus ends. The tubulin that comprises the microtubules accounts for up to a quarter of the soluble protein in the brain. Microtubule asociated proteins (MAPs) are found in high concentration in neurons. MAP2 is particularly abundant in dendrites. Areas possessing many pyramidal cells associated with higher cognition, are ofen rich in MAP2 , suggesting a connection between this protein and cells involved in higher cognition. MAP2 expression is also altered by the recent experience of learning, which suggests a more general involvement in information processing. The connection between microtubules and synapses is indirect. The region of the dendrites where microtubules are concentrated is called the sub-synaptic zone. The microtubules are connected with the dendrite spines by actin filaments. The proteins MAP2 and MAP1B provide the link between actin and microtubules ( 1 Dehmelt & Halpain). Protein kinases regulate the binding process. It has been suggested elsewhere that there is no connection between microtubules and dendrite spines, but the author here appears to describe a full mechanism. MAP2 has been identified as a signal transduction molecule and it also anchors other signal transduction molecules. Signal transduction by MAP 2 stems from synaptic inputs. MAP2 is also indentified as a gelation factor, a process that rigidifies microtubules, and in the Penrose/Hameroff model this process is suggested to screen microtubule quantum activity from decoherence. Ampa and kainate receptors for glutamate can influence microtubules. Ionic currents from receptors penetrate the sub-synaptic area of the microtubules. AMPA receptors can also affect microtubules via the actin filaments. Proteins link the synaptic density with the actin filaments, and the actins in turn link to microtubules ( 2. Qualman, Ladrech). Glutamate also binds to the NMDA receptors. Activation of the NMDA receptor results in an influx of calcium ions. These have the capability to penetrate deep ito the neuron, so it is likley that they come into contact with microtubules. Recent studies have shown that the shape of dendrite spines is altered by learning and experience ( 3. Yuste & Bonhoeffer ). Synapse and dendrite spine densities are also altered by learning ( 4. Leuner ). On the basis of various studies, the author thinks that neither dendrite spines nor actin near spines is sufficiently stable to act as permanent memory store. This leads onto the hypothesis that the permanent memory store is in the sub-synaptic zone of the dendrites. High concentrations of ATPase in the subsynaptic zone suggests a high metabolic rate, which would be a requirement for the laying down of memories. Microtubules are also more stable than actin spines, partly due to their connection with associated proteins. Binding proteins also help to stabilise the plus end of the microtubules. The initial segments of neurites are exceptionally stable, and the means available for stabilising microtubules makes them good candidates to act as memory stores. Polymerisation/depolymerisation cycles are used to update the microtubule network. Plus ends of microtubules near the sub-synaptic zone are affected by glutamate synaptic activation and with the additional presence of a neuromodulator, there would be widespread effects along the dendrite shaft. Woolf quotes initial experimental evidence that changes in dendrite spines may depend on microtubules. The alkaloid, vinpocetine, increases spine changes because of its effects on microtubules ( 5. Lendvai ). It is suggested that microtubules could initiate or maintain potentiation of synaptic acivity. The dependence of long term potentiation (LTP) on the transport of AMPA receptors along the microtubules is suggested as evidence for this. Studies demonstrate the importance of MAP2. Mice bred with a knockout of MAP2 show a reduction in microtubule density and dendritic length. Microtubule transport is seen as important for learning. Experimental results are claimed to be consistent with the hypothesis that brain reorganisation following learning leads to increased receptors in the post synaptic density, and at the same time decreases further transport to the synapse. There is usually an inverse relationship between microtubule transport activity and stability, and the reduction in transport here suggests increased microtubule stability. Experiments with learning involving MAP2 and kinesin suggest that microtubules are central to learning and memory. The author asks how it is that stored memory is able to influence neural processing. Microtubules could perform this function by means of changes in the size and position of synapses, the transport rates of protein and RNA, or regulation of ion channels ( 6. Whatley ). Microtubules self-initiate activity such as plymerisation/depolymerisation, so they are good candidates for self-iniating memory effects. The author also looks for something that can initiate the direction of attention, and suggests that the polymerisation/depolymerisation of microtubules could allow them to search for and activate particular sub-synaptic areas, including those connected to attention and consciousness. The articles proposes that long-term memory storage is concentrated in the sub-synaptic zones beneath the dendritic spines. This differs from the mainstream view of memory storage that pinpoints changes in the actual synapse, the post-synaptic membrane, dendrite spines and receptor density. Changes in dendrite structure that occur with learning indicate probable long-term changes in the underlying microtubules. The brain is not that heavily interconnected and a part of the 'binding problem' is that brain areas that are unified in perception are not necessarily communicating. Thus the left and right visual field may not be in communication, but are still unified. The author suggests that the sub-synaptic areas in dendrites are connected by microtubules and that quantum entanglement in the microtubules deals with the binding problem. The paper points out that the tubulin structure of the microtubules allows dimer/dimer interactions to be felt for a long way along the microtubule. The author rejects the idea that coherence cannot persist in brain tissues pointing to calculations that suggest this is possible ( 8. Hagan). MAP2 interactions would suggest that if entanglement does exist in microtubules, it could exist between different microtubules in the same neuron. The third stage would involve entanglements between microtubules in different neurons. This is suggested to be possible via the action of gap junctions. Changes in MAP2 that are uniform within cortical modules could suggest entanglement ( 9. Woolf & Hameroff ). Other studies ( 10. Ghosh et al and Veral ) calculate that a very small amount of entanglement can produce significant effects in the macroscopic world. The entire history of a synapse could be stored in the microtubules in the sub-synaptic zone. It is also suggested that dreams are produced by quantum coherence amongst the sub-synaptic zones of microtubules ( 9. Woolf & Hameroff ). More widely accepted theories based on random neural firing are criticised for not explaining how the brain produces the partly coherent stories experienced in dreams. References:- 1) Dehmelt, L. & Halpain, S. (2004) Journal of Neurobiology, 58 (1) pp. 18-33 2) Ladrech, S. et al (2003) Hear Res, 186 (1-2) pp. 85-90 Qualmann, B. et al (2004) Journal of Neuroscience, 24 (10) pp. 2481-95 3) Yuste, R. & Bonhoeffer, T. (2001) Annual Review Neuroscience, 24, pp. 1071-89 4) Leuner, B. et al (2003) Journal of Neuroscience, 23 (2), pp. 659-65 5) Lendvai, B. et al (2003) Brain Research Bulletin, 59 (4) pp.257-60 6) Whatley, V. & Harris, R. (1996) International Review of Neurobiology, 39 pp. 113-43 7) Kaech, S. et al (1999) Proceedings of the National Academy of Sciences USA, 96 (18) pp. 10433-7 8) Hagan, S., Hameroff, S., Tuszynski, J. (2002) Physical Review E Stat Nonlin Soft Matter Physics, 65 ( 6 Pt 1 ) 9) Woolf, Nancy & Hameroff, S. (2001) Trends in Cognitive Science, 5 (11) pp. 472-8 10) Gosh, S. et al (2003) Nature, 425 (6953) pp. 48-51 Veral, V (2003) Nature, 425 (6953) pp. 28-29 Dehmelt, L. et al (2003) Journal of Neuroscience, 23 (29) pp. 9479-90 Halpain, S. et al (1998) Journal of Neuroscience, 18 (23) pp. 9835-44 Halpain, S. (2000) Trends in Neuroscience, 23 (4), pp.141-6 Hameroff, S (1998) Philosophical Transactions of the Royal Society of London A. 356, pp. 1869-1896 Hamerof, S. & Penrose, R. (1996b) Journal of Consciousness Studies (3) pp. 36-53 Harada, A. et al (2002) Journal of Cell Biology, 158 (3) pp. 36-53 Khuchua et al (2003) Neuroscience, 119 (1) pp. 101-11 Ozer, R. & Halpain, S. (2000) Mol Biol Cell, 11 (10) pp. 3573-87 Tegmark, M. (2000) Physics Review E., 61 pp. 4194-4206 Tuszynski, J. et al (1997) Journal of Structural Biology 118 (2) pp.94-106 Woolf, Nancy et al (1994) Neuroreport, 5 (9) pp. 1045-8 Woolf, Nancy et al (1999) Brain Research, 821 (1), pp 241-9 Woolf, Nancy (1998) Progressive Neurobiology, 55 (1) pp. 59-77 Woolf, Nancy (1993) Journal of Chemical Neuroanatomny, 6 (6) pp. 375-90 Woolf, Nancy (1996) Neuroscience, 74 (3), pp. 625-51 The Dendritic Cytoskeleton as a Computational Device: An Hypothesis Avner Priel, Jack Tuszynski & Horacion Cantiello Dept. of Physics, University of Alberta & Harvard Medical School In: Tuszynski, J. Ed. The Emerging Physics of Consciousness Springer ISBN-13 978-3-540-23890-4 The hypothesis in this paper is that microtubules (MTS) and actin in the dendritic cytoskeleton are active in neural computation. These proteins are suggested to interact with ion channels, microtubule associated proteins (MAPs) and kinesin. Particular importance is attached to the C-termini of the tubilin, which are suggested to exist in several conformational states, and to be reponsible for the dynamic properties of the neuroskeleton. The authors contend that ionic wave propagation along the cytoskeleton affects channel function and thence the behaviour of the dendritic tree and brain function as a whole. Dendrites are the main site of excitatory inputs, but relatively little is still known about their functions. The activity of the particular dendritic trees, which vary greatly in shape and size, are suggested to be related to these differences. The size and complexity of dendritic trees increases with development, and this is assumed to be related to the complexity of the animal environment and memory ( 1. Kaech, Johnston, Matus ). Inputs come in at dendrite spines which are more numerous in pyramidal neurons and less so in interneurons. The number os spines and the number of excitatory inputs is clearly correlated. The authors contends that the dynamics of the cytoskeletal structure process and deliver information to the synapse. The actin cytoskeleton is known to be related to the stability of dendritic spines ( 2. Fickova, Fischer, Landis ). Twitching of dendrite spines, which has been suggested to encode very short term memories, involves actin dynamics ( 3. Crick, Dunaevsky ). The actin part of the cytoskeleton has a key role in the formation and maintenance of synapses, and is itself remodelled by synapses. Pruning of synapses is also associated with actin ( 4. Collicos, O' Leary, Sanes, Scott, Weimann, Zhang). The authors argue that the extent of dendritic change in terms of growing new branches or developing new spines argues against the rather fixed quality of the traditional Hebbian model of neuronal asseblies. Recent experiments also suggest that synaptic strength is less stable than the Hebbian model suggests. Conventionally, actin and microtubule networks have been seen as performing separate roles, with actin involved in cell movement and microtubules in transport of organelles. However more recent studies suggest that both systems have a role in what were the traditional functions of the other system ( 5. Dehmelt, Letourneau ). In fact, microtubules often grow along actin bundles. Microtubules and actin are both involved in the growth cones of cells. The authors suggest that the actin and microtubular cytoskeletons may be central to the functioning of cells. The authors see a potentially important role for the C-termini on tubulins. It is apparent the neurons utilise MTs in some forms of cognitive processing, with both MAP2 and kinesin involved in learning and memory ( 6. Khuchua, Wong, Woolf ). It is considered likley that the transport of particular proteins and mRNA, important for synaptic development along MTs to the postsynaptic densities is important for learning. One theory put forward has been that counterions form along the length of the polymer, such as MAPs. Map2 acting as a wave guide could transfer the conformational state of a C-termini to an neighbouring MT. Experiments show that there is a possibility of ionic wave generation along actin filaments ( 7. Cantiello, Lin ). The electrical conditions are such that it is argued that most of the ions might be tightly bound round the actin filament. This sheath of ions around the filament could mean that the it acts like an electric wire. These filaments could transmit localised waves or solitons. This actin structure has effects on the surrounding water. The water molecules reorientate themselves towards the ions and at the same time break the hydrogen bond network with neighbouring water molecules. There is then a hydration cell with water molecules orientated around an ion. An experiment has shown that actin filaments are capable of supporting ionic waves. Another experiment with actin filaments produced solitary waves very similar to those in non-linear transmission lines ( 8. Kolosick, Longren, Noguchi ). Actin filaments are abundant in dendrites and axons and this means that the experimental findings about transmissions in actin filaments has implications for signalling and ionic transport within cells. There is extensive new information showing that actin filaments are linked to ion channels ( 9. Chasan, Janmey ). Actin filaments can change their configuration and it is speculated that ionic waves may be involved in this process. In neurons actin is mainly concentrated in the synaptic areas, and it is considered feasible that electrical signals through actin may help to trigger neurotransmitter release, and that in the dendrites it may be involved in the pos-synaptic response. Kaech et al ( 1. ) showed that anesthetics inhibited the actin response in dendrites. The authors expect ionic waves along actin filaments to be shown to have a broad range of effects. They say that the core of their theory is the propagation of ionic waves along actin filaments, MAPs that interact with them and C-termini on tubulins. The interaction between these and membrane components such as ion channels could produce previously undetected modulatory effects on synaptic connections. Microtubules and actin filaments are interconnected, and actin filaments are connected to ion channels. Actin bundles bind to post-synaptic densities in dendrites and dendrite spines. At the other end the actin binds to microtubules. Actin also binds to ion channels. It is envisaged that the electrical reponsiveness of the neuron may be regulated via this cytoskeletal connection to the ion channels. In this model, microtubules in dendrites receive signals from synapses via ion waves propagated along actin filaments that are connected to microtubules by MAP2. The inputs influence the evolution of an existing system. The microtubules develop the inputs by means of the changing conformation of the C-termini, with some operations recurrent where MAPs connected to adjacent MTs. Finally, the MTs produce a read out to ion channels often via wave propagation along actin filaments, and are suggested to regulate voltage sensitive ion channels. This in turn regulates the axon hillock and the output of axons potentials by changing the distribution of open and closed ion channels. The information processing in dendrites is assisted by their special lay out with short microtubules of mixed polarity connected by MAP2. It is considered possible that there could be a Hebbian-type system in which frequent activity in parts of the microtubule could produce a higher or lower actin filament density, which would constitute memory/learning. Johnson and Byerly ( 9. ) showed that agents that modified the cytoskeleton also alter calcium ion activity in some neurons. Potassium channels have been shown to be controlled by disuption of actin filaments ( 10. Maguire ). At the close of the chapter, the authors stress their key finding, which is that MTs, MAPs and actin filaments support ionic waves, and their hypothesis that these ionic waves may have a role in neural function. References:- 1) Kaech, S., et al (1999) Proceedings of the National Academy of Sciences, USA, 96, 10433-10437 + Kaech, S. et al (2001) Proceedings of the National Academy of Sciences, USA, 98, pp. 7086-7092 + Johnston, D. et al (1996) Annual Review of Neuroscience, 19, pp. 165-86 + Johnston, D. et al (1992) Annual Review of Physiology, 54, pp. 489-505 + Matus, A. (2000) Science, 290, pp.754-758 + Matus, A. et al (1982) Proceedings of the National Academy of Science, USA, 79, pp. 7590-7594 2) Fifkova, E. & Delay, R. (1982) Journal of Cell Biology, 95, pp. 345-50 + Fischer, M. et al (1998) Neuron, 20, pp. 847-54 + Fischer, M. et al (2000) Nature Neuroscience, 3, pp. 887-894 + Landis, D. & Reese, T. (1983) Journal of Cell Biology, 97, pp. 1169-1178 3) Crick, F. (1982) Trends in Neuroscience, 5, pp. 44-46 + Dunaevsky, A. et al (1999) Proceedings of the National Academy of Sciences, USA, 96, pp. 13438-13443 4) Colicos, M. et al (2001) Cell, 30, pp. 605-616 + O' Leary, D. & Koester, S. (1993) Neuron, 10, pp. 991 + Sanes, J. & Lichtman, J. (1999) Annual Review of Neuroscience, 22 + Scott, E. & Luo, L. (2001) National Neuroscience, 4, pp. 359-365 + Wang, Y. (1985) Journal of Cell Biology, 78, pp. 1955-1964 + Zhang, W. & Benson, D. (2001) Journal of Neuroscience, 15, pp. 5169-5181 + Weimann, J. et al (1999) Neuron, 24, pp. 819-31 5) Dehmelt, L. & Halpain, S. (2004) Journal of Neurobiology, 58, pp. 18-33 + Dehmelt, L. et al (2003) Journal of Neuroscience, 23, pp. 9479-90 + Letorneau, P. (1996) Perspect. Dev. Neurobiology, 4, pp. 111-123 + Letorneau, P. & Ressler, A. (1984) Journal of Cell Biology, 98, pp. 1355-1362 6) Khuchua, Z. et al (2003) Neuroscience, 119 pp. 101-6 + Woolf, Nancy et al (1999) Brain Research, 821, pp. 241-9 + Wong, R. et al (2002) Proceedings of the National Academy of Sciences, USA, 99, pp. 14500-14505 7) Cantiello, H. et al (1991) Biophysics Journal, 59, pp. 1284-9 + Lin, E. & Cantiello, H. (1993) Biophysics Journal, 65, pp. 1371-8 8) Kolosick, J. et al (1974) Proceedings IEEE, 62, pp. 578-581 + Lonngren, K. (1978) In: Lonngren, K. & Scott, A. Eds Solitons in Action Academic Press + Lonngren, K. et al (1975) IEEE Trans Circuits and Systems, CAS-22, pp. 376-78 + Noguchi, A. (1974) Elec. and Comm. In Japan, 57-A, pp. 9-13 9) Chasan, B. et al (2002) Eur. Biophysical Journal, 30, pp. 617-624 + Janmey, P. (1998) Physiology Review, 78, pp. 763-81 Gregory Engel et al Dept. of Chemistry, University of California, Berkeley Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems Nature, vol 446, pp. 782-786, 12 April 2007 This paper points out that photosynthetic complexes are adapted to capture light, and put its energy into long-term storage. This process has normally been described in classical terms, and quantum coherence has been to a good extent ignored in the traditional analysis. However, the possibility of quantum coherence has been predicted, and in this paper the authors describe evidence for long-lived quantum coherence being involved in energy transfer within photosynthetic systems. The wavelike process is thought to account for the efficiency of the sytem, because it allows the sampling of large areas of phase space, in order to find the most efficient path, or to transfering energy to the area in the lowest energy state. The Engel et al experiment involved electronic spectroscopy to observe the evolution of electronic coherence. Quantum beating was found to last for 660 fs, which was much more than the 250 fs estimated for conventional models. Conventional models had assumed that quantum coherence would be rapidly destroyed, and had therfore not factored it into their models of photosynthetic systems. By contrast, the authors conclude that long-lived quantum coherence must play an active role in photosynthetic systems. A quantum coherent system allows sampling in order to direct energy to the lowest energy state. The system is viewed as performing a quantum computation, in which it senses many states simultaneously and from these selects the correct answer. This is seen as analogous to Grover's algorithm, allowing both the discovery of the lowest energy state and the transfer of coherence. This is more efficent than any classical search engine. Protein is seen as providing the structure in which coherence can be preserved and at the same time modulating the coherence as a result of the local dielectric environment. Photon echo experiments at Starlab Pierre St. Hilaire, Dick Bierman & Stuart Hameroff www.starlab.org The authors claim that quantum consciousness models are testable, which is the acid test of a scientific theory as opposed to conventional or classical theories, which are claimed not to be testable. Physicists Pierre St.Hilaire and Dick Bierman devised a test for evidence of quantum coherence in the retina, the most conveniently accessible part of the brain. Their scheme was to send two separate laser pulses to the part of the retina being studied. This is expected to cause rhodopsin molecules in the rod and cone cells to become quantum coherent. If the coherent state persists for longer than the time between the two laser pulses, then some atoms in the system precess back towards their original state, and they emit a photon in the process of doing this. This is known as a ‘photon echo’. This could be detected, and if detected it would be indicative of quantum coherence in the retina. Structures in the retina such as rhodopsin could in principle be shown to sustain macroscopic quantum coherence. No existing experiments of this kind were known to the authors, but research into the isomerisation of rhodopsin suggests the presence of quantum coherence. The brain is both neurocomputer and quantum computer Stuart Hameroff Dept. of Anesthesiology and Psychology, University of Arizona Health Sciences Centre Cognitive Science, 31, (2007), pp. 1035-1045 This paper is a reply to an article published by Cognitive Science in 2006 criticising the Penrose-Hameroff model of quantum consciousness from the point of view of mainstream consciousness theory (1. Litt et al, 2006). The criticisms make depressing reading for any serious student of consciousness. Not only do they do nothing to plug any explanatory gaps in the mainstream ideas of the previous decade, but they retain a remarkably casual attitude towards any evidence for or against theories that deviate from the mainstream. In much of his paper, Hameroff merely reiterates the long standing proposals of Orch OR, and here we will concentrate on the specific point on which he counter attacks the Litt et al paper. Litt et al advance the now ancient argument that microtubules are widespread in organisms, and if neurons produced consciousness as a function of their microtubules, then large parts of many diverse organisms including plants would be conscious. As many times before, Hameroff points out that neuronal dendritic microtubules differ from other microtubules in several ways. They are much more densely packed than in other cells, and are unique in having mixed polarity, with short microtubules formed into anti-parallel networks (2. Woolf, 1998). Also, the 17 different types of tubulin protein subunits found in neuronal microtubules are more numerous than those found in other microtubules (3. Lee et al, 1986), and Hameroff thinks that this may be connected to information processing. Another golden oldie trundled out by Litt et al is that if Penrose’s objective reduction (OR) were to be true, quantum theory would have to be rewritten. Many in neuroscience and philosophy seems to regard this as a strong argument against Penrose, apparently oblivious of the multiple problems in existing quantum theory, including incompatibility with relativity, lack of description of underlying reality, and lack of clear understanding of the collapse of the wave function. Litt et al revisit the exchange between Max Tegmark (4.) and Hagan, Hameroff and Tuszynski (5.) at the beginning of this decade. They accept the possibility of longer decoherence times than those claimed by Tegmark, but interpret this as only applying to individual tubulin subunits, which are too small to be significant for neural processing. Hameroff counters that Orch OR applies to bundles of dendritic microtubules, and extends to other neurons via gap junctions. Separately to this Hameroff indicates recent studies that show quantum correlations between electrons between neurotransmitters and receptor proteins. (6. Brookes et al, 2006) (7. Kang & Green, 1970), (8. Nichols, 1986), (9. Snyder & Merill, 1965). Litt et al claim that biochemical explanations of anesthesia have surpassed quantum mechanical versions. Hameroff counters by stating that anesthetic gases act via quantum London forces in hydrophobic pockets in receptor and other proteins to inhibit electron resonance. These are quantum mechanical rather than biochemical processes. Hameroff also mentions a more recent extension of the Orch OR theory that claims that the precise global nature of the gamma synchrony in the brain can only be accounted for by long range quantum correlation (10. Freeman & Vitiello, 2006). References:- 1.) Litt, A. et al, (2006) - Is the brain a quantum computer? - Cognitive Science, 30, pp. 593-603 2.) Woolf, N., (1998) - A structural basis for memory storage in mammals - Progress in Neurobiology, 55, pp. 59-77 3.) Lee et al, (1986) - Biochemical and chemical properties of tubulin sub-species - Annals of the New York Academy of Sciences, 466, pp. 111-128 4.) Tegmark, M. (2000) - The importance of quantum coherence in brain processes - Physical Reviews E, 61, pp. 4194-4206 5.) Hagan, S., Hameroff, S., & Tuszynski, J. (2002) - Quantum computation in brain microtubules? Decoherence and biological feasibility - Physical Reviews E, 061-901 6.) Brookes et al, (2006) - Could humans recognise odour by phonon assisted tunnelling - http://www.arxiv.org/abs/physics/0611205 7.) Kang, S. & Green, J. (1970) - Steric and electronic relationships among some hallucinogenic compounds - Proceedings of the National Academy of Sciences, USA, 67, pp. 62-67 8.) Nichols, D. (1986) - Studies of the relationship between molecular structure and hallucinogenic activity - Pharmacology Biochemistry and Behaviour, 24, pp. 335-40 9.) Snyder, S. & Merrill, C. (1965) - A relationship between the hallucinogenic activity of drugs and their electronic configuration - Proceedings of the National Academy of Sciences, USA, 54, pp. 258-266 10.) Freeman, W. & Vitiello, G. (2006) – Nonlinear brain dynamics as macroscopic many-body field dynamics - Physics of Life Reviews, 3, pp. 93-118 Engel, G. et al (2007) - Evidence for wavelike transfer through quantum coherence in photosynthetic systems - Nature, 446, pp. 782-786 The geometry of π electron resonance clouds Stuart Hameroff June 19th 2007 Living organisms are seen as being characterised by self-organisation, maintenance of a stable internal environment, metabolism (energy utilisation), growth, adaption, reproduction and evolution. The uniqueness of living systems is often attributed to emergent properties of biochemical and physiological processes. The idea of emergence, much touted in consciousness studies, assumes an hierarchical organisation in which a novel property arises from the interaction of simpler components. Thus candle flames emerge from the interaction of gas and dust particles. In his 1944 book, ‘What is Life?’ Schrödinger suggested a quantum basis for life. His ideas relate to the subsequently discovered structures of DNA, RNA, cytoskeletal proteins such as microtubules and actin. Schrödinger also proposed that living systems might involve non-local quantum correlations. Mainstream science moved away from the idea of quantum processes in living organisms during the second half of the 20th century, although a few physicists such as Fröhlich kept the idea alive. Fröhlich proposed that biochemical energy could pump quantum coherent dipole states in geometrical arrays of non-polar π electron resonance clouds. Such electron clouds are now known to be isolated from water and ions, and present in cells within membranes, microtubules and organelles. These electron clouds can use London forces, involving interaction between instantly forming dipoles in different electron clouds, to govern the conformation of biomolecules, particularly proteins. The solid parts of cells include membranes and protein structures and these have within them hydrophobic areas containing oil-like structures with π electron resonance clouds. In water, non-polar oily molecules such as benzene, which are hydrophobic are pushed together, attracting each other by London forces, and eventually aggregate into stable regions shielded from interaction with water. London forces can govern the configurations of protein in these regions. Such regions occur as planes in membranes and as pockets in proteins. In some structures, notably the microtubule lattices, π electrons are less than two nanometres apart, at which distance they can become entangled. The mainstream view has discounted quantum processes on the basis that in the conditions within living organisms, they would decohere too rapidly to be useful for biological processes. Hameroff has for a long time argued that the microtubules could be screened from the general environment of the brain. Some recent studies suggest that biomolecules can use the energy of the system to support quantum states (1. Engel et al) (2. Ouyang & Awscalom). The Engel study demonstrated quantum coherence as driving photosynthesis at normal temperatures for plant life. Photons travel through all the possible pathways of the protein scaffold surrounding photosynthetic chlorophyll. Ouyang & Awschalom demonstrated that quantum spin transfer through benzene π electrons clouds is enhanced in efficiency as temperature rises, which is opposite to what conventional thinking would have predicted. Life is based on carbon chemistry and notably carbon ring molecules, such as benzene, which has electron resonance clouds in which London forces are active. Carbon has four atoms in its outer shell, able to form four covalent bonds with other atoms. In some cases two of the electrons form a double bond with another atom, and the remaining two outer electrons remain mobile and are known as π electrons. In benzene, there are three double bonds between six carbon atoms, such that all six carbon atoms are involved in a bond. The ring structure, into which these atoms are formed, famously came to its discoverer, Friedrich von Kekule, in a dream of a snake biting its tail. There are varying configurations of the bonds and the π electrons and the molecule resonates between these stable configurations. Benzene rings and the more complex indole rings are referred to as aromatic rings and make up several of the amino acid side groups that are attached to proteins. The indole rings also resonate between states. Some larger biomolecules can have a polar hydrophylic end and a non-polar hydrophobic ring end. Components of the lipid cell membrane take this form. Membranes comprise double layers of such molecules, with an internal non-polar and hydrophobic region. However, Hameroff regards the cell membrane as too fluid and lacking in lattice structure to make it a good candidate for information processing. Proteins look to be more suitable in this respect. Proteins constitute the driving machinery of living systems, since it is they which open and close ion channels, grasp molecules to enzymes and receptors, make alterations within cells, and govern the bending and sliding of muscle filaments. The organisation of protein is still poorly understood. Proteins are formed from 20 different amino acids with an enormous number of possible sequences. Van der Waals forces are involved in the proteins folding into different conformations, with a huge number of possible patterns of attraction and repulsion between the side groups of the protein. During the protein folding process there are non-local interactions between aromatic rings, which has been seen as suggestive of quantum mechanical sampling of possible foldings (3. Klein-Seetharaman). Once formed a protein structure can be stabilised by outwardly facing polar groups and by regulation from non-polar regions within. The coalescence of non-polar amino acid side groups such as two aromatic rings can result in extended electron clouds constituting hydrophobic pockets. Protein conformation represents a delicate balance between forces such as chemical and ionic bonds, and as a result London forces driven by π electrons in hydrophobic pockets can tip the balance and thus govern conformations of protein. Anaesthesia appears to be another process involving hydrophobic pockets in protein. A century ago Meyer & Overton showed that the potency of anaesthetic gases correlated with their solubility in lipid-like mediums, and for a long time this was assumed to mean that the lipid cell membrane was the site of action. However, Franks and Lieb (4.) showed that anaesthetic agents act in the hydrophobic pockets in protein, by means of London forces. Microtubules are comprised of the protein tubulin. Tubulin has a dimer form with an alpha and beta monomer joined by a ‘hinge’. The tubulin has a large non-polar region in the beta monomer just below the ‘hinge’. Other smaller non-polar regions with π electron rich indole rings, are distributed throughout the tubulin with distances of about two nanometres between them. The positioning of π electron clouds within about two nanometres of one another is suggested to allow the electrons to become entangled. This entanglement can spread through the microtubule and to other microtubules in the same dendrite and then via gap junctions to other neurons, thus allowing a macroscopic quantum state to extend over a large region of the brain. Hameroff argues for the information processing potential of microtubules. This idea goes back to Sherrington, the prominent mid-20th century neuroscientist. The microtubules are formed of 13 filamentous tubulin chains skewed so that the filaments run down the cylinder of the microtubule in a helical form, and hexagonal in that each tubulin dimer has six neighbours. It is argued that if each tubulin stands for an information bit, then the form of this microtubule lattice is suitable for computation. In simulations, interactions with neighbouring tubulins allow processing of information with memory coded into modification of the tubulins (5. Rasmussen et al). The skewed form of the microtubules means that where winding patterns intersect, they coincide with the attachment of microtubule associated proteins (MAPs), which link microtubules into a scaffolding. In simulations these sites correspond with coherent phonon resonance in the lattice (6. Samsonovich). Hameroff also suggests that the Fibonacci series of the winding pathways on the microtubules match biochemical resonances, and may provide for quantum error correction, which can help to prevent decoherence. References:- 1.) Engel, G. et al (2007) - Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems - Nature, 446, pp. 782-786 2.) Ouyang, M. & Awschalom, D. (2003) - Coherent spin transfer between molecularly bridged quantum dots - Science, 301, pp. 1074-1078 3.) Klein-Seetharaman, J. et al (2002) - Long-rang interactions with a non-native protein - Science, 295, pp. 1719-22 4.) Franks, N. & Lieb, W. (1984) - Do general anaesthetics act by competitive binding to specific receptors? - Nature, 310, pp. 599-601 5.) Rasmussen, S. et al (1990) - Computational connectionism with neurons: A model of cytoskeletal automata subserving neural networks - Physica D, 42, pp. 428-49 6.) Samsonovich, A., Scott, A. & Hameroff, S. (1992) - Acoustoconformational transitions in cytoskeletal microtubules: Implications for information processing - Nanobiology 1, pp. 457-468 Tuszynski et al (1995) - Ferroelectric behaviour in microtubule dipole lattices: implications for information processing, signalling and assembly/disassembly - Journal of Theoretical Biology, 174, pp. 371-80 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 |
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