(Short form; full version also available)
It is a working assumption of cognitive science that all fast computations in the brain are done by neurons. However, this is only a working assumption. Biology is built on marvellous and intricate mechanisms right down to molecular level, and some of these mechanisms (other than transmission of neural impulses) might be used for computation and information storage in the brain. We do not yet know enough about brain function to rule out the possibility. So we should from time to time question our working assumption, and explore the alternative — that the brain uses a hybrid of neural and non-neural computation.
However, there is no point in postulating non-neural mechanisms without sound biological reasons. Any non-neural mechanism must help the brain meet some strong selection pressure, in a way that neurons alone cannot.
This paper investigates the idea that a non-neural mechanism - storing a 3-dimensional metric representation of local space as a wave excitation - contributes to a vital cognitive function - the planning and control of movements. The wave storage is located in the thalamus.
If we then assume that the wave storage is the seat of conscious experience, we get a highly constrained and predictive theory of consciousness. It agrees with the major facts of conscious experience, in a direct and unforced manner.
Is it implausible that neurons should couple to a wave-like excitation in the brain? Arguably no more so than their coupling to a wave-like excitation in the retina. Both evolved to meet the same massive selection pressure - the need to know where things are, as precisely as possible, at every moment. It would be implausible if the precise optics and geometry of the eye were not matched by a precise geometric representation inside the brain; wave excitations, I propose, do that job better than any neural mechanism can.
Neural theories of the brain have two missing elements:
There is a remarkable match between these two gaps - in that our conscious experience (2) feels like a 3-D model of local space (1). This paper argues that it is no coincidence, because the same ingredient fills both gaps - a wave storage mechanism in the thalamus, which is the basis of both 3-D working memory and consciousness.
In order to arrive at a novel account of consciousness, we need to make several hypotheses. As some of these are unfamiliar, the reader may suffer from hypothesis overload, straining the credibility of the theory. However, at base the theory makes one hypothesis:
A. There is something non-neural going on in the brain; that something is a spatial working memory, and is the seat of consciousness.
This is the sine qua non of the theory, by which it stands or falls. The other more detailed hypotheses - wave storage of spatial information, a Bose condensate to support the waves, and the location of wave storage in the thalamus - are in some sense illustrative, ‘existence proofs’ that we can build a coherent theory along these lines in agreement with the data. While each detailed hypothesis is the current best candidate, any one of them might be replaced or modified without losing the essence of the theory. If they are replaced by better ones, so much the better.
Sections 1 and 2 argue on functional grounds (irrespective of consciousness) that a non-neural spatial memory may serve a vital biological function. Section 4 shows how this same non-neural memory can bridge the explanatory gap to consciousness, in good agreement with the facts. In between, section 3 proposes a possible mechanism and location for the non-neural component, introducing the detailed hypotheses above. The core of the theory is in sections 2 and 4, with the more speculative details in 3.
1. We should allow the possibility of non-neural information storage in the brain
1.1 Neurons are slow and have low output bandwidth; they may not be the best way to do all brain functions.
1.2 Neurons couple to signals at very low intensities, so they may couple to forms of energy in the brain which are hard to detect.
1.3 Information storage is simpler than computation; non-neural memory may be particularly feasible.
We have come to regard the neuron as the general-purpose workhorse of the brain, for both computation and information storage.
However, neurons have limitations; they are slow devices, with a latency of around 10 milliseconds, and have limited output bandwidth (of the order of 1 bit per 10 ms). It is hard to see how the most time-critical cognitive functions are done in the very fast times, of 100 ms or less, which we observe.
Maybe we will discover ingenious highly-parallel neural architectures which solve these problems. An alternative is to investigate those aspects of cognition where the problems are most severe, to see whether some hybrid neural/non-neural mechanism may do better than a purely neural design. That is the approach of this paper.
If such a hybrid exists, it is not clear that we would have detected it yet. Neurons couple to other forms of information at very low energy levels (e.g at the one-quantum level, in the retina). As energy consumption in the brain is at a premium, we would expect any non-neural mechanism to use low-energy physical effects, coupled to highly sensitive neurons. These low-energy effects might be hard to detect in living brains.
To find any such effects, I suggest we should look at:
2. The representation of local spatial information may require non-neural storage
2.1 Knowing the disposition of objects in local space is vital for survival, giving very strong selection pressure on the brain.
2.2 It requires a working memory for spatial relations, in an unrestricted 3-D metric representation of local space.
2.3 Having one master map of local reality is simpler and more efficient than just having multiple cooperating sensory maps.
2.4 For an unrestricted 3-D representation of local objects, neural storage has problems of information capacity and wiring.
To know what drives the design of brains, we must understand the strongest selection pressures. The strongest pressures are on brain functions used every second of the day, because any failures in these give large deficits in lifetime fitness.
Therefore we consider the control of (non-routine) physical movement. If a monkey in the forest canopy - like one of our primate ancestors - makes even a few movement errors per day, his fitness is much decreased. Each movement must fit the spatial dispositions of local objects and one's own body. Typical questions to be answered, in planning movements, are:
Q1. Can I jump onto that rock over there ?
Q2. If I move my paw from X to Y, will it hit anything on the way ?
Q3. When the branch bends, were will I end up ?
Answering these requires `what if' computations about the dynamics and movements of local objects. These are metric computations, using operations of 3-D vector geometry (e.g to find the 3-vector displacement between two objects). Movement is geometry in time.
Input for these computations comes from the senses, particularly visual and somatosensory, which give geometric information about the positions of objects. It is not enough just to have this information as it arrives; data over the last few seconds is also essential. For best performance we need a very short-term memory (a working memory) for the shapes, positions and movements of local objects.
This might be stored in several possible encodings, but the stringent demands of movement planning narrow the choices. Anything which loses geometric accuracy, or requires lengthy decoding, will be less fit. I propose that the working memory is an unrestricted 3-D metric representation of local space, which minimises both distortion and decoding time.
This representation needs to be:
Unrestricted: to model an unlimited range of arbitrary objects and shapes - unlike a symbolic representation, which models a restricted set of standard shapes denoted by its symbols. Real rocks and trees have arbitrary shapes, which must be modelled faithfully to plan movements.
Three Dimensional: because reality is 3-dimensional, and movements are executed in three dimensions; anything less than a 3-D representation would introduce dangerous ambiguities.
Metric: because movement questions are metric questions, requiring actual distances and angles (or the equivalent) to answer them. These need to be retrieved directly and accurately to answer the questions.
A Representation: because the number of movement questions which may arise, and the information required to answer them, is unlimited. A simple catalogue of facts would have insufficient capacity; we need a map-like representation, which implicitly contains all geometric facts.
We require an analogue representation (Sloman 1974), or map of local space, rather than a symbolic representation. The drawbacks of symbolic representations are (a) imprecision in representing arbitrary shapes and (b) time delays in reconstructing geometric information from a symbolic encoding. For instance, Marr's (1982) 3-D model is symbolic, and has these limitations.
The encoding of the visual field in the V1 visual cortex is an unrestricted representation, modelling arbitrary shapes in two dimensions. Representations in later visual areas are typically more symbolic.
For planning bodily movements, the 3-D metric representation must integrate spatial information about the body (from somatosensory data) with information about other local objects (e.g. from vision) in a consistent 3-D model. Having to deal with at least two modalities of sense data, it might also be used to integrate all modalities. It would then serve as a master map of local 3-D reality.
Neuroanatomists have looked for such a master map in the brain and not found it. There are many two-dimensional topographic maps in the cortex, but none of them seems to be a primus inter pares, or convergence region for all the others (see e.g. Damasio 1989, Crick 1995); because we have not yet found any master map, the prevailing view is that our model of reality is built up cooperatively by the many cortical maps.
However, there are good reasons why one `master map' is more effective than equal cooperating maps. To see this, consider the two alternative neural architectures, shown in figure 1.
Figure 1: Connectivity required for (a) master map and satellites, and (b) cooperation of many maps
The advantages of the `master map' architecture are:
(1) Translation Complexity : For comparisons, feature descriptions must be translated and aligned between maps. For the master map architecture, with N maps, this requires 2N simple translations. For the cooperating maps case, it requires N(N-1) more complex translations.
Extra complexity is costly when coping with changes. We know that cortical sensory maps can change within a lifetime. If so, the mapping between maps must change with them; this seems unnecessarily complex and difficult in the cooperating maps architecture.
(2) Required calculations : The task of perceptual processing is to find the most likely state of the world, for given sense data - or to find that state of the world which maximises the likelihood, P(state|sense data). By Bayes' theorem, this is a product of terms P(sense data|state) for the different modalities of sense data. The state is the 3-D model of local space in the centre of figure (1a), so the radial connections are just the links required to calculate each P(sense data|state). While the links of figure (1b) might be used to compute P(sense data|sense data), that is just not what brains need to calculate.
(3) Fast Decision-making : A committee with a chairman can decide any issue by a simple vote; but with no chairman, the decision is reached by an iterative process in which local coalitions form, and then vote against other coalitions. In the distributed decision-making of figure 1(b), sub-groups of sensory maps may agree with one another about some feature which is later overridden by information from other maps. One sensory map may have several successive `changes of mind' during this settling process. This adds to the decision-making time.
Therefore the master map architecture has advantages of efficiency and simplicity over the multi-map alternative. If there is such a master map, which is an unrestricted 3-D representation of local space, how might it be realised in the brain ?
There are few published accounts of how an unrestricted neural 3-D neural representation might work, and equally few candidate neural structures in the brain. This reflects two difficulties:
Information capacity: Most neural models of short-term memory use an encoding in the firing rates of neurons, maintained by positive feedback - rather than short-term synapse modification. This gives an information capacity of the order of 1 bit/ neuron. To store a grid of resolution 1 in N in two dimensions requires N2 neurons, which may be feasible (eg in the V1 visual cortex); in three dimensions it requires N3 neurons (i.e of order 10**9) which for a small mammal is prohibitive.
Wiring and Topology: The cortex is 2-dimensional, so it can store 2-D unrestricted analogue representations, representing place by place; the third dimension is used to take information in and out of the representation. To represent 3-D information in the same way would require a three-dimensional clump of neurons. There would be severe problems of wiring, to get the information in and out of the centre, through the barrier of outer neurons. A 3-D clump must have some inhomogeneities (such as blood vessels) making gaps in the representation.
So the direct analogue approach (representing local space by a neuron-filled space in the brain) appears not to work, and we are forced to consider alternatives - such as a distributed representation, or a 2-D representation enhanced with depth information. These lose the directness and simplicity of the `representing places by places' option, and require extra decoding time to deliver the information needed for geometric computations.
I do not imply that a neural encoding of unrestricted 3-D spatial information is impossible - just that it presents serious design and performance problems which have not, as far as I know, yet been addressed. It is a challenge to connectionist researchers to find such mechanisms. Meanwhile we can investigate the alternative, that a non-neural mechanism is used for this purpose.
3 A wave excitation in the brain may store local 3-D spatial information
3.1 Wave excitations can store large amounts of 3-D information efficiently and faithfully
3.2 Spatially selective retrieval can deliver the information needed for the geometric computations of movement
3.3 3-dimensional wave storage may occur in the thalamus
3.4 The waves may propagate on a superfluid-like state of matter.
A plane wave in a three-dimensional medium has the form E = E0 cos(k.x) , where E is a position-dependent parameter of the medium (such as polarisation), x is position in the medium, and k is the wave vector. Since k is a three-dimensional vector, one plane wave can represent a point at an external position r, where k = µr and µ is a constant. If the wave equation in the medium is linear, there can be very many different waves in the same region, with different wave vectors k , without any interference between them. The waves can represent an arbitrary collection of points in three-dimensional space.
So a wave excitation can be an unrestricted 3-D metric representation of objects in space. This representation is similar to a hologram, with similar advantages:
Suppose there is such a wave excitation somewhere in the brain, used as the 3-D metric representation of local space. Certain neurons have transducers (analogous to the light-receptive structures in rods and cones) which couple to the oscillations, to store and retrieve the information.
If each such neuron has tranducers on its dendrites distributed in the medium so that it is sensitive to one wave vector k, it needs many such transducers to get good resolution in k; but these need not cover the whole wave medium, so we can satisfy any neuron wiring constraints. It acts like an extended antenna in the medium. However, if each input/output neuron had a fixed wave vector, then to store and retrieve information about N**3 points we would need N**3 neurons - no saving over a pure-neural design. There are two possible solutions:
(a) Tunable antennae : The sensitivities of the tranducers on one neuron could be altered dynamically (e.g by synapses from other neurons) to tune or `steer' the antenna to different wave vectors. This can be used for both storage and retrieval - transmission and reception.
(b) Non-linear `searchlight' retrieval: The transducers are sensitive to the wave excitation E in a non-linear manner; for instance, they measure E**2. To retrieve information about objects in the vicinity of some spatial position r0 , the medium is stimulated by an extra `searchlight' excitation with wave vector k0 = µ r0 . Memory traces for spatial positions near r0 (i.e. excitations with wave vectors near k) beat with this excitation producing slowly-varying changes in E**2. Neurons tuned to this slowly-varying component of E**2 observe memory traces for objects near r0 .
Either (a) or (b) removes the need for very large numbers of retrieval neurons. The non-linear method, while selectively retrieving information about objects in a small spatial neighborhood, also computes the three-vector displacements of points relative to the centre of the neighborhood.
This spatially invariant retrieval is of great help for many important computations. It is useful for both movement computation (because the laws of motion and geometry are spatially invariant) and for spatially invariant object recognition.
The wave storage mechanism meets all the requirements for a spatial working memory. It is:
Its inputs and outputs are summarised in figure 2.
Figure 2: Information flows to and from the unrestricted 3-D spatial representation
Where might such a representation be located in the brain? The cortex, being a two-dimensional structure, is not suitable; more likely candidates are in central structures. Of these, there are reasons to favour the thalamus:
1. Shape: Each thalamus has roughly equal size along all three dimensions; it can support excitations with well-defined wave-vectors in three dimensions,with neurons in thalamic nuclei acting as transducers.
2. Required inputs and outputs: The thalamus is directly coupled to sense data of all modalities, and has reciprocal paths to many cortical areas, to support all the information flows of figures 1 or 2.
3. Attention-Focusing Mechanisms: There need to be control mechanisms to direct the focus of input and output to the 3-D representation. There are several lines of evidence (reviewed by Newman 1995, 1996) that the thalamic reticular nucleus serves this purpose. Theoretical models for this attention gating have been proposed (e.g. Crick 1984; Taylor & Alavi 1993).
4. Evolutionary Timescales: The need to understand the shapes and places of objects in local space has probably been a strong selection pressure for up to half a billion years. We would not expect the brain's response to this pressure to be localised in recent brain structures, such as the neocortex. The thalamus is an older structure.
5. Distinctive Synaptic Structure: Wave transducers are probably associated with synaptic structure; there are distinctive large synaptic aggregations in all thalamic nuclei, involving the dendrites of relay cells and interneurons (Jones 1985; Steriade et al 1990).
We can sketch a possible thalamic architecture for wave storage and retrieval. Each thalamic relay cell receives dense subcortical input of some sensory modality on its proximal dendrites. Its distal dendrites act as an antenna, to store and retrieve from the wave excitation. This antenna is dynamically tuned to specific wave vectors by corticothalamic afferents (which terminate on the distal dendrites) and interneurons. Thus the relay neurons both pass sensory information on to the cortex, and store it in the wave excitation; cortical feedback tells them where to store it [1] (i.e where in represented space to put each sensory input) or retrieve it from. Non-specific nuclei may have a retrieval-only function such as the non-linear `searchlight' retrieval mechanism.
The thalamus is not currently favoured as a site for a master map of local space, because specific thalamic nuclei have few interconnections with one another, and so do not seem to be part of any integrated map (Mesulam 1985). In this theory all the specific nuclei couple to the wave storage which permeates the thalamus, and which is the master map.
What biophysical mechanisms might underlie the wave storage ?
High-temperature superconductors show us how a remarkable state of matter can exist near room temperature in complex materials, for reasons we did not suspect until it was observed. This remarkable state - the Bose-Einstein condensate, where very many quanta are in the same quantum state - is frictionless, so it can store information for long periods, insulated from thermal noise (Tilley & Tilley 1990). This makes it a candidate basis for biological information storage.
I propose that there is some form of Bose-condensed superfluid state in the thalamus, which acts as the substrate for the wave storage.
Could there be a high-temperature superconducting or superfluid state in the brain, which acts in this way ? There are three reasons not to rule out this possibility too hastily:
There is one proposal for a superfluid-like mechanism in biological material. This is the coherent polarisation oscillation investigated by Frohlich (1968), which uses an energy pumping mechanism to sustain a Bose condensed state. His theoretical analysis is controversial, and some other Bose condensed state might equally be the basis of a `superconducting' memory in the brain; but Frohlich's proposal illustrates how such effects might occur in the brain at room temperature.
4. Bose-Condensed states may be the basis of consciousness
4.1 In a hybrid theory of cognition, it is possible that only the non-neural wave component is conscious.
4.2 If any Bose condensate consciously experiences its own quantum state, then for the condensate in the brain, that experience is a 3-D model of local space.
4.3 This makes clear predictions for the limits and the nature of conscious experience. They agree well with the evidence.
Neural theories of consciousness give us no clear grounds to understand how some specific pattern of neural firing gives rise to phenomenal consciousness - our experience of what it feels like to be us - while most neural activity does not.
If there are two distinct computational mechanisms in the brain (neural and wave storage) then we may be able to understand phenomenal consciousness in this two-component theory - by assuming that only the non-neural component is conscious. How well does this two-component theory fit the facts ?
To make the theory specific, we assume, following Marshall (1989) a law of nature that (1a) Bose-Einstein condensates are conscious. This is a plausible candidate law, being simple in form, and relating consciousness to a fundamental and pure state of matter. Consciousness arises from very many quanta in an identical macroscopic quantum state.
We further assume that (1b) the form of conscious experience is the form of the shared quantum state. From this we expect the Bose-condensed excitation which is the master map in the thalamus to experience its own form - a map of local space.
Having tied our account of consciousness to a simple law of nature, defining the form of experience by (1b), we now have little room for manoeuvre in explaining its properties. They have to be the properties of the 3-D spatial representation; in describing what phenomenal consciousness is like, the buck stops here. There is no homunculus to observe the condensate, and to somehow make its properties right. If our theory gets those properties wrong, there is little we can do about it; the theory is easily falsifiable.
In the tests which follow, we are concerned with phenomenal consciousness, the `what it is like' aspect, rather than, for instance, access consciousness (having information available in the brain for other uses, such as verbal report) (Block 1995). The data for the comparisons is your own experience.
(1) Consciousness is about sense data of all modalities: All modalities of sense data converge in the thalamus, in the 3-D representation.
(2) Consciousness is about the present moment: The 3-D representation is a working memory, concerned with the very recent past. (Awareness of long-term memories uses the same mechanism, but stimulated more weakly by stored memories rather than sense data.)
(3) Consciousness is located in space : All qualia are located in a represented space. The 3-D representation must have a location for each memory trace, and we expect each form of sense data to be as precisely located as is required for sensory integration and movement calculation - visual data very precisely, sounds and bodily sensations rather less so. This is just what we feel.
(4) Consciousness is an undistorted spatial model of reality: Consciousness is qualia with shape - defining the shapes and places of real rocks, trees, or bodies, vivid and undistorted. It might not have been so transparent and precise. It might have been a set of symbolic labels for `chair', `dog' etc. distributed in space; or it might have been a distorted map. However, it is an undistorted metric map of reality - just like the wave representation of local space.
(5) Phenomenal consciousness correlates with access consciousness: Consciously experiencing something correlates with that information being available in the brain for many kinds of functional processing (eg for making verbal reports, or for memorising) which do not a priori have anything to do with phenomenal consciousness . The 3-D spatial representation (which by hypothesis, is the seat of phenomenal consciousness) makes information available to many other subsystems in the brain (leading to access consciousness); so the theory predicts that the two are correlated.
(6) Consciousness has a variable focus of attention: Within our general awareness there is a changing focus on particular spatial sub-regions, and consciousness is more vivid and clearly defined within this focus. Thalamic attention mechanisms act so that regions of the 3-D model have more intensive retrieval to cortical areas (producing a focus of access consciousness), accompanied by more intensive feedback from those cortical areas into the 3-D representation - sharpening the model in those regions and producing a focus of phenomenal consciousness. This explains why the foci of access consiousness and phenomenal consciousness move in step.
(7) There is a unity in consciousness: We feel that the continous stream of overlapping qualia are all happening on one conscious `stage', not on separate screens of a multi-screen cinema. This matches with the Bose-Einstein condensate, which is a single quantum state, shaped to reflect the model of the local world [2]. A key role of the working memory is to create one unified model of the world from all the modalities of sense data; so we expect that the experience within the Bose condensate is experience of a unified reality.
(8) Much brain activity is not conscious : For instance, the processes to generate or understand language, and many learning processes, are outside consciousness. As they are neural processes, they are not expected to be conscious.
(9) Damage to the thalamus interrupts consciousness : Clinical evidence shows that the thalamus, in particular the Intra-Laminar Nuclei, is essential for waking consciousness; no region of the cortex, and very few other brain areas, have this property (Bogen 1995). This clearly agrees with a theory in which conscious awareness is located in the thalamus.
The theory does not yet account neatly for the diversity or nature of qualia, although it can give some account of them. However, in properties (1) - (9), the theory is in remarkable and unforced agreement with the main facts of consciousness. The 3-D spatial working memory is about just the same things that consciousness is about - no more, and no less - and has a very similar character.
In the pre-war study of nuclear beta decay, something was missing. Some of the mass-energy and spin of the initial state could not be found in the decay products. Taking this seriously led to the discovery of the neutrino, a particle with interactions so weak that it can travel through the earth without hitting anything; a particle which was not to be observed directly for another twenty years - but which is a vital part of the fundamental theory of matter.
I have argued that for neural theories of cognition, something equally important is missing:
This is a bit like beta decay without the neutrino, or Hamlet without the prince. We can either press on hopefully, or take the gaps seriously as clues to something missing - something which, like the neutrino, may be so subtle as to have evaded detection.
I propose that one missing ingredient fills all these gaps - a wave-like representation of local space, stored in the thalamus, using a superfluid-like state of matter. This is the metric map for motion calculations and the master map for integrating sense data. If we then assume that consciousness resides in Bose-condensed states, it gives a highly constrained and predictive theory of consciousness. It seems to agree well with the evidence - it successfully predicts what is conscious and what is not, it predicts the unity of consciousness, and predicts much of its phenomenal form.
However, the weak link in this theory is still the fact that we have not directly observed any such wave excitation in the brain. To confirm or refute hybrid cognition, therefore, the priority is clear. We need either to observe the missing element directly, or to prove that it does not exist in the brain. To do either requires detailed biophysical and neuroanatomical analysis of possible mechanisms.
This theory is not so much a radical departure from modern neuroscience, as (potentially) a radical addition to it. It owes much to current neural theories of cognition and consciousness - and hopefully can now add to them, filling in the `explanatory gap' to consciousness.
There is only space here to mention some key influences, such as the seminal ideas of Crick and Koch (eg Crick & Koch 1990; Crick 1994), the Global Workspace (GW) theory of Baars & Newman (Baars 1988; Newman & Baars 1993), the theory of Jackendoff (1987), and Gray's (1995) theory of consciousness.
In these theories, neural activity tells us important things about the nature of consciousness; but the key link to consciousness itself is still to be filled in, as emphasised by Chalmers (1995,1996), by some new law of nature. This theory proposes a link, through an ultra-pure quantum state.
Many authors since Wigner (1961) have suggested that consciousness is linked with quantum effects. The link to the Bose-Einstein condensate was proposed by Marshall (1989), and developed by others (Lockwood 1989; Penrose 1994; Hameroff 1994; Cairns-Smith 1995). This theory is distinctive in that (a) the quantum state serves a vital biological purpose, (b) the form of the quantum state directly and simply defines the form of consciousness, and (c) it does not yet depend on any disputed interpretation of quantum mechanics.
This theory goes against current thinking in localising consciousness in one region of the brain. In a purely neural theory, the wish to de-localise consciousness is understandable; whenever you look at a small enough region or group of neurons, you find nothing which seems capable of creating consciousness, or seems much different from other (unconscious) groups of neurons. However, distributing the problem across the brain has not solved it; we may still need to consider radically different, possibly localised solutions.
Similarly it goes against the mainstream in proposing one `master map' in the brain for integration of sense data. No neural candidates have been found for the master map, so a `multiple cooperating maps' picture is widely believed; but wave storage makes possible a master map in the thalamus.
A many-maps theory of cognition leads to a multiple-drafts theory of consciousness, as in the work of Dennett (1991) or Damasio (1994). As the multiple drafts are in 2-D topographic and symbolic representations, it is unclear how the resulting conscious experience has a 3-D Euclidean character.
It is as if four people, conversing over telephones in English, Finnish, Japanese, and Serbo-Croat, should somehow cause conscious experience in Sanskrit. Why Sanskrit, and not Swahili? Equally, why does conscious experience have a 3-D Euclidean geometry, not 4-D Riemannian geometry ? Experience without a closely matching physical representation seems highly implausible and ungrounded; it asks too much of the required bridging law between physical phenomena and conscious experience.
A key argument against a single-draft, `Cartesian theatre' model of consciousness is the argument of the homunculus: if the Cartesian theatre requires an observer, or homunculus, then where is consciousness in that homunculus, without an infinite regress?
In this theory, consciousness resides in a wave excitation, which is a kind of inner light in the brain. By assumption, this light contains consciousness, and needs no observer. Neurons in the thalamus create and observe the inner light. We are conscious not because our neurons, or any homunculus, observe the light - but because we are the light.
If that account fits the facts, then it is no more mystical than the inverse square law of gravitation. In accounting for consciousness, as for gravitational motion, the buck has to stop somewhere; making it stop in the wave storage seems to agree well with the facts of consciousness.
However, the key test of this theory will be to observe the wave storage mechanism directly in the brain. If it were found, I think the case for linking it to consciousness would be very strong. Even if wave storage is not found, and this theory turns out to be completely wrong, it may still stand as an example of what a simple, predictive, testable theory of consciousness would look like. That in itself is perhaps useful.
Baars, B. J. (1988) A cognitive theory of consciousness, Cambridge University Press
Block, N. (1995) On a confusion about a function of consciousness, Behavioral and Brain Sciences 18:227-47
Bogen, J. E. (1995) On the Neurophysiology of Consciousness. Consciousness and Cognition, 4:1, 52-62.
Cairns-Smith, A. G. (1996) Evolving the mind: on the nature of matter and the origin of consciousness, Cambridge University Press
Chalmers, D. J. (1995) Facing up to the problem of consciousness, Journal of Consciousness Studies 2:200-219
Chalmers, D. J. (1996) The conscious mind: in search of a fundamental theory, Oxford University Press
Crick, F. (1984) Function of the thalamic reticular complex: the searchlight hypothesis. Proceedings of the National Academy of Sciences, USA, 81, 4586-4590.
Crick F and Koch C (1990) Towards a neurobiological theory of consciousness. Seminars in Neuroscience, 2:263-275.
Crick F.H.C. (1994) The Astonishing Hypothesis: the scientific search for the soul , Simon and Schuster.
Damasio, A. R. (1989) The brain binds entities and events by multi-region activation from convergence zones, Neural Computation, 1, 132-132.
Damasio, A. R. (1994) Descartes' Error: emotion, reason and the human brain, G. P. Putnam, New York
Dennett, D. C. (1991) Consciousness Explained. Boston: Little, Brown
Fröhlich, H. (1968) Long range coherence and energy storage in biological systems, Int. J. Quantum Chem. 2, 641-649
Gray, J.A.. (1995) The contents of consciousness: A neuropsycho- logical conjecture. Behavioral and Brain Sciences, 18:4, 659-722.
Hameroff, S. R. (1994) Quantum coherence in microtubules: a neural basis for emergent consciousness ? J. Consciousness Studies 1, 91
Jackendoff, R. (1987) Consciousness and the computational mind, MIT Press
Jones, E. G. (1985) The thalamus, Plenum, New York
Lockwood, M. (1989) Mind, Brain and the Quantum Oxford:Blackwell
Marr, D. (1982) Vision, W.H.Freeman
Marshall, I. N. (1989) Consciousness and Bose-Einstein condensates, New ideas in psychology, 7,73-83
Mesulam, M. (1985) Principles of behavioral neurology, Philadelphia, Davis.
Newman, J. (1995) Thalamic contributions to attention and consciousness, Consciousness and cognition 4, 172 - 193
Newman, J. (1996), Lead review and summary for the ASSC electronic seminar on Thalamocortical foundations of conscious experience, http://www.phil.vt.edu/assc/newman/
Newman, J. and B. J. Baars (1993) A neural attentional model for access to consciousness: a global workspace perspective, Concepts in Neuroscience 4, 255 - 290
Penrose, R. (1994) Shadows of the mind, Oxford University Press
Sloman, A. (1975) Afterthoughts on analogical representations, in Proc. theoretical issues in natural language processing, Cambridge, Mass.
Steriade, M. , E. G. Jones and R. R. Llinas (1990) Thalamic oscillations and signalling, Wiley
Taylor, J.G. and Alavi, F.N. (1993). Mathematical analysis of a competitive network for attention. In J.G. Taylor (Ed.) Mathematical Approaches to Neural Networks. Elsevier Science Publishers B.V., 341-382.
Tilley, D. R. annd Tilley, J. (1990) Superconductivity and Superfluidity, 3rd edition, IOP
Wigner, E. P. (1961) Remarks on the mind-body problem, in The Scientist speculates, Princeton University Press
[1] Each piece of visual input must be placed quite precisely in represented space, requiring a precise corticothalamic steering signal; thus corticothalamic fibres outnumber thalamocortical fibres by about 10:1 (Newman 1995)
[2] Since the thalamus is a bilateral organ, so why are there not two consciousnesses? Possibly there are, but in normal subjects we would expect the two to be very well coordinated with one another - so well coordinated that the differences are negligible. The observations of split-brain patients are relevant to this issue.