Figure 8.1: Nerve signals travel from the brain down the spinal cord to reach muscle fibers, causing the muscle to contract and thereby move a limb, such as an arm.
JEAN-MARIE CHAUVET was born in the ancient French province of Auvergne, but when he was five his parents moved south-east to the Ardèche, a spectacular region of rivers, gorges and canyons cut into the underlying limestone rock. At twelve, Jean-Marie discovered his lifelong passion as he and his friends first donned Second World War helmets to explore the many caverns and caves dug out of the Ardèche valley walls along its great river. He left school at the age of fourteen, working first as a stonemason, then as a hardware store clerk and finally as a caretaker. Yet, inspired by Norbert Casteret’s book My Life Underground, Jean-Marie devoted every weekend he could to his childhood passion, climbing across sheer rock faces or digging through dark caverns, dreaming of one day being the first person to set eyes on the hidden treasure of an unexplored cave. “It’s always the unknown that leads us. When you’re walking along in a cave, you don’t know what you’re going to find. Will it end around the next corner, or will you discover something fantastic?”1
Saturday, December 18, 1994, started off a weekend like any other for the forty-two-year-old Jean-Marie and his two spelunking friends, Eliette Brunel-Deschamps and Christian Hillaire, roaming the gorges looking for something new. As the afternoon waned and the air grew colder they decided to explore an area known as the Cirque d’Estre, which captures the meager afternoon sunlight and so is usually a little warmer than sheltered parts of the valley on a cold winter’s day. The friends followed an old mule track that wound along the cliff between terraces of evergreen oak, box trees and heather, with a great view of the Pont d’Arc at the entrance of the gorge. As they struggled through the undergrowth they noticed a small cavity in the rock, measuring about 25 centimeters wide and 75 high.
This was—literally—an open invitation to the cavers, and they had soon squeezed through the gap to enter a small chamber only a few meters long and barely high enough to allow them to stand upright. Almost immediately, they noticed a faint draft coming from the back of the chamber. Anyone who has explored caves will be familiar with the sensation of a warm draft coming from an unseen tunnel. Most hidden passages are well known to experienced cavers; it’s just that they lie beyond the narrow pencil of illumination provided by your torch. But the draft in that tiny chamber was not coming from any known cave. The team took turns removing stones from the end of the chamber until they located the source of the air: a duct falling vertically downward. The smallest of the team, Eliette, was the first to be lowered by rope into the darkness to a narrow shaft that she could crawl through. It first went down, then turned back up again before opening out, at which point Eliette could see that she was hanging 10 meters above a clay floor. Her torch was too weak to illuminate the far wall, but the echo that returned her shout from the darkness told her that she was in a big cave.
The team were very excited, but had to return to their van parked at the foot of the cliff to fetch a ladder. After retracing their steps back to the cavity they unrolled the ladder and Jean-Marie was the first to reach the floor of the cave. It was indeed a big cavern, at least 50 meters high and just as wide, with stunning columns of white calcite. The three carefully made their way through the darkness, stepping in one another’s footprints to avoid disturbing the pristine environment, past great flows and curtains of mother of pearl and between the bones and teeth of long-dead bears scattered in ancient hibernation nests dug into the clay floor.
When the light of Eliette’s torch reached the wall she let out a cry. She had spotted a line of red ocher forming the outline of a small mammoth. Speechless, the friends made their way along the wall, illuminating in turn the shapes of a bear, a lion, birds of prey, another mammoth, even a rhino and stenciled human hands. “I kept thinking, ‘We’re dreaming. We’re dreaming,’ ” Chauvet remembered.2
The team’s torches were losing power, so they retraced their steps, crawled out of the cave and drove back to Eliette’s home to have dinner with her daughter, Carole. But their emotional, disjointed and largely incoherent accounts of what they had seen so intrigued Carole that she insisted they take her straight back to the cave so that she could see the marvels for herself.
It was after dark by the time they reentered the cave, this time with more powerful torches that revealed the full splendor of their discovery: several caverns decorated with a marvelous menagerie of animals: horses, ducks, an owl, lions, hyenas, panthers, stags, mammoths, ibexes and bison. Most were drawn in a wonderful naturalistic style, with charcoal shading and overlapping heads to suggest perspective, and in poses that possess a real emotional appeal. There was a row of calmly pensive horses, a cute baby mammoth with large round feet and a pair of charging rhinos. There was even a rhino whose seven legs suggested a running motion.
The Chauvet cave, as it is now called, is today recognized as one of the world’s most important sites of prehistoric art. Because it is so pristine—it even has the intact footprints of its ancient inhabitants—it remains sealed and guarded so as to preserve its delicate environment. Access is strictly controlled, with only a lucky few allowed to enter the cave: one of these was the German filmmaker Werner Herzog, whose 2011 film Cave of Forgotten Dreams is the closest most of us will get to enjoying the remarkable rock art of the ice-age hunters who sheltered in those caves thirty thousand years ago.
What we wish to explore in this chapter are not the rock images themselves, but the puzzle probably best evoked by the title of Herzog’s film. It is clear from any viewing of the paintings that they are not simply flat representations of what was seen by the eye. They are often abstracted to conjure an impression of motion, and they utilize bends and curves in the rock to endow the represented animals with an almost three-dimensional presence.*1 The artists had not simply painted objects; they painted ideas. The humans who smeared pigment over the walls of the Chauvet cave were, like us, people who thought about the world and their place in it; they were conscious.
But what is consciousness? This is, of course, a question that has vexed philosophers, artists, neurobiologists and indeed the rest of us for probably as long as we have been conscious. In this chapter we will take the coward’s way out by not attempting any rigid definition. Indeed, it is our view that the quest to understand this strangest of biological phenomena is often hindered by a pernickety insistence on defining it. Biologists cannot even agree on a unique definition of life itself; but that hasn’t stopped them from unraveling aspects of the cell, the double helix, photosynthesis, enzymes and a host of other living phenomena, including many driven by quantum mechanics, that have now revealed a great deal about what it means to be alive.
We have explored many of these revelations in earlier chapters, but all those we have so far discussed, from magnetic compasses to enzyme action, from photosynthesis to heredity to olfaction, can be discussed in terms of conventional chemistry and physics. While quantum mechanics may be unfamiliar, particularly from many biologists’ perspectives, it nevertheless fits completely within the framework of modern science. And although we may not have an intuitive or commonsense grasp of what is going on in the two-slit experiment or quantum entanglement, the mathematics that underpins quantum mechanics is precise, logical and incredibly powerful.
But consciousness is different. Nobody knows where or how it fits in with the kind of science that we have discussed so far. There are no (reputable) mathematical equations that include the term “consciousness,” and unlike, say, catalysis or energy transport, it has not, so far, been discovered in anything that isn’t alive. Is it a property of all life? Most people would think not, and would reserve consciousness for those creatures that possess nervous systems; but then how much of a nervous system is necessary? Do clownfish yearn for their home reef? Did our European robin really feel an urge to fly south for the winter, or was she on automatic pilot like a drone aircraft? Most pet owners are convinced that their dogs, cats or horses are conscious; so did consciousness emerge in mammals? Many people who keep budgerigars or canaries are equally sure that their pets also have their own personalities and are just as conscious as the cats that chase them. But if consciousness is common to both birds and mammals, then both probably inherited the property from a common conscious ancestor, perhaps something like the primitive reptile called an amniote that lived more than three hundred million years ago and appears to be the ancestor of birds, mammals and dinosaurs. So, did the Tyrannosaurus rex that we met in chapter 3 experience fear as it sank into the Triassic swamp? And are more primitive animals really unconscious? Many aquarium owners would insist that fish or molluscs such as octopi are conscious; but to find an ancestor to all these groups we have to go back to the emergence of vertebrates in the Cambrian period five hundred million years ago. Is consciousness really that ancient?
Of course, we don’t know. Even pet owners are only making guesses, since nobody really knows how to distinguish human-like behavior from true consciousness. Without knowing what consciousness is, we can never know which life forms possess the property. So our naïve approach will be to eschew these arguments and debates and remain entirely agnostic on questions of when consciousness emerged on our planet, or which of our relatives in the animal kingdom are self-aware. We take as our starting point an insistence that those of our ancestors who painted the ideas of bears, bison or wild horses on ancient cave walls were definitely conscious. So, some time between three billion or so years ago, when microbes first emerged from the primeval mud, and the tens of thousands of years ago when those early modern humans decorated caves with impressions of animals, a bizarre property appeared in the matter of which living organisms are composed: some of that matter became aware. Our aim in this chapter will be to consider how and why this happened—and to examine the controversial suggestion that quantum mechanics played a key role in the emergence of consciousness.
First, in the spirit of our previous chapters, we will ask the question whether we need to resort to quantum mechanics in order to explain this most mysterious of human phenomena. It is certainly not enough to adopt the view, as some have, that consciousness is mysterious and difficult to pin down, and quantum mechanics is mysterious and difficult to pin down, therefore surely the two must be connected in some way.
Perhaps the oddest fact we know about the universe is that we know a great deal about it, owing to an extraordinary property possessed by those parts of it that are enclosed within our own skulls: our conscious minds. This is indeed highly bizarre, not least because the function of this odd property isn’t at all clear.
Philosophers often probe this question by imagining the existence of zombies. These function just as human beings going about their activities, painting the walls of caves or reading books, but without any inner life; nothing is going on inside their heads except mechanical calculations that drive the movement of their limbs or the motor functions that power their language. Zombies are automatons, without awareness or sense of experience. That such beings are at least a theoretical possibility is evidenced by the fact that a lot of our actions—walking, riding a bicycle, the movements required to play a familiar musical instrument, etc.—may be performed unconsciously (in the sense that our conscious mind can be elsewhere when performing these tasks), without awareness or recollection of experience. Indeed, when we actually think about them, our performance in these activities is paradoxically hampered. For these actions at least, consciousness seems to be dispensable. But if there exist activities that can be executed without consciousness, then is it at least possible to imagine a creature performing all human activities on automatic pilot?
It would seem not; there are some activities for which consciousness appears to be indispensable, such as natural language. It is very hard to imagine holding a conversation on automatic pilot. It would also be difficult for us to do a tricky calculation automatically or solve a crossword. We cannot imagine our ice-age artist (we will arbitrarily assume our painter was female) being able to paint a bison with nothing but the wall of a cave in front of her, if she were not conscious. What all of these necessarily conscious activities have in common is that they are driven by ideas, such as the idea behind a word, the solution to a problem, or an understanding of what a bison is and means to stone age people. Indeed, the walls of the Chauvet cave provide lots of evidence for that most powerful application of ideas: bringing several of them together to form a novel concept. A hanging rock, for example, is painted with an impossible figure that possesses a bison’s upper body but a human lower half. Such an object could only have been created in a conscious mind.
What, then, are ideas? For our purposes, we will assume that ideas represent complex information that is joined up in our conscious mind to form concepts that have meaning to us, such as whatever it was that the half-man half-bison image on the wall of the Chauvet cave meant to the people who inhabited those caves. This compression of complex information into a singular idea was noted in a description attributed to Mozart of how an entire musical composition might be “finished in my head though it may be long. Then my mind seizes it as a glance of my eye … It does not come successively, with various parts worked out in detail, as they will be later on, but in its entirety.”3 The conscious mind is able to “seize” on complex information “with various parts” so that its meaning can be grasped “in its entirety.” Consciousness allows our mind to be driven by ideas and concepts, rather than mere stimuli.
But how does complex neuronal information get glued together in our conscious minds to form an idea? This question is an aspect of the first puzzle of consciousness—what is often termed the binding problem: how does information encoded in disparate regions of our brain come together in our conscious mind? The binding problem is usually formulated in terms of visual or other sensory information. Recall, for example, Luca Turin’s evocative description of the scent of Shiseido’s Nombre Noir perfume: “It was halfway between a rose and a violet, but without a trace of the sweetness of either, set instead against an austere, almost saintly background of cigar-box cedar notes.” Turin did not experience the perfume as a mix of distinct smells each associated with the firing of its particular olfactory receptor neuron, but as a single aroma with a range of underlying evocative notes and tones, including the meaning of a whole host of accessory concepts, such as cigars and violets. Similarly, sights and sounds are not experienced as distinct proportions of colors, textures or notes but as integrated sensory impressions, memories and concepts of, for example, a bison, a tree or a person.
Imagine our Palaeolithic artist observing a real bison. Her eyes, nose, ears and, if it was a dead bison, touch receptors in her fingers will have captured a multitude of sensory impressions of the animal, including its smell, shape, color, texture, motion and sound. In chapter 5 we discussed how scents are captured by our sense of smell. You may recall that odorant molecules that bind to each olfactory neuron cause the nerve to “fire,” which means that it sends an electrical signal along its axon (the broom-handle end of the cell) that goes from the olfactory epithelium in the back of the nose to the olfactory bulb in the brain. We will be exploring the details of this firing process later in the chapter, because it is key to understanding the potential involvement of quantum mechanics in our thoughts. For now, however, we will imagine a bovine odorant molecule wafting off our bison and into the nose of our artist, where it binds to an olfactory receptor and triggers a chain of electrical pulses to travel down the wire-like axon, a bit like a telegraph signal but comprising only dots, or blips, rather than the dots and dashes of telegrams.
Once the olfactory nerve signal arrived in our artist’s brain, it triggered the firing (more blips) of many more downstream nerves: the blip signal hopped from one nerve to another, with each nerve acting like a kind of telegraph relay station. Other sensory data were similarly captured into blip signals. For example, rods and cones (specialist neurons like olfactory neurons, but responding to light rather than odorants) lining the retina of her eye would have sent trains of blip signals via optical nerves to the visual cortex of her brain. And just as olfactory neurons responded to individual odorant molecules, optical nerves responded to only certain features of an image that fell on her retina: some will have responded to a particular color or shade of gray, others to edges or lines or particular textures. Auditory nerves in her inner ear similarly responded to sound, perhaps the heavy breathing of a speared bison; and the touch of its fur would have been captured by mechanosensitive nerves in her skin. In all these instances, each sensory neuron would have responded only to certain features of the sensory input. For example, a particular auditory neuron would have fired only if the sound entering the artist’s ears included a certain frequency. But, whatever its source, the signal generated by each nerve would have been precisely the same: a pulse of electrical blips traveling from the sensory organ to specialist regions of the artist’s brain. There, those signals may have triggered immediate motor outputs; but they may also have modified the connectivity between neurons to lay down a memory of her observations via the “neurons that fire together wire together” principle that appears to underpin how memories are encoded in the brain.
The important point is that there is nowhere in the 100 billion or so neurons of a human brain where this vast sensory stream of blips comes together to form the conscious impression of a bison. In fact, “stream” isn’t really the right word here, because it suggests some pooling of information within that stream, and that doesn’t happen in neurons. Instead, each nerve signal remains locked into an individual nerve. So, rather than a stream, you should think of the information traveling through the brain as sequences of blip blip blip blip … signals passing along individual strands of an immense tangle of billions of neurons. The binding problem is the problem of understanding how all the disparate blip-encoded information generates the unified perception of a bison.
And it isn’t just sensory impressions that need to be bound. The raw material of consciousness is not sensory data stripped of context, but meaningful concepts—in the case of a bison, hairy, smelly, scary or magnificent—each of which is loaded with lots of complex information. All this additional baggage must have been bound together with the sensory impressions to provide the impression of a hairy, smelly, scary but magnificent bison that our Palaeolithic artist could have later recalled when reproducing it in pigment smears.4
Formulating the binding problem in terms of ideas, rather than sensory impressions, brings us to the nub of the problem of consciousness, which is the puzzle of how ideas can move minds and thereby bodies. We will never know what precisely was in the mind of our stone age artist that provoked her into applying pigment to stone. Perhaps she thought that the form of a bison would cheer up a dark corner; or maybe she believed that painting the animal would improve her fellow hunters’ chances of success. But what we can be sure of is that the artist would have believed that the decision to paint the bison was her idea.
But how can an idea move matter? Understood as an entirely classical object, the brain receives information via one of the sensory inputs and then processes that information to generate outputs, just like a computer (or a zombie). But where in that tangle of blips is our conscious mind, that sense of “self” that, we are convinced, drives our voluntary actions? What exactly is this consciousness and how does it interact with the matter of our brain to move our arms, legs or tongue? Consciousness, or free will, just doesn’t figure in an entirely deterministic universe, because the laws of causality allow only for one thing after another in an endless chain of cause and effect that stretches from that Chauvet cave right back to the Big Bang.
Jean-Marie describes the moment when he and his friends first laid eyes on the paintings in the Chauvet cave: “We were weighed down by the feeling that we were not alone; the artists’ souls and spirits surrounded us. We thought we could feel their presence.”5 Clearly the cavers were experiencing a profound, what some would call a spiritual, experience. When we look inside the skull of a human or animal all we find is wet squishy tissue, not very different from the stuff of a bison steak. But when that stuff is inside our own skull, it is aware and has experiences, concepts that just don’t seem to exist in the material world. And somehow this ethereal stuff of awareness and experience—our conscious mind—drives the material stuff of our brain to cause our actions (or at least, that’s our impression). This puzzle, variously referred to as the mind–body problem or the hard problem of consciousness, is surely the deepest mystery of our entire existence.
In this chapter we will be asking whether quantum mechanics can provide any answers to this deep mystery. We should emphasize at the outset that any ideas about consciousness remain highly speculative in nature, since no one really knows what it is or how it works. There isn’t even a consensus among neuroscientists, psychologists, computer scientists and artificial intelligence researchers that there is a need for something beyond the sheer complexity of the human brain to explain consciousness.
Our starting point will be the brain processes that led to the shape of a bison being impressed on Ardèche limestone.
In this section we will follow the causal chain backward from the appearance of a line of red ocher on the wall of a cave thirty thousand years ago. This pursuit will lead us from the contracting muscles in the arm of the painter who drew that line, back to the nerve impulses that caused the muscles to contract, further back to the brain impulses that fired those nerves and the sensory inputs that set the chain of events in motion. Our aim is to try to pin down where consciousness makes its input in this causal chain so that we can then investigate whether quantum mechanics might have played a role in that event.
We can imagine the scene all those millennia ago when an unknown artist, dressed perhaps in bearskins, peered into the gloom of the Chauvet cave. The paintings were discovered deep within the cave, so she would have had to carry a torch, along with pots of pigments, into the cave. Then at some point the painter dipped a finger into the pot of colored charcoal and smeared the pigment onto the wall to create the outline of a bison.
The motion of the painter’s arm across the cave wall was initiated by a muscle protein called myosin. Myosin is an enzyme that uses chemical energy to power the contraction of muscles, essentially by causing the fibers to slide over one another. The details of this contraction mechanism have been worked out by hundreds of scientists over several decades, and it is a remarkable example of nanoscale biological engineering and dynamics. But in this chapter we will skip the fascinating molecular details of muscle contraction to focus instead on the question of how something as ephemeral as an idea could cause muscles to contract (figure 8.1).
The immediate answer is that it didn’t. The contraction of the artist’s muscle fibers was actually triggered when positively charged sodium ions rushed into her muscle cells. Muscle cells have more sodium ions on the outside of their membrane than the inside, giving rise to a voltage difference across their membrane, a bit like a tiny battery. However, there are pores in these membranes called ion channels, which, if opened, allow the sodium ions into the cell. It was this electrical discharging process that triggered the artist’s muscle contraction.
The next backward step in our chain of causation is the question: What caused those muscle ion channels to flip open at that moment? The answer is that motor nerves attached to the muscles in the artist’s arm released chemicals called neurotransmitters that popped the ion channels open. But what then caused these motor nerves to release their package of neurotransmitters? Nerve endings release neurotransmitters whenever an electric signal called an action potential arrives (figure 8.2). Action potentials are fundamental to all nerve signaling, so we need to take a closer look at how they work.
Figure 8.1: Nerve signals travel from the brain down the spinal cord to reach muscle fibers, causing the muscle to contract and thereby move a limb, such as an arm.
A nerve cell, or neuron, is an extremely long, thin, snake-like cell consisting of three parts. At its head end is a spider-like cell body, which is where the action potential is initiated. This then travels along the thin middle section, called the axon (the “broom handle” of an olfactory neuron), to the nerve ending, where the neurotransmitter molecules are released (figure 8.2). Although the nerve axon looks a little like a tiny electric cable, the way it transmits its electrical signal is far cleverer than the process by which a simple flow of negatively charged electrons passes through a copper wire.
The nerve cell, just like a muscle cell, normally has more positively charged sodium ions outside than inside. This difference is maintained by pumps that push positively charged sodium ions out of the cell through the nerve cell membrane. The excess of external positive charges provides a voltage difference across the cell membrane of about one-hundredth of a volt. Although this doesn’t sound like much, you have to remember that cell membranes are just a few nanometers thick, so it is a voltage across a very short distance. This means that we have an electrical gradient (what voltage actually is) across the cell membrane of a million volts per meter. This is equivalent to a staggering ten thousand volts across a one-centimeter gap and is almost enough to create a spark, such as is required in your car’s spark plug to ignite the fuel.
Figure 8.2: Nerves send electrical signals from the cell body along the axon to the nerve ending, where they cause the release of neurotransmitter into a synapse. The neurotransmitter is picked up by the cell body of a downstream neuron, causing it to fire and thereby transmit the nerve signal from one neuron to the next.
The head-end of the artist’s motor nerve, the body of the nerve cell, is connected to a cluster of structures called synapses (figure 8.2), which are kind of nerve-to-nerve junction boxes. Upstream nerves release neurotransmitter molecules into these junctions much as neurotransmitters are released at the nerve–muscle junction; this triggers the opening of ion channels in the membrane surrounding the nerve cell body, thereby allowing positively charged ions to rush inside, causing its voltage to drop sharply.
Most voltage drops caused by the opening of a handful of ion channels in a synapse will have little or no effect. But if lots of neurotransmitter arrives, then lots of ion channels will flip open. The ensuing rush of positive ions into the cell causes its membrane voltage to dip below a critical threshold of about −0.04 volts. When this happens, another set of nerve ion channels come into play. These are voltage-gated ion channels, which means they are sensitive not to neurotransmitters but to the voltage difference across the membrane. In the example of our artist, when the voltage in the cell body dropped below the critical threshold, a whole bunch of these channels opened to allow more ions to rush into the nerve, further short-circuiting their patch of membrane. The ensuing voltage drop caused more voltage-gated ion channels to pop open, allowing more ions to rush inside the cell, causing more of the membrane to short-circuit. The long cable of the nerve, the axon, is lined with these voltage-gated channels, so once the short-circuiting was kicked off at the cell body, it triggered a kind of domino effect of membrane short-circuiting—the action potential—that quickly traveled down the nerve until it reached the nerve ending (figure 8.3). There it stimulated the release of neurotransmitter into the neuromuscular junction, causing our artist’s arm muscle to contract to trace the line of a bison on the wall of the cave (figure 8.1).
You can see from this description how different nerve signals are from an electrical signal traveling down a wire. For a start, the current, the movement of charges, is not down the length of nerve cables in the direction of the nerve signal, but perpendicular to the direction of the action potential: from outside in, through those ion channels in the cell membrane. Also, immediately after the action potential is initiated by the opening of the first ion channels, they are slammed shut again and the ion pumps get to work on reestablishing the original battery voltage across the membrane. So another way of viewing the nerve signal is as a wave of opening and closing of membrane ion doors that travels from the cell body to the nerve ending: a moving electrical blip.
Figure 8.3: Action potentials travel along nerve axons via the action of voltage-gated ion channels in the nerve cell membranes. In its resting state the membrane has more positive ions on the outside than on the inside. However, a change in voltage caused by an upstream action potential will trigger opening of the ion channels and a surge of positively charged sodium ions—an action potential—will rush into the cell, temporarily reversing the membrane voltage. This electrical blip will trigger the opening of downstream ion channels in a kind of domino effect electrical impulse that travels down the nerve until it reaches the nerve ending, where it triggers neurotransmitter release. After the action potential has passed, ion pumps return the membrane to its normal resting state.
The nerve–nerve junctions for most motor nerve cells are located in the spinal cord, where they receive neurotransmitter signals from hundreds or even thousands of upstream nerves (figure 8.1). Some upstream nerves release neurotransmitters into the junction box (synapse) that open ion channels in the cell body to increase the likelihood of firing up the motor nerve, whereas others tend to close them. In this way the cell body of each nerve cell seems to be acting like the logic gate of a computer, generating an output—whether or not it fires—based on its inputs. So, if the neuron is like a logic gate, then the brain, made up of billions of neurons, might be thought of as some kind of computer; or at least, this is the assumption of most cognitive neuroscientists who subscribe to what is called the computational theory of mind.
But we are jumping too far ahead—we haven’t yet reached the brain. Our artist’s motor nerve must have received lots of neurotransmitters in its nerve–nerve junction boxes, causing it to fire. Those inputs came from upstream nerves that mostly originated in her brain. Following the chain of causation back, the heads of those nerves would have made their decisions about whether or not they fired on the basis of their many inputs, and the inputs of those inputs, and so on further and further backward through the causal chain until we reach the nerves that received input signals from the artist’s eyes, ears, nose and touch receptors, and memory centers that would have received sensory inputs from her earlier observations of live and dead bison. Between sensory inputs and motor output is the brain’s neural network that performed the computations dictating the decision to generate, or not to generate, the precise motor output needed to draw the outline of a bison.
So there we have it: the entire chain of events leading up to that muscle contraction that swept the artist’s arm across the wall. But have we missed something? What we have described so far is an entirely mechanistic causal chain from sensory input to motor output, with some of the information channeled through memory centers. This is the kind of mechanism that Descartes was talking about when he made the claim (discussed in chapter 2) that animals are mere machines; all we’ve done is replace his pulleys and levers with nerves, muscles and logic gates.
But remember that Descartes reserved a role for a spiritual entity, the soul, as the ultimate driver of human actions. Where is the soul in this input–output chain of events? So far, we have described only a zombie artist. Where did her consciousness, her idea that she should represent a meaningful bison on the wall of the cave, enter the chain of events between input and output? This remains the biggest puzzle of brain science.
In one way or another, most people probably subscribe to the notion of dualism—the belief that the mind/soul/consciousness is something other than the physical body. But dualism fell out of favor in scientific circles in the twentieth century, and most neurobiologists now prefer the idea of monism—the belief that mind and body are one and the same thing. For example, the neuroscientist Marcel Kinsbourne claims that “being conscious is what it is like to have neural circuitry in particular interactive functional states.”6 But the logic gates of a computer are, as we have already noted, rather similar to neurons, so it isn’t clear why highly connected computers, such as the World Wide Web with its one billion or so Internet hosts (though still small compared to the brain’s one hundred billion neurons), show no sign of awareness. Why are silicon-based computers zombies whereas flesh-based computers are conscious? Is it simply a matter of complexity and the sheer “interconnectedness” of our brain cells, not yet matched by the World Wide Web,*2 or is consciousness a very different kind of computing?
There are of course many explanations of consciousness, all of which have been laid out in a whole host of books on the topic. But, for the purposes of this account, we will focus on the highly controversial, yet fascinating, claim that is most relevant to our theme: namely, that consciousness is a quantum mechanical phenomenon. The case was most famously made by the Oxford mathematician Roger Penrose who, in his 1989 book The Emperor’s New Mind, claimed that the human mind is a quantum computer.
You may remember the idea of quantum computers from chapter 4, where we recalled that New York Times article of 2007 claiming that plants were quantum computers. The MIT team eventually came around to the idea that microbes and plant photosynthesis systems may indeed be performing tricks somewhat analogous to those required in quantum computation. But could their own very clever brains have also been operating in the quantum realm? To examine this question we first need to take a closer look at what quantum computers are, and how they work.
When we think of a computer today we mean any electronic device capable of carrying out instructions to manipulate and process information via a collection of electrical switches that can be either ON or OFF—each capable of encoding a binary digit (or bit) as a 1 or a 0. A collection of such switches can be arranged to build circuits that perform logic instructions, which can be combined and used to carry out arithmetical operations such as addition and subtraction or indeed the opening and closing of gates that we described for neurons. The great advantage of this electrical digital computer is that it is very much faster than any manual way of performing the same kind of task, whether by counting on fingers, mental arithmetic or using a pen and paper.
But while electronic computers may be extraordinarily fast at doing sums, even they cannot keep track of the complexity of the quantum world with its multitude of overlapping probabilities. To overcome this problem, the Nobel Prize–winning physicist Richard Feynman came up with a possible solution. He suggested performing calculations in the quantum world, with a quantum computer.
To see how quantum computers might work, it will be useful first to represent the “bit” of a classical computer as a kind of spherical compass whose needle may point at either 1 (north pole) or 0 (south pole) and is capable of turning through 180° to switch between these two states (figure 8.4a). The central processing unit (CPU) of a computer consists of many millions of these one-bit switches, so the entire computational process can be envisaged as the application of a complex set of switching rules (algorithms) that can flip lots and lots of spheres by 180°.
The quantum computing equivalent of the bit is called a qubit. This is similar to the classical sphere,*3 but its movement is not limited to a 180° flip. Instead, it can rotate through any arbitrary angle in space and, being quantum mechanical, it can also point in many directions simultaneously in a quantum coherent superposition (figure 8.4b). This increased flexibility allows a qubit to encode more information than a classical bit. But the real boost to computing power comes when you put qubits together.
Figure 8.4: (a) A classical bit being switched from 1 to 0 is represented as a rotation of a classical sphere through 180°. (b) A qubit being switched may be represented as a rotation of a sphere through any arbitrary angle. However, a coherent qubit may also be in a superposition of many rotations. (c) Three coherent qubits showing their entanglement interactions as imaginary strings connecting the surface of each sphere. It is the tension on these strings following rotations that instantiates quantum calculations.
Whereas the state of one classical bit has no influence on its neighbors, qubits may also be quantum entangled. You may remember from chapter 6 that entanglement is a quantum step up from coherence whereby quantum particles lose their individuality, so that what happens to one affects them all, instantaneously. From the perspective of quantum computing, entanglement can be visualized as each qubit sphere being connected by elastic strings*4 to every other qubit (figure 8.4c). Now, let us imagine that we rotate just one of the spheres. Without entanglement, the rotation will not affect neighboring qubits. But if our qubit is entangled with other qubits, then the rotation changes the tensions in all the connecting strings between these connected qubits. The computational resource of all those entanglement strings increases exponentially with the number of qubits, which means that it increases very rapidly indeed.
To get a feeling for exponential growth, you may have heard the fable about the Chinese emperor who was so pleased by the invention of chess that he promised to reward its inventor with a prize of his own choosing. The canny inventor asked for just one grain of rice for the first square on the chessboard, two grains of rice for the second, four for the third and so on, doubling the number of grains with each successive square until he reached the sixty-fourth square. The emperor, considering this to be a modest request, eagerly agreed and ordered his servants to bring out the rice. But, when the rice grains were counted out, he soon discovered his error. The first row of squares amassed only 255 grains (28 − 1) and even by the end of the second row of squares he had to find only 32,768 grains, just less than a kilogram of rice. But as the kilograms begin to multiply on subsequent squares, the emperor was dismayed to discover that by the end of the third row he had to hand over half a ton of rice. Reaching even the end of the fifth row would have bankrupted the kingdom! In fact, to reach the end of the chessboard would have required 9,223,372,036,854,775,808 (264 − 1) grains of rice, or 230,584,300,921 tons, which is roughly equivalent to the entire world’s rice harvest throughout the history of humankind.
The problem for the emperor was his failure to realize that doubling a number again and again leads to exponential growth—which is another way of saying that the increase from one number to the next is proportional to the size of the previous number. Exponential growth is explosive growth, as the emperor discovered to his cost. And just as the rice grains in the fable increased exponentially with the number of chessboard squares, so the power of a quantum computer scales exponentially with its number of qubits.
This is very different from a classical computer, whose power increases only linearly with the number of bits. For example, adding one more bit to an 8-bit classical computer will increase its power by a factor of one-eighth; to double its power, the number of bits will have to be doubled. But simply adding one qubit to a quantum computer will double its power, leading to the same kind of exponential increase in power that the emperor saw running away with his rice grains. In fact, if a quantum computer could maintain coherence and entanglement within just 300 qubits, which could potentially involve just 300 atoms, it could outperform, on certain tasks, a classical computer the size of the entire universe!
But, and this is a very big but, for the quantum computer to work, the qubits must interact only with one another to perform calculations (via their invisible entangled “strings”). This means they must be completely isolated from their environment. The problem is that any interaction with the outside world causes the qubits to become entangled with their environment, which we can envisage as the formation of many more strings, all pulling on the qubits from different directions, competing with the strings between the qubits and therefore interfering with the calculation they are performing. This, essentially, is the process of decoherence (figure 8.5). With even the faintest interaction, the environment throws such a confusion of entanglement strings over the qubits that they cease to behave in a coherent fashion with one another: their quantum strings are effectively severed and the qubits will behave as independent classical bits.
Quantum physicists do their best to maintain coherence in the entangled qubits by working with very rarefied and carefully controlled physical systems, encoding qubits in a handful of atoms, cooling the system to within a fraction of absolute zero and surrounding their apparatus with extensive lagging to shut out any environmental influence. Using these approaches they have delivered some landmark achievements. In 2001, scientists from IBM and Stanford University managed to build a seven-qubit “test tube quantum computer” that could implement a clever code called Shor’s Algorithm, named after the mathematician Peter Shor, who devised it in 1994 specifically to be run on a quantum computer. Shor’s Algorithm encodes a very efficient way of factorizing numbers (working out what prime numbers need to be multiplied to give the required number). This was a huge breakthrough and made scientific headlines around the world; yet the maiden flight of this fledgling quantum computer managed only to compute the prime factors of the number 15 (3 and 5, in case you were wondering).
Over the past decade, some of the top physicists, mathematicians and engineers have worked hard to build bigger and better quantum computers, but progress has been modest. In 2011, Chinese researchers managed to factorize the number 143 (13 × 11), using just four qubits. Like the US group before them, the Chinese team used a system in which qubits were encoded in the spin states of atoms. A quite different approach has been pioneered by the Canadian company D-Wave, which encodes qubits in the motion of electrons in electrical circuits. In 2007, the company claimed to have developed the first commercial 16-qubit quantum computer, able to solve a Sudoku puzzle and other pattern-matching and optimization problems. In 2013, a collaboration of NASA, Google and the Universities Space Research Association (USRA) purchased (for an undisclosed sum) a 512-qubit machine built by D-Wave that NASA plans to use to search for exoplanets, that is, planets orbiting not our sun but distant stars. However, the problems so far tackled by the company have all been within the reach of conventional computer power, and many quantum computing experts remain unconvinced that D-Wave’s technology is really quantum computing—or if it is, whether its design would ever make it any faster than a classical computer.
Figure 8.5: Decoherence in a quantum computer can be thought of as caused by entanglement of qubits with a tangle of environmental strings. These tug and pull at the qubits this way and that so that they no longer respond to their own entanglement connections.
Whatever approach the experimenters choose to take, the challenges facing them in turning the current generation of fledgling quantum computers into something useful remain immense. The biggest problem is scaling up. Every qubit added doubles the computation power, but it also doubles the difficulty of maintaining quantum coherence and entanglement. Atoms have to be colder, shielding has to be more effective, and it becomes more and more difficult to maintain coherence for more than a few trillionths of a second. Decoherence sets in well before the computer manages to complete even the simplest calculation. (Although at the time of writing, the record for room temperature quantum coherence of nuclear spin states is an impressive thirty-nine minutes.7) But, as we have discovered, living cells do manage to keep decoherence at bay for long enough to transport excitons in photosynthetic complexes, or electrons and protons in enzymes. Could decoherence similarly be kept at bay in the central nervous system to allow quantum computation to be performed in the brain?
Penrose’s initial argument that the brain is a quantum computer came from a rather surprising direction: the famous (at least in mathematical circles) set of incompleteness theorems put forward by the Austrian mathematician Kurt Gödel. These theorems were very shocking to mathematicians in the 1930s who had confidently embarked on a program to identify a powerful set of mathematical axioms that could prove true statements were true and false statements were false—basically, that the whole of arithmetic was internally consistent and free of any self-contradictions. It sounds like the sort of thing that only mathematicians or philosophers would worry about, but it was and continues to be a big deal in the field of logic. Gödel’s incompleteness theorems showed that such an endeavor was doomed to failure.
The first of his theorems demonstrated that logical systems, such as natural language or mathematics, can make some true statements that they can’t prove. This may seem an innocuous assertion, but its implications are very far-reaching. Consider a familiar logical system, such as language, which is capable of reasoning through statements such as “All men are mortal. Socrates is a man” to conclude that “Socrates is mortal.” It’s easy to see, and easy to formally prove, that the last statement follows logically from the first two, given a simple set of algebraic rules (if A = B and B = C then A = C). But Gödel showed that any logical system complex enough to prove mathematical theorems has a fundamental limitation: application of their rules can generate statements that are true, but these statements cannot be proved with the same tools that were used to generate them in the first place.
This seems rather odd, and indeed it is. However, and this is important, Gödel’s theorem does not mean that some true statements are simply not provable. Instead, one set of rules may be able to prove the truth of statements generated by, and therefore unprovable with, any other set of rules. For example, true but unprovable language statements may be provable within the rules of algebra, and vice versa.
This is, of course, a huge oversimplification that does not do justice to the subtleties of the subject. The interested reader might like to try the 1979 book on this and related subjects by the American professor of cognitive science Douglas Hofstadter.8 The key point here is that in his book The Emperor’s New Mind, Penrose takes Gödel’s incompleteness theorems as the starting point for his argument, by first pointing out that classical computers use formal logical systems (computer algorithms) to make their statements. It follows from Gödel’s theorem that they must also be capable of generating true statements they can’t prove. But, Penrose argues, humans (or at least those members of the species who are mathematicians) can prove the truth of these unprovable but true computer statements. Therefore, he argues, the human mind is more than just a classical computer, since it is capable of what he calls noncomputable processes. He then postulates that this noncomputability requires something extra, something that can only be provided by quantum mechanics. Consciousness, he argues, requires a quantum computer.
This is, of course, a very bold claim to make on the grounds of the provability or not of a difficult mathematical statement, a point to which we will return. But in his later book The Shadows of the Mind, Penrose went even further to propose a physical mechanism by which the brain might calculate its sums in the quantum world.9 He teamed up with Stuart Hameroff,*5 Professor of Anesthesiology and Psychology at the University of Arizona, to claim that structures called microtubules that are found in neurons are the qubits of quantum brains.10
Microtubules are long strings of a protein called tubulin. Hameroff and Penrose proposed that these tubulin proteins—the beads on the string—are capable of flipping between at least two different shapes, extended and contracted, and, crucially, are able to behave as quantum objects that exist in a superposition of both shapes at once to form something akin to qubits. Not only that, they postulated that tubulin proteins in one neuron are entangled with tubulin proteins in lots of other neurons. You will remember that entanglement is that “spooky action at a distance” that potentially connects objects that are very far away from one another. If spooky connections between all the hundred billion neurons in a human brain were possible, then they could, potentially, bind together all the information encoded in separated nerves and thereby solve the binding problem. They could also provide the conscious mind with the elusive but extraordinary powerful capabilities of a quantum computer.
There is much more to the Penrose–Hameroff consciousness theory, including, possibly even more controversially, a proposed involvement of gravity.*6 But is it credible? We, together with nearly all neurobiologists and quantum physicists, are far from convinced. One of the most obvious objections may be clear from the preceding description of how information travels from the brain through to the nerves. You may have noticed that we did not mention microtubules in that description. That is because it was unnecessary to do so, since they do not, as far as is known, have any direct role in neural information processing. Microtubules support the architecture of each neuron and transport neurotransmitters up and down its length; but they are not thought to be involved in the network-based information processing responsible for brain computations. So microtubules are unlikely substrates for our thoughts.
But perhaps an even more important objection is that brain microtubules are highly unlikely candidates as coherent quantum qubits simply because they are too big and complicated. In previous chapters we made a case for quantum coherence, entanglement and tunneling in a whole range of biological systems from photosynthetic systems to enzymes, smell receptors, DNA and the elusive organ of magnetoreception in birds. But a key feature of all of these is that the “quantum” part of the system (the exciton, electron, proton or free radical) is simple. It consists of either a single particle or small numbers of particles that do what they do over atomic-scale distances. This corresponds of course to Schrödinger’s seventy-year-old insight that the kinds of living system that are likely to support quantum rules will involve small numbers of particles.
But the Penrose–Hameroff theory proposes that entire protein molecules composed of millions of particles are in quantum superposition and entangled not only with molecules within the same microtubule but with microtubules, similarly composed of millions of particles, in billions of nerve cells across the entire volume of the brain. This is very far from being plausible. Although no one has managed to measure coherence in brain microtubules, calculations suggest that quantum coherence in even single microtubules could not be maintained for timescales longer than a few picoseconds,11 far too fleeting a time to have any impact on brain computation.*7
However, perhaps an even more fundamental problem with the Penrose–Hameroff quantum consciousness theory is Penrose’s original case for the brain being a quantum computer. You will remember that Penrose based this claim on his assertion that humans can prove Gödelian statements whereas computers can’t. But this implicates quantum computation in the brain only if quantum computers can prove Gödelian statements better than a classical computer; not only is there absolutely no evidence for this assertion, but most researchers believe the contrary.12
A further point is that it is not at all clear that a human brain can actually perform any better than a classical computer in proving Gödelian statements. Although humans may be able to prove the truth of an unprovable Gödelian statement generated by a computer, it is equally possible that computers may be able to prove the truth of an unprovable Gödelian statement generated by a human mind. Gödel’s theorem only limits the ability of one system of logic to prove all its own statements; it does not place limits on the ability of one system of logic to prove Gödelian statements generated by another.
But does that mean that there is no role for quantum mechanics in the brain? Is it likely that, with so much quantum action going on elsewhere in our bodies, our thoughts are driven entirely by the steam-engine processes of the classical world? Perhaps not. Recent research suggests that quantum mechanics may indeed play a crucial role in how the mind works.
A possible site for quantum mechanical phenomena in the brain lies within ion channels in neuronal cell membranes. As we have already described, these are responsible for mediating the action potentials—the nerve signals—that transmit information in the brain, so they play a central role in neural information processing. The channels are only about one-billionth of a meter long (1.2 nanometers) and less than half that wide, so the ions have to pass through them in single file. Yet they do so at an extraordinarily high rate of about a hundred million per second. And the channels are also highly selective. For example, the channel responsible for allowing potassium ions into the cell allows about one sodium ion through for every ten thousand potassium ions, despite the fact that the sodium ion is a little smaller than potassium—so you might naively expect it to easily slip though anything big enough to accommodate a potassium ion.
These very high transport rates, coupled with the extraordinary degree of selectivity exercised, underpin the speed of action potentials and, thereby, their ability to transmit our thoughts around our brain. But how ions are transported so rapidly and selectively has remained something of a mystery. Could quantum mechanics help? We have already discovered (in chapter 4) that quantum mechanics can enhance energy transport in photosynthesis. Can it also enhance ion transport in the brain? In 2012 the neuroscientist Gustav Bernroider, from the University of Salzburg, teamed up with Johann Summhammer from the Atom Institute at the Vienna University of Technology to perform a quantum mechanical simulation of an ion passing through a voltage-gated ion channel and discovered that the ion is delocalized (spread out) when it travels through the channel: more of a coherent wave than a particle. Also, this ion wave oscillates at very high frequencies and transfers energy to the surrounding protein by a kind of resonance process so that the channel effectively acts as an ion refrigerator that reduces the kinetic energy of the ion by about half. This effective cooling of the ion helps to maintain its delocalized quantum state by keeping decoherence at bay and thereby promotes rapid quantum transport through the channel. It also contributes to selectivity, since the degree of refrigeration will be very different if potassium is replaced with sodium: constructive interference can promote potassium ion transport while destructive interference can inhibit sodium ion transport. The team concluded that quantum coherence plays an “indispensable” role in the conduction of ions through nerve ion channels, and is thereby an essential part of our thinking process.13
We should emphasize that these researchers have not suggested that quantum coherent ions are capable of acting as any kind of neural qubits, nor have they suggested that they could play a role in consciousness; and, at first sight, it is hard to see how they could contribute to solving some of the problems of consciousness, such as the binding problem. However, unlike the microtubules in the Penrose–Hameroff hypothesis, the ion channels do at least play a clear role in neural computation—they underpin action potentials—so their state will reflect the state of the nerve cell: if the nerve is firing, then ions will be flowing (remember, they are moving as quantum waves) rapidly through the channels, whereas if the nerve is resting, any ions in the channels will be stationary. So, since the total sum of firing and nonfiring neurons in our brain must somehow encode our thoughts, then those thoughts are also reflected—encoded—in the sum of all that quantum flow of ions into and out of nerve cells.
But how might the individual thought processes be combined to generate conscious, bound-up thoughts? One coherent ion channel—whether quantum or classical—can’t possibly encode all the information bound into the thought processes that culminate in visualizing a complex object, such as a bison. To play a role in consciousness, ion channels would have to be linked in some way. Could quantum mechanics help? Is it possible, for example, that the ions in a channel are not only coherent along the length of the channel but also coherent or even entangled with ions in adjacent channels or even nearby nerve cells? Almost certainly not. Ion channels and the ions within them would suffer the same problem as the Penrose–Hameroff microtubule idea. Although it is just about conceivable that a single ion channel could be entangled with an adjacent channel within the same nerve cell, entanglement between ion channels in different nerves, which would be needed to solve the binding problem, is totally unfeasible in the warm, wet, highly dynamic and decoherence-inducing environment of a living brain.
So, if entanglement can’t bind the quantum-level information in ion channels, is there anything else that could do the job? There may be. Voltage-gated ion channels are of course sensitive to voltage: it’s what opens and closes the channels. Voltage is just a measure of the gradient of an electric field. But the entire volume of the brain is filled with its own electromagnetic (EM) field, which is generated by the electrical activity of all its nerves. This field is what is routinely detected by brain-scanning technologies such as electroencephalography (EEG) or magnetoencephalography (MEG), and even a glance at one of those scans will tell you just what an extraordinarily complex and information-rich field it is. Most neuroscientists have ignored the potential role that the EM field might play in brain computation because they have assumed it is like the steam whistle of a train: a product of brain activity, but with no impact on that activity. However, several scientists, including Johnjoe, have recently seized upon the idea that shifting consciousness from the discrete particles of matter in the brain to the joined-up EM field could potentially solve the binding problem and provide a seat for consciousness.14
To understand how this might work, we probably need to say a bit more about what we mean by a field. The term derives from its common usage: it means something that is extended through space, like a cornfield or a football field. In physics, the term “field” has the same essential meaning, but usually refers to energy fields that are able to move objects. Gravitational fields move anything that has mass, and electric or magnetic fields move electrically charged or magnetic particles such as the ions in nerve ion channels. In the nineteenth century James Clerk Maxwell discovered that electricity and magnetism are two aspects of the same phenomenon, electromagnetism, so we refer to both as EM fields. Einstein’s equation, E = mc2, with energy on one side and mass on the other, famously demonstrated that energy and matter are interchangeable. So the brain’s EM energy field—the left-hand side of Einstein’s equation—is just as real as the matter that makes up its neurons; and, because it is generated by neuron firing, it encodes exactly the same information as the neural firing patterns of the brain. However, whereas neuronal information remains trapped in those blipping neurons, the electrical activity generated by all the blipping unifies all the information within the brain’s EM field. This could potentially solve the binding problem.15 And, by opening and closing the voltage-gated ion channels, the EM field couples to those quantum coherent ions traveling through the channels.
When EM field theories of consciousness were first proposed at the very beginning of the present century, there was no direct evidence that the brain’s EM field could influence nerve firing patterns to drive our thoughts and actions. However, experiments carried out in several laboratories have recently demonstrated that external EM fields, of similar strength and structure to those that the brain itself generates, do indeed influence nerve firing.16 In fact, what the field seems to do is to coordinate nerve firing: that is, bring lots of neurons into synchrony so that they all fire together. The findings suggest that the brain’s own EM field, generated by nerve firing, also influences nerve firing, providing a kind of self-referencing loop that many theorists argue is an essential component of consciousness.17
Synchronization of nerve firing by the brain’s EM field is also very significant in the context of the puzzle of consciousness because it is one of the very few features of nerve activity that is known to correlate with consciousness. For example, we have all experienced the phenomenon of looking for an object that is in plain sight, such as our glasses, and then spotting it among a jumble of other objects. While we were looking at that jumble, the visual information encoding that object was traveling through our brain, via our eyes, but somehow we didn’t see the object we were searching for: we were not conscious of it. But then we do see it. What changes in our brain between the times when we are first unconscious and then conscious of an object within the same visual field? Remarkably, neural firing itself doesn’t seem to change: the same neurons fire whether or not we see the glasses. But when we don’t spot our glasses, the neurons fire asynchronously and when we do they fire synchronously.18 The EM field, pulling together all those coherent ion channels in disparate parts of the brain to generate synchronous firing, could play a role in this transition between unconscious and conscious thoughts.
We should stress that invoking ideas such as brain EM fields, or indeed quantum coherent ion channels, in order to explain consciousness does not in any way provide support for so-called “paranormal phenomena” such as telepathy, since both concepts are only capable of influencing neural processes going on inside a single brain—they do not allow communication between different brains! And, as we have pointed out when considering Penrose’s Gödelian argument, there is in fact no evidence that quantum mechanics is actually needed at all to account for consciousness—unlike other biological phenomena that we have considered in this book such as enzyme action or photosynthesis. But is it likely that the strange features of quantum mechanics we have discovered to be involved in so many crucial phenomena of life are excluded from its most mysterious product, consciousness? We will leave the reader to decide. The scheme outlined above, involving quantum coherent ion channels and EM fields, is certainly speculative, but it does at least provide a plausible link between the quantum and classical realms in the brain.
So with this in mind, let us return once again to that dark cave in the South of France to complete the chain of events from brain to hand as our artist stands poised before the wall watching the torchlight flicker over its gray contours. Some play of the light and rock brings the image of a bison to her conscious mind. This is sufficient to create an idea in her head, perhaps instantiated as a fluctuation of her brain’s EM field, that flips open clusters of coherent ion channels in lots of separated neurons, causing them to fire synchronously. The synchronous nerve signals fire action potentials throughout her brain and, via synaptic connections, initiate a train of signals that travels down her spine and, via nerve–nerve junctions, to the motor nerves that discharge their packets of neurotransmitters into the neuromuscular junctions that are attached to the muscles of her arm. Those muscles contract to generate the coordinated motion of her hand that sweeps across the cave wall, depositing a line of charcoal on the rock in the shape of a bison. And, perhaps more important, she perceives that she initiated the action because of an idea in her conscious mind. She is not a zombie.
Thirty thousand years later, Jean-Marie Chauvet shines a torch on that same cave wall and the idea that came to life within the brain of that long-dead artist is once again flickering through the neurons of a conscious human mind.
*1 Shocking to many film buffs, Herzog’s film is in 3-D.
*2 The size of the Internet is not easy to estimate, but each web page currently links to, on average, fewer than a hundred other pages, whereas neurons have synaptic links to thousands of other neurons. So, in terms of links, there are about a trillion between web pages and about a hundred times that number between neurons in the human brain. But the web doubles in size every few years, so it is anticipated that it will rival the complexity of the human brain within a decade. Will the Internet then become conscious?
*3 For the physicist reader, what we are describing here is a Bloch sphere.
*4 In reality the strings represent the mathematical relationship between the phase and amplitude of the entangled qubits instantiated in the Schrödinger equation.
*5 Johnjoe would like to take the opportunity to apologize to Stuart Hameroff for spelling his name wrongly in his book Quantum Evolution.
*6 This is another difficult concept, but Penrose proposed an entirely idiosyncratic interpretation of the measurement problem in quantum mechanics by postulating that for sufficiently complex (and therefore more massive) quantum systems, their gravitational effect on space-time creates a disturbance that collapses the wave function, transforming quantum into classical systems, and that this process generates our thoughts. Details of this extraordinary theory are well described in Penrose’s books, but it is fair to say that his proposal has, to date, few adherents in the quantum physics community.
*7 A picosecond is one millionth of one millionth (or 10−12) of a second.
