Consciousness and the Physical World: Proceedings of the Conference on Consciousness Held at the University of Cambridge, 9Th-10th January, 1978
This is an utterly fascinating book, one that often got me so excited that I could hardly sleep or walk without having loud, vocal arguments with myself. It takes a novel view of consciousness that places minds (and not just brains) at the center of evolution and the universe. It is based on a symposium on consciousness at Cambridge University held in 1979 and is edited by Brian Josephson and V. S. Ramachandran, both incredibly creative scientists. Most essays in the volume are immensely thought-provoking, but I will highlight a few here.
The preface by Freeman Dyson states that "this book stands in opposition to the scientific orthodoxy of our day." Why? Because it postulates that minds and consciousness have as important of a role to play in the evolution of the universe as matter, energy and inanimate forces. As Dyson says, most natural scientists frown upon any inclusion of the mind as an equal player in the arena of biology; for them this amounts to a taboo against the mixing of values and facts. And yet even Francis Crick, as hard a scientist as any other, once called the emergence of culture and the mind from the brain the "astonishing hypothesis." This book defies conventional wisdom and mixes values and facts with aplomb. It should be required reading for any scientist who dares to dream and wants to boldly think outside the box.
Much of the book is in some sense an extension - albeit a novel one - of ideas laid out in an equally fascinating book by Karl Popper and John Eccles titled "The Self and Its Brain: An Argument for Interactionism". Popper and Eccles propose that consciousness arises when brains interact with each other. Without interaction brains stay brains. When brains interact they create both mind and culture.
Popper and Eccles say that there are three "worlds" encompassing the human experience:
World 1 consists of brains, matter and the material universe.
World 2 consists of individual human minds.
World 3 consists of the elements of culture, including language, social culture and science.
Popper's novel hypothesis is that while World 3 clearly derives from World 2, at some point it took on a life of its own as an emergent entity that was independent of individuals minds and brains. In a trivial sense we know this is true since culture and ideas propagate long after their originators are dead. What is more interesting is the hypothesis that World 2 and World 3 somehow feed on each other, so that minds, fueled by cultural determinants and novelty, also start acquiring lives of their own, lives that are no longer dependent on the substrate of World 1 brains. In some sense this is the classic definition of emergent complexity, a phrase that was not quite in vogue in 1978. Not just that but Eccles proposes that minds can in turn act on brains just like culture can act on minds. This is of course an astounding hypothesis since it suggests that minds are separate from brains and that they can influence culture in a self-reinforcing loop that is derived from the brain and yet independent of it.
The rest of the chapters go on to suggest similarly incredible and fascinating ideas. Perhaps the most interesting are chapters 4 and 5 by Nicholas Humphrey (a grand nephew of John Maynard Keynes) and Horace Barlow, both of them well known neuroscientists. Barlow and Humphrey's central thesis is that consciousness arose as an evolutionary novelty in animals for promoting interactions - cooperation, competition, gregariousness and other forms of social communication. In this view, consciousness was an accidental byproduct of primitive neural processes that was then selected by natural selection to thrive because of its key role in facilitating interactions. This raises more interesting questions: Would non-social animals then lack consciousness? The other big question in my mind was, how can we even define "non-social" animals: after all, even bacteria, not to mention more advanced yet primitive creatures (by human standards) like slime molds and ants evidence superior modes of social communication. In what sense would these creatures be conscious, then? Because the volume was written in 1978, it does not discuss Giulio Tononi's "integrated information theory" and Christof Koch's ideas about consciousness existing on a continuum, but the above mentioned ideas certainly contain trappings of these concepts.
There is finally an utterly fascinating discussion of an evolutionary approach to free will,. It states in a nutshell that free will is a biologically useful delusion. This is not the same as saying that free will is an *illusion*. In this definition, free will arose as a kind of evolutionary trick to ensure survival. Without free will, humans would have no sense of controlling their own fates and environments, and this feeling of lack of control would not only detrimentally impact their day to day existence and basic subsistence but impact all the long-term planning, qualities and values that are the hallmark of Homo sapiens. A great analogy that the volume provides is with the basic instinct of hunger. In an environment where food was infinitely abundant, a creature would be free from the burden of choice. So why was hunger "invented"? In Ramachandran's view, hunger was invented to explore the environment around us; similarly, the sensation of free will was "invented" to allow us to plan for the future, make smart choices and even pursue terribly important and useful but abstract ideas like "freedom" and "truth". It allows us to make what Jacob Bronowski called "unbounded plans". In an evolutionary framework, "those who believed in their ability to will survived and those who did not died out."
Is there any support for this hypothesis? As Ramachandran points, there is at least one simple but very striking natural experiment that lends credence to the view of free will being an evolutionarily useful biological delusion. People who are depressed are well known to lack a feeling of control over their environment. In extreme cases this feeling can lead to significantly reduced mortality and death from suicide. Clearly there is at least one group of people in which the lack of a freedom to will can have disastrous consequences if not corrected.
I can go on about the other fascinating arguments and essays of these proceedings. But even reading the amazing introduction by Ramachandran and a few of the essays should give the reader a taste of the sheer chutzpah and creativity demonstrated by these scientific heretics in going beyond the boundary of the known. May this tribe of scientific heretics thrive and grow.
Progress in science often happens when two or more fields productively meet. Astrophysics got a huge boost when the tools of radio and radar met the age-old science of astronomy. From this fruitful marriage came things like the discovery of the radiation from the big bang. Another example was the union of biology with chemistry and quantum mechanics that gave rise to molecular biology. There is little doubt that some of the most important future discoveries in science in the future will similarly arise from the accidental fusion of multiple disciplines.
One such fusion sits on the horizon, largely underappreciated and unseen by the public. It is the fusion between physics, computer science and biology. More specifically, this fusion will likely see its greatest manifestation in the interplay between information theory, thermodynamics and neuroscience. My prediction is that this fusion will be every bit as important as any potential fusion of general relativity with quantum theory, and at least as important as the development of molecular biology in the mid 20th century. I also believe that this development will likely happen during my own lifetime.
The roots of this predicted marriage go back to 1867. In that year the great Scottish physicist James Clerk Maxwell proposed a thought experiment that was later called ‘Maxwell’s Demon’. Maxwell’s Demon was purportedly a way to defy the second law of thermodynamics that had been proposed a few years earlier. The second law of thermodynamics is one of the fundamental laws governing everything in the universe, from the birth of stars to the birth of babies. It basically states that left to itself, an isolated system will tend to go from a state of order to one of disorder. A good example is how a bottle of perfume wafts throughout a room with time. This order and disorder was quantified by a quantity called entropy.
In technical terms, the order and disorder refers to the number of states a system can exist in; order means fewer states and disorder means more. The second law states that isolated systems will always go from fewer states and lower entropy (order) to more states and higher entropy (disorder). Ludwig Boltzmann quantified this relationship with a simple equation carved on his tombstone in Vienna: S = klnW, where k is a constant called the Boltzmann constant, ln is the natural logarithm (to the base e) and W is the number of states.
Maxwell’s Demon was a mischievous creature which sat on top of a box with a partition in the middle. The box contains molecules of a gas which are ricocheting in every direction. Maxwell himself had found that these molecules’ velocities follow a particular distribution of fast and slow. The demon observes these velocities, and whenever there is a molecule moving faster than usual in the right side of the box, he opens the partition and lets it into the left side, quickly closing the partition. Similarly he lets in slower moving molecules from left to right. After some time, all the slow molecules will be in the right side and the fast ones will in the left. Now, velocity is related to temperature, so this means that one side of the box has heated up and the other has cooled down. To put it another way, the box went from a state of random disorder to order. According to the second law this means that the entropy of the system of the system decreased, which is impossible.
Maxwell’s demon seemingly contravenes the second law of thermodynamics (University of Pittsburgh)
For the next few years scientists tried to get around Maxwell’s Demon’s paradox, but it was in 1922 that the Hungarian physicist Leo Szilard made a dent in it when he was a graduate student hobnobbing with Einstein, Planck and other physicists in Berlin. Szilard realized an obvious truth that many others seem to have missed. The work and decision-making that the demon does to determine the velocities of the molecules itself generates entropy. If one takes this work into account, it turns out that the total entropy of the system has indeed increased. The second law is safe. Szilard later went on to a distinguished career as a nuclear physicist, patenting a refrigerator with Einstein and becoming the first person to think of a chain reaction.
Perhaps unknowingly, however, Szilard had also discovered a connection – a fusion of two fields – that was going to revolutionize both science and technology. When the demon does work to determine the velocities of molecules, the entropy that he creates comes not just from the raising and lowering of the partition but from his thinking processes, and these processes involve information processing. Szilard had discovered a crucial and tantalizing link between entropy and information. Two decades later, mathematician Claude Shannon was working at Bell Labs, trying to improve the communication of signals through wires. This was unsurprisingly an important problem for a telephone and communications company. The problem was that when engineers were trying to send a message over a wire, it would lose its quality because of many factors including noise. One of Shannon’s jobs was to figure out how to make this transmission more efficient.
Shannon found out that there is a quantity that relates to the information transmitted over the wire. In crude terms, this quantity was inversely related to the information as well as to the probability of transmitting that information; the higher the probability of transmitting accurate information over a channel, the lower this quantity was and vice versa. When Shannon showed his result to the famous mathematician John von Neumann, von Neumann with his well-known lightning-fast ability to connect disparate ideas, immediately saw what it was: “You should call your function ‘entropy’”, he said, “firstly because that is what it looks like in thermodynamics, and secondly because nobody really knows what entropy is, so in a debate you will always have the upper hand.” Thus was born the connection between information and entropy. Another fortuitous connection was born – between information, entropy and error or uncertainty. The greater the uncertainty in transmitting a message, the greater the entropy, so entropy also provided a way to quantify error. Shannon’s 1948 paper, “A Mathematical Theory of Communication”, was a seminal publication and has been called the Magna Carta of the information age.
Even before Shannon, another pioneer had published a paper that laid the foundations of the theory of computing. In 1936 Alan Turing published “On Computable Numbers, with an Application to the Entscheidungsproblem”. This paper introduced the concept of Turing machines which also process information. But neither Turing nor von Neumann really made the connection between computation, entropy and information explicit. Making it explicit would take another few decades. But during those decades, another fascinating connection between thermodynamics and information would be discovered.
Stephen Hawking’s tombstone at Westminster Abbey (Cambridge News)
That connection came from Stephen Hawking getting annoyed. Hawking was one of the pioneers of black holes, and along with Roger Penrose he had discovered that at the center of every black hole is a singularity that warps spacetime infinitely. The boundary of the black hole is its event horizon and within that boundary not even light can escape. But black holes posed some fundamental problems for thermodynamics. Every object contains entropy, so when an object disappears into a black hole, where does its entropy go? If the entropy of the black hole does not increase then the second law of thermodynamics would be violated. Hawking had proven that the area of a black hole’s event horizon never decreases, but he had pushed the thermodynamic question under the rug. In 1972 at a physics summer school, Hawking met a graduate student from Princeton named Jacob Bekenstein who proposed that the increasing area of the black hole was basically a proxy for its increasing entropy. This annoyed Hawking and he did not believe it because increased entropy is related to heat (heat is the highest- entropy form of energy) and black holes, being black, could not radiate heat. With two colleagues Hawking set out to prove Bekenstein wrong. In the process, he not only proved him right but also made what is considered his greatest breakthrough: he gave black holes a temperature. Hawking found out that black holes do emit thermal radiation. This radiation can be explained when you take quantum mechanics into account. The Hawking-Bekenstein discovery was a spectacular example of another fusion: between information, thermodynamics, quantum mechanics and general relativity. Hawking deemed it so important that he wanted to put it on his tombstone in Westminster Abbey, and so it has been.
The Landauer bound applies to all kinds of computation in principle and biological processes are also a form of information processing and computation, so it’s tantalizing to ask whether Landauer’s calculation applies to them. Enter Charles Bennett. Bennett is one of the most famous scientists whose name you may not have heard of. He is not only one of the fathers of quantum computing and quantum cryptography but he is also one of the two fathers of the marriage of thermodynamics with computation, Landauer being the other. Working with Landauer in the 1970s and 80s, Bennett applied thermodynamics to both Turing machines and biology. By good fortune he had gotten his PhD in physical chemistry studying the motion of molecules, so his background primed him to apply ideas from computation to biology.
Charles Bennett from IBM has revolutionized our understanding of the thermodynamics of computation (AMSS)
To simplify matters, Bennett considered what he called a Brownian Turing machine. Brownian motion is the random motion of atoms and molecules. A Brownian Turing machine can write and erase characters on a tape using energy extracted from a random environment. This makes the Brownian Turing machine reversible. A reversible process might seem strange, but in fact it’s found in biology all the time. Enzyme reactions occur from the reversible motion of chemicals – at equilibrium there is equal probability that an enzymatic reaction will go forward or backward. What makes these processes irreversible is the addition of starting materials or the elimination of chemical products. Even in computation, only a process which erases bits is truly irreversible because you lose information. Bennett envisaged a biological process like protein translation as a Brownian Turing machine which adds or subtracts a molecule like an amino acid, and he calculated the energy and entropy expenditures involved in running this machine. Visualizing translation as a Turing machine made it easier to do a head-to-head comparison between biological processes and bit operations. Bennett found out that if the process is reversible the Landauer bound does not hold and there is no minimum entropy required. Real life of course is irreversible, so how do real-life processes compare to the Landauer bound?
In 2017, a group of researchers published a fascinating paper in the Philosophical Transactions of the Royal Society in which they explicitly calculated the thermodynamic efficiency of biological processes. Remarkably, they found that the efficiency of protein translation is several orders of magnitude better than the best supercomputers, in some cases as better as a million fold. More remarkably, they found that the efficiency is only one order of magnitude worse than the theoretical minimum Landauer bound. In other words, evolution has done one hell of a job in optimizing the thermodynamic efficiency of biological processes.
But not all biological processes. Circling back to the thinking processes of Maxwell’s little demon, how does this efficiency compare to the efficiency of the human brain? Surprisingly, it turns out that neural processes like the firing of synapses are estimated to be much worse than protein translation and more comparable to the efficiency of supercomputers. At first glance, the human brain thus appears to be worse than other biological processes. However, this seemingly low computational efficiency of the brain must be compared to its complex structure and function. The brain weighs only about a fiftieth of the weight of an average human but it uses up 20% of the body’s energy. It might seem that we are simply not getting the biggest bang for our buck, with an energy-hungry brain providing low computational efficiency. What would explain this inefficiency and this paradox?
My guess is that the brain has been designed to be inefficient through a combination of evolutionary accident and design and that efficiency is the wrong metric for gauging the performance of the brain. Efficiency is the wrong metric because thinking of the brain in digital terms is the wrong metric. The brain arose through a series of modular inventions responding to new environments created by both biology and culture. We now know that thriving in these environments needed a combination of analog and digital functions.; for instance, the nerve impulses controlling blood pressure are digital while the actual change in pressure is continuous and analog. It is likely that digital neuronal firing is built on an analog substrate of wet matter, and that higher-order analog functions could be emergent forms of digital neuronal firing. As early as the 1950s, von Neumann conjectured that we would need to model the brain as both analog and digital in order to understand it. Around the time that Bennett was working out the thermodynamics of computation, two mathematicians named Marian Pour-El and Ian Richards proved a very interesting theorem which showed that in certain cases, there are numbers that are not computable with digital computers but are computable with analog processes; analog computers are thus more powerful in such cases.
If our brains are a combination of digital and analog, it’s very likely that they are this way so that they can span a much bigger range of computation. But this bigger range would come at the expense of inefficiency in the analog computation process. The small price of lower computational efficiency as measured by the Landauer bound would come at the expense of the much greater evolutionary benefits of performing complex calculations that allow us to farm, build cities, know stranger from kin and develop technology. Essentially, the Landauer bound could be evidence for the analog nature of our brains. There is another interesting fact about analog computation, which is its greater error rate; digital computers took off precisely because they had low error rates. How does the brain function so well in spite of this relatively high error rate? Is the brain consolidating this error when we dream? And can we reduce this error rate by improving the brain’s efficiency? Would that make our brains better or worse at grasping the world?
From the origins of thermodynamics and Maxwell’s Demon to the fusion of thermodynamics with information processing, black holes, computation and biology, we have come a long way. The fusion of thermodynamics and computation with neuroscience just seems to be beginning, so for a young person starting out in the field the possibilities are exciting and limitless. A multitude of general questions abound: How does the efficiency of the brain relate to its computational abilities? What might be the evolutionary origins of such abilities? What analogies between the processing of information in our memories and that in computers might we discover through this analysis? And finally, just like Shannon did for information, Hawking and Bekenstein did for black holes and Landauer and Bennett did for computation and biology, can we find out a simple equation describing how the entropy of thought processes relates to simple neural parameters connected to memory, thinking, empathy and emotion? I do not know the answers to these questions, but I am hoping someone who is reading this will, and at the very least they will then be able to immortalize themselves by putting another simple formula describing the secrets of the universe on their tombstone.
John Markowitz who is a clinical psychiatrist at the NIH has a cogent column in the New York Times in which he argues that an excessive focus on neuroscience translational research is stifling useful and proven research in psychiatry. His main point is that the neuroscience research is unproven and long term, and while it may promise attractive dividends, there are many patients who need good psychiatric treatment now, patients who cannot work along the timelines promised by cutting edge neuroscience work. I think in general he's right. Neuroscience seeks to find out the basic mechanisms governing neural health and disease by way of genes, receptors and small molecule drugs. Psychiatry and especially psychotherapy takes a more empirical and holistic approach, trying various combinations of talk therapy and drugs to treat mental illness. Even psychiatry itself has suffered from the kind of crisis that the author talks about. For instance, it is now increasingly clear that talk therapy (especially CBT) works at least as well as psychiatric drugs like antidepressants. To me, at least part of the debate seems to be about a topic that I have often explored on this blog: emergence vs reductionism. Generally speaking, the goals of neuroscience are reductionist, seeking to modulate mental processes in health and disease by understanding and engineering interactions between genes, proteins and drugs at the molecular and network levels. The goals of psychiatry are emergent and empirical. Psychiatry does not care about the underlying molecular mechanisms of mental health; instead its goal is to work at a higher and more holistic level, empirically trying out different approaches until a particular combination of methods seems to show efficacy. It is not surprising that drugs like antidepressants which aim to interact with specific protein receptors in the brain are often found wanting because they target only part of a much larger system. This philosophical difference between neuroscience and psychotherapy also strikes me as being a bit similar to the philosophical difference between chemistry and physics which I have oftentalkedabout here. Physics may want to find out how the world works by tracing the interactions between elementary particles like quarks, but chemists have little use for this information, benefiting tremendously instead by understanding semi-empirical concepts like hydrogen bonds and hydrophobic effects. The time discrepancy that the author points out regarding the fruits of neuroscience research and psychiatry also applies to physics and chemistry; if chemists waited long enough to be able to use physics and understand every complex molecular system from first principles, we would still be living in the age of alchemy. The NYT article ends by appealing to the NIH to not sacrifice proven empirical psychiatry research at the altar of long-term translational research in neuroscience, and this underscores yet another one of the more general problems with translational research that I and others have pointed out. It is why the much celebrated and publicized Brain Initiative troubles me; I fear that it will detract from more mundane but effective psychiatric research. Far flung reductionist research may well promise and eventually bring great insights, but it should not be pursued at the cost of immediately workable emergent research whose very lack of precision makes it so useful.
Here's a very interesting video of mathematical physicist Edward Witten - widely regarded as perhaps the most brilliant mind in the field of the last fifty years - holding forth on consciousness (the relevant part begins at 1:10:25).
Many people regard consciousness as the last nut to crack at the frontier of science. If we crack that nut it would open the way to an unprecedented understanding of humanity that may in part explain why mankind produces thinkers like Ed Witten that allow us to understand the deep secrets of the universe. But Witten is not too optimistic about it. And he seems to have fairly clear reasons for believing that consciousness will always remain a mystery. Here's what he has to say (italics mine).
"I think consciousness will remain a mystery. Yes, that's what I tend to believe. I tend to think that the workings of the conscious brain will be elucidated to a large extent. Biologists and perhaps physicists will understand much better how the brain works. But why something that we call consciousness goes with those workings, I think that will remain mysterious.I have a much easier time imagining how we understand the Big Bang than I have imagining how we can understand consciousness...
Understanding the function of the brain is a very exciting problem in which probably there will be a lot of progress during the next few decades. That's not out of reach. But I think there probably will remain a level of mystery regarding why the brain is functioning in the ways that we can see it, why it creates consciousness or whatever you want to call it. How it functions in the way a conscious human being functions will become clear. But what it is we are experiencing when we are experiencing consciousness, I see as remaining a mystery...
Perhaps it won't remain a mystery if there is a modification in the laws of physics as they apply to the brain. I think that's very unlikely. I am skeptical that it's going to be a part of physics.
Later on he talks a bit about Roger Penrose's thesis on why we could never build an AI simulating the human mind, and why we may need a modification of the laws of physics to account for consciousness: Witten personally disagrees with the latter stance. I am partial myself toward the belief that we may not understand consciousness simply because you cannot truly understand a system which you are already a part of. But what Witten is saying here is in some sense quite simple: even if we understand the how of consciousness, we still won't understand the why. This kind of ignorance of whys is not limited to consciousness, however. For instance among other things, we don't know why our universe happens to be the one in which the tuning of the fundamental constants of nature is precisely such that it allows the evolution of sentient human beings which can ask that question. We don't know why the elementary particles have the masses that they do. We don't know why the eukaryotic cell evolved only once. It's interesting to contrast Witten's thoughts with John Horgan's "End of Science" thesis. In that case Horgan is really saying that the fundamental laws of physics have been largely discovered. They cannot be discovered twice and they almost without question won't ever be fundamentally modified. But Horgan's thesis applies in a larger sense to the whys that Witten is treading on. The end of science really is the end of the search for final causation. In that sense not just consciousness but many aspects of the world may always remain a mystery. Whether that is emotionally pleasing or disconcerting is an individual choice that each one of us has to make.
There is a wonderful episode of
Star Trek (The Next Generation) titled “The Measure of a Man” which
tackles an issue that all of us take for granted. The episode asks if Data –
the highly intelligent and indispensable android on the USS Enterprise – has
self-determination. In fact, Data faces an even greater challenge: he has to
prove that he is equivalent to a person. If he cannot do this he faces a grim
fate.
We might think it’s easy enough to decide if an android is a
person or not, partly because we think we know how we define ourselves as
persons. But is it really that simple? Can we actually ascribe unique,
well-defined qualities to ourselves that lend themselves to a singular definition
of “personhood”? Can we be sure that these qualities distinguish us from cats
and jellyfish? Or mountains and computers for that matter?
Let’s throw down the gauntlet right
away in the form of a question: A tree and I are both composed of the same kinds
of atoms – carbon, hydrogen, oxygen and a handful of others. So what makes me a
person and the tree a mere tree, a non-person?
To a simple first approximation, the difference lies in the
arrangement of those atoms. Clearly the arrangement is different in a tree, in
a lion and in me. But it’s not just the arrangement of the parts, it’s the
connections between them. If you delete enough connections between the neurons
in a human brain, at some point the person possessing that brain would clearly
cease to be a sentient human being. The same goes for the connections between
the cells in a tree.
Thus, simply from a physical standpoint, a person is an object
that presents an arrangement of atoms in a particular configuration. But that
definition comes no closer to telling us exactly what makes that particular
arrangement special. To get some insights into these reasons, it’s worth
thinking about the concept of emergence.
Emergence is a very common phenomenon, and in its basic
incarnation it simply means that the whole is different from the sum of the
parts; or, as the physicist Philip Anderson put it in a seminal article in
1972, “More is Different”. Going back to our example, the human brain may be
composed of the same atoms as a tree, but because of the unique arrangement of
these atoms and the connections between them, the sum total of these atoms
possesses properties that are very different from those of the individual
atoms. Just as an example, a carbon atom in our brain can be uniquely defined
by what are called quantum numbers – atomic parameters related to properties
like the spin of the atom’s electrons, the energy levels on which the electrons
lie and their angular momentum. And yet it’s downright absurd to talk about
these properties in the context of the brain which these atoms make up. Thus
it’s the emergent properties of carbon and other atoms that contribute to the
structure and function of the human brain.
Emergent properties of individual atoms don’t uniquely make a
human person, however, since even the brains of cats and dogs exhibit these
properties. We don’t yet have a perfect understanding of all the qualities that
distinguish a cat’s brain from a human’s, but we do know for certain that at
least some of those qualities pertain to the size and shape of parts of the
brains in the two species and the exact nature of the connections between their
neurons. For instance, the cerebral cortex in a human brain is bigger and far
more convoluted than in a cat. In addition the human brain has a much greater
density of neurons. The layering of neurons is also different.
Taking our idea of emergence further, each one of these
qualities is an emergent property that distinguishes cat brains from human
brains. There are thus different levels of emergence. Let’s call the emergence
of properties arising when individual atoms coalesce into neurons Level I
emergence. This level is very similar for humans and cats. However, the Level II
emergence which arises when these neurons connect differently in humans and
cats is very different in the two species. The Level III emergence that arises
when these connections give rise to modules of a particular size and shape is
even more different. The interactions of these modules with themselves and with
the environment presumably give rise to the unique phenomenon of human
consciousness: Level IV emergence. And finally, the connections that different
human brains form with each other, giving rise to networks of families,
friends, communities and societies, constitute an overarching Level V emergence
that truly distinguishes persons not just from cats but also from every other
creature that we can imagine.
This idea of thinking in terms of different levels of emergence is useful because it captures both similarities and differences between
persons and non-persons, emphasizing the common as well as the distinct
evolutionary roots of the two kinds of entities. Cats and human beings are
similar when defined in terms of certain levels of emergence, but very
different when defined in terms of ‘higher order’ levels.
The foregoing discussion makes it sounds as if a simple way to
distinguish persons from non-persons would be to map different levels of
emergence on each other and declare something to be a non-person if we are
successfully able to map the lower levels of emergence but not the higher ones. I think this is generally true if we are comparing animals with
human beings. But the analogy is actually turned on its head when we start to
compare humans with a very important and presumably non-person object: a
sophisticated computer. In that case it’s actually thehigher order emergent functions
that can be mapped on to each other, butnotthe lower order ones.
A computer is
built up of silicon rather than carbon, and silicon and carbon are different
emergent entities. But as we proceed further up the hierarchy, we start to find
that we can actually simulate primitive forms of human thinking by connecting
silicon atoms to each other in specific ways. For instance, we can teach the
silicon-based circuitry in a computer to play chess. Chess is presumably a very
high level (Level VIIXXI?) emergent human property, and yet we can simulate
this common higher order property from very different lower order emergent
properties. Today computers can translate languages, solve complex mathematical puzzles and defeat Go champions. All of these accomplishments constitute higher levels of emergent behavior similar to human behavior, arising from lower levels that are very different from those in human.
In fact it is precisely this kind of comparison that allowed
Captain Jean-Luc Picard to secure personhood for Data. Data is a human-machine hybrid
that is built up from a combination of carbon and non-carbon atoms. His
underlying molecular structure is thus very different from those of persons.
But the higher order emergent functions he exhibits – especially free will
allows Picard to make a convincing case for Data to be treated as a person.
This crucial recognition of emergent functions in fact saves Data’s life. It's what compels the man who is trying to dismantle him to address him as "he" instead of "it".
Whether it’s Data or a dolphin, a computer or a catfish, while
it’s probably not possible to give a wholly objective and airtight definition
of personhood, framing the discussion in terms of comparing different levels of
emergent functions and behaviors provides a useful guide.
More is indeed different.
This piece was published yesterday in an issue of '3-Hours', the online magazine of Neuwrite Boston.
I have written
about the ‘Big Brain Project’ a few times before including a
post for the Canadian TV channel TVO last year. The project
basically seeks to make sense of that magnificent 3-pound bag of jelly inside
our skull at multiple levels, from molecules to neurons to interactions at the
whole brain level. The aims of the project are typical of ‘moon shot’
endeavors; ambitious, multidisciplinary, multi-institutional and of course,
expensive. Yet right after the project was announced in both the US (partly by
President Obama) and in Europe there were whispers of criticism that turned
first into a trickle and then into a cascade. The criticism was at multiple
levels – administrative, financial and scientific. But even discounting the administrative
and financial problems, many scientists saw issues with the project even at the basic scientific level.
The gist of those issues can be boiled down to one phrase:
“trying to chew on more than we can bite off”. Basically we are trying to
engineer a complex, emergent system whose workings we still don’t understand,
even at basic levels of organization. Our data is impoverished and our
approaches are too reductionist. One major part of the project especially
suffers from this drawback – in-silico
simulation of the brain at multiple levels, from neurons to entire mouse and
human brains. Now here’s a report from a committee which has examined the pros
and cons of the project and reached the conclusion that much of the criticism
was indeed valid, and that we are trying to achieve something for which we still
don’t have the tools. The report is
here. The conclusion of the committee is simple: first work on the tools; then
incorporate the findings from those tools into a bigger picture. The report
makes this clear in a paragraph that also showcases problems with the public’s skewed
perception of the project.
The goal of
reconstructing the mouse and human brain in
silico and the associated comprehensive bottom-up approach is viewed by
one part of the scientific community as being impossible in principle or at
least infeasible within the next ten years, while another part sees value not
only in making such simulation tools available but also in their development,
in organizing data, tools and experts (see, e.g.,
http://www.bbc.com/future/story/ 20130207-will-we-ever-simulate-the-brain). A
similar level of disagreement exists with respect to the assertion that
simulating the brain will allow new cures to be found for brain diseases with
much less effort than in experimental investigations alone.
The public relations
and communication strategy of the HBP and the continuing and intense public
debate also led to the misperception by many neuroscientists that the HBP aims
to cover the field of neuroscience comprehensively and that it constitutes the
major neuroscience research effort in the European Research Area (ERA).
This whole discussion reminds me of the idea of tool-driven
scientific revolutions publicized by Peter
Galison, Freeman
Dyson and others, of which chemistry
is an exemplary instance. The Galisonian picture of scientific revolutions
does not discount the role of ideas in causing seismic shifts in science, but
it places tools on an equal footing. Discussions of grand ideas and goals (like
simulating a brain) often give short shrift to the mundane but critical
everyday tools that need to be developed in order to enable those ideas in the
first place. They are great for sound bytes for the public but brittle in their
foundations. Although scientific ideas are often considered the progenitors of
a lot of everyday scientific activity by the public, in reality the progression
can equally often be the opposite: first come the tools, then the ideas.
Sometimes tools can follow ideas, as was the case with a lot of predictions of
the general theory of relativity. At other times ideas follow the tools and the
experiments, as was the case with the Lamb Shift and quantum
electrodynamics.
Generally speaking it’s more common for ideas to follow
tools when a field is theory-poor, like quantum field theory was in the 1930s, while
it’s more common for tools to follow ideas when a field is theory-rich. From
this viewpoint neuroscience is currently theory-poor, so it seems much more
likely to me that ideas will follow the tools in the field. To be sure the importance
of tools has long been recognized in neurology; where would we be without MRI
and patch-clamp techniques for instance? And yet these tools have only started
to scratch the surface of what we are trying to understand. We need much better
tools before we get our hands on a theory of the brain, let alone one of the
mind.
I believe the same progression also applies to my own field
of molecular modeling in some sense. Part of the problem with modeling proteins
and molecules is that we still don’t have a good idea of the myriad factors
that drive molecular recognition. We have of course had an inkling of these
factors (such as water and protein dynamics) for a while now but we haven’t
really had a good theoretical framework to understand the interactions. We can
wave this objection away by saying that sure we have a theoretical framework,
that of quantum mechanics and statistical mechanics, but that would be little
more than a homage to strong reductionism.
The problem is we still don’t have a handle on the quantitative contribution of
various factors to protein-small molecule binding. Until we have this conceptual
understanding the simulation of such interactions is bound to suffer. And most
importantly, until we have such understanding what we really need is not
simulation but improved instrumental and analytical techniques that enable us
to measure even simple things like molecular concentrations and the kinetics of
binding. Once we get an idea of these parameters using good tools, we can start
incorporating the parameters in modeling frameworks. Now the brain project is indeed working on tools too, but
reports like the current one ask whether we need to predominantly focus on those
tools and perhaps divert some of the money and attention from the simulation
aspects of the project to the tool-driven aspects. The message from the current
status report is ultimately simple: we need to first stand before we can run.
Bell's number calculates the number of connections between
various components of a system and scales exponentially
with those components (Image: Science Magazine).
I have been reading an excellent collection of essays on the brain titled "The Future of the Brain" which contains ruminations on current and future brain research from leading neuroscientists and other researchers like Gary Marcus, George Church and the Moser husband and wife pair who won last year's Nobel prize. Quite a few of the authors are from the Allen Institute for Brain Science in Seattle. In starting this institute, Microsoft co-founder Paul Allen has placed his bets on mapping the brain…or at least the mouse visual cortex for starters. His institute is engaged in charting the sum total of neurons and other working parts of the visual cortex and then mapping their connections. Allen is not alone in doing this; there’s projects like the Connectome at MIT which are trying to do the same thing (and the project’s leader Sebastian Seung has written a readable book about it). Now we have heard prognostications about mapping and reverse engineering brains from more eccentric sources before, but fortunately Allen is one of those who does not believe that the singularity is around the corner. He also seems to have entrusted his vision to sane minds. His institute’s chief science officer is Christof Koch, former professor at Caltech, longtime collaborator of the late Francis Crick and self-proclaimed “romantic reductionist” who started at the institute earlier this year. Koch has written one of the articles in the essay collection. His article and the book in general reminded me of a very interesting perspective that he penned in Science last year which points out the staggering challenge of understanding the connections between all the components of the brain; the “neural interactome” if you will. The article is worth reading if you want to get an idea of how even simple numerical arguments illuminate the sheer magnitude of mapping out the neurons, cells, proteins and connections that make up the wonder that’s the human brain. Koch starts by pointing out that calculating the interactions between all the components in the brain is not the same as computing the interactions between, say, all atoms of an ideal gas since unlike a gas, the interactions are between different kinds of entities and are therefore not identical. Instead, he proposes, we have to use something called Bell’s number Bn which reminds me of the partitions that I learnt about when I was sleepwalking through set theory in college. Briefly for n objects, Bn refers to the number of combinations (doubles, triples, quadruples etc.) that can be formed. Thus, when n=3 Bn is 5. Not surprisingly, Bn scales exponentially with n and Koch points out that B10 is already 115,975. If we think of a typical presynaptic terminal with its 1000 proteins or so, Bn starts giving us serious heartburn. For something like the visual cortex where n= 2 million Bn would be inconceivable, and it's futile to even start thinking about what the number would be for the entire brain. Koch then uses a simple calculation based on Moore’s Law in trying to estimate the time needed for “sequencing” these interactions. For n = 2 million the time needed would be of the order of 10 million years. And as the graph on top demonstrates, for more than 105 components or so the amount of time spirals out of hand at warp speed. This considers only the 2 million neurons in the visual cortex; it doesn’t even consider the proteins and cells which might interact with the neurons on an individual basis. In addition, at this point we are not even really aware of how neuronal types there are in the brain: neurons are not all identical like indistinguishable electrons. What makes the picture even more complicated that these types may be malleable so that sometimes a single neuron can be of one type while at other types it can team up with other neurons to form a unit that is of a different type. This multilayered, fluid hierarchy rapidly reveals the outlines of what Paul Allen has called the “complexity brake”: he described this in the same article that was cogently critical of Ray Kurzweil's singularity. And the neural complexity brake that Koch is talking about seems poised to make an asteroid-sized impact on our dreams. So are we doomed in trying to understand the brain, consciousness and the whole works? Not necessarily, argues Koch. He gives the example of electronic circuits where individual components are grouped separately into modules. If you bunch a number of interacting entities together and form a separate module, then the complexity of the problem reduces since you now have to only calculate interactions between modules. The key question then is, is the brain modular, and how many modules does it present? Commonsense would have us think it is modular, but it is far from clear how we can exactly define the modules. We would also need a sense of the minimal number of modules to calculate interactions between them. This work is going to need a long time (hopefully not as long as that for B2 million) and I don’t think we are going to have an exhaustive list any time soon, especially since these are going to be composed of different kinds of components and not just one kind. But it's quite clear that whataver the nature of these modules, delineating their particulars would go a long way in making the problem more manageable. Any attempt to define these modules are going to run into problems of emergent complexity that I have occasionally written about. Two neurons plus one protein might be different from two neurons plus two proteins in unanticipated ways. Also if we are thinking about forward and reverse neural pathways, I would hazard a guess that one neuron plus one neuron in one direction may even be different from the same interaction in the reverse direction. Then there’s the more obvious problem of dynamics. The brain is not a static entity and its interactions would reasonably be expected to change over time. This might interpose a formidable new barrier in brain mapping, since it may mean that whatever modules are defined may not even be the same during every time slice. A fluid landscape of complex modules whose very identity changes every single moment could well be a neuroscientist’s nightmare. In addition, the amount of data that captures such neural dynamics would be staggering since even a millimeter sized volume of rat visual tissue requires a few terabytes of data to store all its intricacies. However, the data storage problem pales in comparison to the data interpretation problem. Nevertheless this goal of mapping modules seems far more attainable in principle than calculating every individual interaction, and that’s probably the reason Koch left Caltech to join the Allen Institute in spite of the pessimistic calculation above. The value of modular approaches goes beyond neuroscience though; similar thinking may provide insights into other areas of biology, such as the interaction of genes with proteins and of proteins with drugs. As an amusing analogy, this kind of analysis reminds me of trying to understand the interactions between different components in a stew; we have to appreciate how the salt interacts with the pepper and how the pepper interacts with the broth and how the three of them combined interact with the chicken. Could the salt and broth be considered a single module? If we can ever get a sense of the modular structure of the brain, we may have at least a fighting chance to map out the whole neural interactome. I am not holding my breath too hard, but my ears will be wide open since this is definitely going to be one of the most exciting areas of science around. Adapted from a previous post on Scientific American Blogs.
Consciousness and the Physical World: Proceedings of the Conference on Consciousness Held at the University of Cambridge, 9Th-10th January, 1978
