What Will Disappear by 2030?

Cindy Wagner, the future-savvy editor at The Futurist magazine is running a new feature, Disappearing Futures: What Won’t Be Around in 2030? They are looking for around 300 words, a good length for crowdsourced submissions. Email Cindy if you’d like to send in your own. I sent a shorter version of the piece below, considering three things I expect to disappear (or head greatly in that direction) over this timeframe: Endangered Languages, Economic Immigration Barriers, and Mass Fundamentalist Religious Intolerance. Agree or disagree? Let me know in the comments, thanks.

The leading language learning software on the planet. Waiting to be disrupted by something entirely free and crowdsourced, like Wikipedia.

True Wearable wristphone concept, 2007. Someone make this now. Please.

By 2020, the ubiquity and affordability of wearable smartphones (Google glass, wristphones, etc), and the power of the conversational interface (Google Now, etc.) will give enterprising youth everywhere access to “teacherless education,” lifelong learning by talking to their peers and machines. The “killer app” of teacherless education will be learning a developed nations language while learning their own, increasingly from birth. Their wearable will “listen in” as they learn their native language, and deliver the same words in the foreign language, along with images, learning aids, and games that reinforce and test their proficiency, and post their language skill level on global recruiting, collaboration, and microwork networks (LinkedIn, oDesk, etc.). Imagine a Rosetta Stone that’s free, wearable, conversational, and 24/7, and you’re foreseeing what I call Global English. Want to help it emerge faster? Call me, I’ve got a few ideas.

As this linguistic convergence accelerates, perhaps one fourth of the ~6,000 languages spoken today, mostly the 3,000 or so endangered languages, may disappear by 2030, and other less-spoken ethnic languages will continue to lose mindshare, as developed nations languages with the most open cultures increasingly take their place. While we mourn the loss of endangered languages and the minds that speak them, what matters most is making sure their cultural history, values, and semantic complexity are captured in the languages we continue to speak. We’ll also see many more scientific, technical, business, social, and artistic “languages” (knowledge bases) increasingly taught from birth with these amazing learning systems.

A mix of merit-based and humanitarian immigration has always been a key driver of economic and cultural wealth. Politicos may not want it, but the internet will accelerate global virtual immigration.

English, the global language of business today (and much easier to learn than Chinese, it’s closest competitor), should benefit most, bringing English-speaking nations as many as 1 billion new “virtual immigrants” by 2030, growing the total English-language workforce on the order of 50% in two decades, a rate of growth we haven’t seen since Industrial Revolution-era immigration. In the high-bandwidth 2020’s, many economic barriers to participating in the global economy will disappear. Eager underemployed youth anywhere, speaking the same language and increasingly understanding the same global culture, will be able to work with large and small companies everywhere, vastly accelerating global innovation and entrepreneurship in the 2030′s and beyond. That will be an amazing time.

While most youth and adults will likely use machine translation for any contact with outside cultures (certainly the most convenient option), the “death of language learning” predicted by some futurists by the 2030′s simply won’t occur. Indeed, enterprising companies like Open English are accelerating English-language learning today with $1000, yearlong, four-person online classes. Imagine what will happen when this price drops to free, the learning is 24/7, and the AI behind it gets good. Consider further how the parents who push their children to learn a leading foreign language (or two) will give those children both measurably greater economic opportunities and I believe, provably greater collaborative and cognitive fluencies, since learning a foreign language and getting immersion experiences in that culture, even digitally, allows you to better think in, work with, and understand that culture. Although there are as yet no good measures for the semantic size of vocabularies in our languages, (a topic we care about, unlike R.L.G.’s conclusion in the post I’ve linked to) it is well known that leading languages have by far the largest semantic vocabularies by comparison to languages spoken by just a few hundred thousand people. English is often claimed to have a special place in this regard, having absorbed so many words and concepts from other cultures, and with deep technical vocabularies, that some estimate that it has over 1 million words now. Of all the knowledge bases one could easily learn at birth, choice of language(s) seems key. Linguists and cognitive psychologists have argued for decades that language influences thought. What we can all agree on is that semantic complexity influences thought, and that some languages have much more of it than others. It is also true that learning just a tiny percentage (perhaps 2%?) of the words in most languages can give you basic fluency in that language, and we can expect to see a lot more of this kind of polyglot learning of languages in the future.

One day, when we hit the tech singularity (which I’m guessing will be in the 2060′s, and it’s just a guess because acceleration studies doesn’t exist yet as a funded field, we have some waking up still to do) I imagine the AIs will create, and teach us all, a new global language that is a semantic mashup of all the best of our global cultures, even more than our mongrel English, and with a structure that is grammatically easier and phonetically far more efficient (perhaps by using all 100 phonemes we use across all cultures, instead of just the 20 or so in a typical language) than anything that exists today. An Esperanto for the late 21st century. But until that time arrives, what seems obvious to me is that English, the most widely taught foreign language today, will continue to win as the collaboration language of choice in coming decades, just as cities will continue to win over rural areas.  And those who speak in any language will have a much richer ability to interact with all others who use that language.

Now for perhaps the most controversial prediction. As long as global science, technology, free trade, and wealth continue to accelerate, as I expect they will, and our resilience to catastrophes of all types continues to grow, all the major religions and ideologies will grow more ecumenical and secular as well. Mass fundamentalist religious intolerance, still a serious issue today (Islamists of the West, Hindus of Dalits, Christians of gays, etc.) will be decimated in the ambiently intelligent, hyperconnected world of 2030. Specifically, fundamentalist religious movements ability to use economic or other catastrophes to roll back social reforms at national levels, as we saw in the Iranian Revolution in 1979 (there were many reasons for the revolution but reactionary mass religious fundamentalism was a key driver, as I’ve written before), will have disappeared for good, in any nation. Political and religious fundamentalist backlashes within subcultures will always be with us, but we can expect (and hope) they’ll be far more circumscribed, weak, and short-lived in a world of youth who see those views as extremist, arrogant, and counterproductive. Amen!

About the Author: John M. Smart is a technology foresight scholar, educator, speaker, and consultant. President, Acceleration Studies Foundation. Blog: http://EverSmarterWorld.com

Keep Calm and Carry On – Reacting to the Boston Marathon Bombing

I’ve had some deep discussions today about the Boston Marathon bombings with friends. Here’s something I shared with a friend who lives in the Boston area in Massachusetts. His predominant feeling right now is disillusionment. If you’re in the same boat, I hope you find it helpful in some way. Thanks for any feedback.

Friend, I hope this event won’t shake your faith in humanity or in the continued acceleration of global progress, or in our ability to better understand what progress is, and for reasons yet to be discovered, why accelerating progress seems only partly under our control, and partly driven by the amazingly intelligent and self-correcting environment into which we were born.

There are always half of one percent of us who are seriously broken in some way. It is surprising, when you stop to think about it, that majority of us are so strongly against doing such cowardly and terrible things. Almost all violence is rapidly self-limiting. It can be a calculation of fairness, a seeking of justice in the wild. Or a case of beliefs being seriously out of step with reality, or emotions not being sufficiently regulated. Fortunately, for the vast majority of us, our moral sentiments and desire to cooperate are incredibly deep, selected and self-organized over countless previous life cycles. At the same time, our tools and policies for protecting the world system get only better and smarter. We must understand these processes better, and aggressively work to improve them in society and the individual.

The mentally ill, extremists and oligarchs throughout history are a persistently tiny fraction of society. The main effect of mental illness events like this (these particular bombings, irrational as they are, are even more a mental and psychological illness than an extremist/terrorist event, as I see it), aside from their tragic short-term cost, is to grow our global immunity to them in future years. If we learn from them (a critical “if”), they accelerate the emergence of the transparency tools and social development programs that we know is our future, and as long as it is increasingly a bottom-up, citizen-driven transparency and social development process, we gain greater control over both the extremists and the autocrats, our democracy strengthens, and the world gets collectively more intelligent. Imagine, as social and media futurist Alvis Brigis says, if it was ten years in the future and one out of twenty people in that Boston crowd had been wearing Google Glass or an equivalent? (I’m a Glass Explorer, so I’m looking forward to getting an early adopter version of this fantastic new wearable computer and lifelogging tech). They’d all be able to share their recent archives and feeds and it wouldn’t be long before we’d have the perpetrators identities and last public locations.

Mental illness is one issue, but what about oligarchy (government by elites, without representation) and plutocracy (government by the wealthy), and the way such governments breed extremism in the developing world by replacing culture with commercialism, removing self-determination and representation, and inducing cornered cultures to react with Fundamentalism? If increasing political, economic, and social fairness is a clear vector of social progress, how do we keep building it in all our societies in the years ahead?

With regard to the plutocrats, there is good news: our global rich poor divide has never been smaller. It was highest in the 13th century  under Feudalism by several measures, and has slowly decreased ever since. But the problem we face is that in the world’s leading and fastest developing countries inequality seesaws, at first going up as the wealth of new technology revolutions is initially captured by the well-capitalized few, and then later down again as the revolution works its way out to the many, where the maturing and cheapening tech allows disruptive new entrepreneurship on top of the platform, and as new rights and entitlements eventually emerge.

The Finland Phenomenon, a great film on the education reform the US needs to make more self-reliant, innovative, and less fearful citizens.

As Joseph Stiglitz discusses in The Price of Inequality, 2013, we need a certain amount of income inequality to spur innovation, but if we let it get too big, the wealthy and the corporations capture our political machinery, only their interests are represented, and democracy, political reform, and political compromise and moderation die. Due to tech globalization’s great wealth creation, income inequality has grown rapidly in the last 60 years in a handful of nations, in the 1970′s-80′s in the US, UK, and Israel, and in the 1990′s and 2000′s also in rapidly developing countries like China and Brazil (and to a much lower degree, in a few low-inequality countries like Germany and Sweden). In the U.S., asset inequality is now so extreme that just 1% of us own 40% of the nation’s wealth. When our lower and middle classes can no longer find meaningful jobs under constant technological change, while we see other developed nations doing far better with education and job creation, we should not be surprised. We let this happen, by letting our MNCs get larger than governments (instead of splitting them up, as we used to), and by dismantling progressive income and inheritance tax for the wealthy (which last existed seriously in the US in the 1950′s).

To bring this back to the theme of this post, another big price of plutocracy is that our citizens lose the ability to engage with the developing world an empathic and positive-sum way, and our fear grows. We fear technological progress, as the job disruption dumps us into a degraded society that doesn’t keep job creation and retraining as the top priority. We fear the further loss of jobs via outsourcing. We fear immigration, and forget that merit-based immigration is one of the fastest creators of new jobs, science, and industries. We fear other belief systems, and we demonize the other, rather than finding common cause with the moderates in every religion and group. As our political system gets captured by unresponsive and polarized elites (they are wealth driven and fight hard to divide the spoils among themselves), tough social problems like educational reform don’t get done. See The Finland Phenomenon for an excellent example of what we can will one day do to fix our broken educational system, when we finally get the political will. In the meantime, our citizens grow increasingly globally ignorant, inward-focused, and politically apathetic, or polarized and uncompromising like their wealthy masters.

Source: Growing Unequal?, OECD 2008.
Click the graphic for the report.

But, thank the Universe, America is an outlier, with our elites capturing such an outsized portion of the new technological wealth in the last six decades that we are going temporarily against the global trend. We will eventually reverse this and be forced, by accelerating technoeconomic integration, to get back to the global trend. The developed OCED countries as a whole aren’t following our sad course of sixty years of rapidly increasing income inequality and 60% higher levels of income poverty, as the 2008 OECD graphic at right shows. Remember that for the global economy, the absolute size of the inequity gap is still closing since Feudalism. As visionary books like Abundance, 2012, make clear, we can see how extreme global economic and educational poverty will disappear just a few decades hence.  Many of the emerging nations are now in the process of growing their GDP two or three times faster than us. Check out Gapminder.org for some beautiful graphs telling that story. If we’re thinking at all about accelerating tech, we can see a new world of the conversational interface and of teacherless education (to use futurist Thomas Frey’s great phrase) less than ten years hence, where every literate and illiterate child has a wearable waterproof smartphone on their wrist, listening in to what they are learning and teaching them who knows what.

Accelerating technology always causes evolutionary disruption in the first phase. More money goes to the rich and the leading corporations, at first, rather than the rest of society from any new technological and trade revolution, be it industrial, transportation, mass consumption, communications, personal computing, internet, web services, or any other revolution affecting the global marketplace. In the U.S. and a few other countries, these and other revolutions have been the dominant story of the latest 60 years of globalization. In turn, the vast new wealth increase of the MNCs, many of whom now have revenues larger than those of the leading countries, and their unrestrained effects on the developing world, has been a great driver of the clash of cultures and the extremist events we see today. We are pushing citizens in many of these cultures to change at a rate far faster than their reformists are comfortable with, and successive waves of technology innovation are driving them (and us, but always to a far lesser degree) continually out of their livelihoods into a globally wealthier but, in the absence of good retraining and social safety nets, a much more socially uncertain future.

Eventually the global system, being not only evolutionary but also developmental, always gains irreversible new levels of total positive-sum integration, and immunity. For the system as a whole, virtuous cycles are always underway and antifragility will increasingly dominate, if global development is like living systems development, as I believe it is. I hope you can find a way to see and guide the positive changes that will come from this tragic event, as they surely must.

Bruce Schneier, Security Maven

So regarding our emotions and actions around this bombing, with a potential to cause disproportionate fear and immune response, as occurred after 9/11, I think Bruce Schneier’s brief piece in The Atlantic says it best: “Keep Calm and Carry On.” Let’s not overreact, overspend, overregulate. Let’s not fixate on or overgeneralize this rare event itself, or get scared. Let’s continue to work calmly on the social development processes (income equity, representation, education, psych services, job creation, civics, religious tolerance and reform) that will reduce the probability of this happening again, and the transparency processes (primarily bottom up, and secondarily top down cameras, sensors, networks, databases, pattern recognizers, human intelligence) that will increase our ability to find, isolate, and help (or at least, prevent from further harm) the broken folks or individual who did this.

Let’s implement our actions carefully and incrementally, while always insuring their social benefits exceed their costs. Let’s keep calm and carry on.

Leadership of Technological Change (35 min video)

A recent keynote, at USNI’s West Conference, Jan 2013, San Diego, CA. The talk has three parts:

1. A brief intro to evolutionary developmental foresight, a strategically useful theory of change for leaders,

2. A selection of important developmental (highly probable) opportunities, disruptions, and threats I think we can expect in coming years due to accelerating technological change,

3. Strategies for innovation, management, and foresight (IMF) with respect to technological change that can be employed by middle and senior mgmt.

Those who want one quick takeaway may enjoy the last minute, starting at 35:06, which wraps up with a Navy innovation brand vision for an Open Oceans GIS Platform. I think something like this could be a big win-win for Navy global transparency and partnership activities, and with luck, some Navy service leader is out there now championing a variant of this idea.

Hope you like it! As always let me know your thoughts below or by email (johnsmart{at}accelerating{dot}org),  thanks.

Mankind Rising: Why Evolutionary Developmentalism Will Inherit the Future

Evo Devo Universe – Exploring Models of Universal Evolution and Development

What is evolutionary development (“evo devo”)? It is a minority view of change in science, business, policy, foresight and philosophy today, a simultaneous application of both evolutionary and developmental thinking to the universe and its replicating subsystems. It is derived from evo-devo biology, a view of biological change that is redefining our thinking about evolution and development. As a big picture perspective on complex systems, I think evo devo models will be critical to understanding our past, present, and future. The sixty-some scholars at Evo Devo Universe, an interdisciplinary community I co-founded with philosopher Clement Vidal in 2008, are interested in arguing, critiquing and testing evolutionary and developmental models of the universe and its subsystems, and exploring their variations and implications.

Whatever else our universe is, and allowing that there are physical mysteries, like dark matter, dark energy, the substructure of quarks, and the nature of black holes still to be uncovered, reasonable analysis suggests that it is both evolutionary and developmental, or “evo devo”. Like a living organism, it undergoes both experimental, stochastic, divergent, and unpredictable change, a process we can call evolution , and at the same time, programmed, convergent, conservative, and predictable change, a process we can call development. Evo devo thinking is practiced by any who realize that parts of our future are unpredictable and creative, while other parts are predictable and conservative, and that in the universe, as in life, both processes are always operating at the same time.

Like living organisms, our universe may have a developmental life cycle.

Our universe builds intelligence in a developmental hierarchy as it unfolds, from physics, to chemistry, to biology, to biominds, to postbiological intelligence. As physicists like Lee Smolin (The Life of the Cosmos, 1999) have argued, our universe may also be chained to a developmental life cycle, like a living organism. Since almost every interesting complex system we know of within the universe, from solar systems to cells, undergoes some form of replication, inheritance, variation, and selection to build its complexity, it is parsimonious (conceptually the simplest model) to suspect this is how the universe built its complexity as well, within a still poorly understood environment that physicists call the multiverse.

An evo devo universe answers theologian William Paley’s famous watchmaker argument, that only a God could have designed our planet’s breathtaking complexity, with the observation that any physical system that has both evolutionary  (variation) and developmental (replication, inheritance) features, and operates in a selective environment, will self-organize its own adaptive complexity as replication proceeds. Consider how replicating stars have advanced from the primitive Population III stars to the far more complex Population I solar systems, like our Sun and its complex planets, over galactic time. Replicating evo devo chemicals built up from nucleic acids to cells, over billions of years. Replicating evo devo cells created multicellular life with nervous systems,  again over billions of years.  Replicating evo devo nervous systems forged hominids, over roughly 500 million years. Replicating languages, ideas, and behaviors in hominid brains birthed nonbiological computing systems, over something like 5 million years. Now computing and robotics systems, whose replication is presently aided by human culture, are soon (within the next few decades, it seems) going to be able to replicate, evolve, and develop autonomously. As much as some might find comfort in believing in a God who designed our universe, it is perhaps even more comforting to believe, tentatively and conditionally, in the history and abilities of this evolutionary and developmental  self-organization process itself. Evo devo processes have apparently created both matter and mind, and have been astonishingly resilient to generating complexity and intelligence at ever-accelerating rates. These processes may even transcend our universe, and may have determined the first replicator, if such a thing exists. Then again, perhaps our physics and information theory will never reach back that far, and such knowledge may forever remain metaphysics. In the meantime we can say that Big History, the science story of the universe so far, is sufficiently awe inspiring, humbling, useful, and hopeful to give us guidance, once we place it in an evo devo frame. As we’ll suggest, we now know enough about evolution and development at the universal scale to begin relating these processes to our own lives, and most interestingly, to ask how we can make our values and goals more consistent with universal processes.

As our universe grows islands of accelerating local order and intelligence in a sea of ever-increasing entropy, physics tells us this process cannot continue forever. The universe’s “body” is aging, and will end in either heat death, or a big rip, or both. If our universe is indeed a replicating complex adaptive system that engages in both evolution and development, as it grows older it must package its intelligence into some kind of reproductive system, so it’s complexity can survive its death and begin again. Developmental models thus argue that intelligent civilizations throughout the universe are part of that reproductive system – protecting our complexity and ultimately reproducing the universe and further improving the intelligence it contains. In other words, growing, protecting, and reproducing personal, family, social, and universal intelligence may be the evolutionary developmental purpose of all intelligent beings, to the greatest extent that they are able.

Charles Darwin, On the Origin of Species, 1859

Beginning in 1859, Charles Darwin helped us to clearly see evolutionism in living systems, for the first time. Discovering that humanity was an incremental, experimental product of the natural world was a revolutionary advance over our intellectually passive, antirational and humanocentric religious beliefs. But until we also understand and accept developmentalism, recognizing that the universe not only evolves but develops, the purpose and values of the universe, and our place in it will remain high mysteries about which science has little of interest to say. Our science will remain infantile, descriptive without also being prescriptive, and unable to deeply inform our morality and politics. That must and will change in coming decades.

Curiosity – A Discover Channel TV Series

As an example of where we are today, I just watched a Discovery Channel program on evolution, Mankind Rising, available for $1.99 at YouTube.  It is Season 2, Episode 8 of Curiosity a new educational television series launched by Discovery founder and chairman John Hendricks. Curiosity is a five-year, multi-million dollar initiative to tackle fundamental questions and mysteries of science, technology, and society, in sixty episodes. There is also a commendable Curiosity initiative in American K-12 schools, to use the show to increase our children’s engagement in STEM education.

Mankind Rising – Season 2, Episode 8 of Curiosity

Mankind Rising considers the question “How did we get here?” It tells the journey of humanity from the cooling of life’s nursery, Earth, 4 billion years ago, and the emergence of the first cell 3.8 billion years ago, to Homo erectus, anatomically modern humans, 1.8 million years ago. It does this in one 43 minute time-lapsed computer animation, the first time our biological history has received such a treatment, as far as I know. The animation is primitive, but it holds your interest enough to follow the story. And what an amazing story it is.  We see a lovely visualization of the Phylogenetic hypothesis, which proposes that human hiccups are a holdover from our amphibian ancestry, when we gulped air at the surface across our gills, which are now vestigial (think of pharyngeal pouches in human embryos), before we grew lungs. Human babies do a lot of gulping-hiccuping both in utero and when born prematurely, and both amphibian gill-gulping and human hiccups are stopped by elevated carbon dioxide, hence the folk remedy to breathe into a bag to stop them.

We also get to see the rise of the first tool users, Homo habilis, 4 million years ago, in a dramatic sequence where an early human strikes one rock against another and is fascinated to discover a sharp rock in his hands. H. habilis’ ability to hold sharp rocks and clubs in their hands, and use them imitatively in groups to defend against other animals was perhaps the original human event. The best definition of humanity, in my opinion, is any species that gains the ability to use technology creatively and socially to continually turn themselves into something more than their biological selves. We inevitably become a species with both greater mind (rationality, intellect) and greater heart (emotion, empathy, love), two core kinds of intelligence. I would predict the first collaborative rock-users on any Earth-like planet must soon thereafter become its dominant species, as there are so many paths to further adaptiveness from the powerful developmental duo of creative tool use and socially imitative behavior.

Homo habilis, perhaps the first persistence hunters.

One clever thing that the first socially-adept rock- and club-holding animals on any Earth-like planet gain access to is pack hunting (and if good at sweating,  persistence hunting). Learning these two skills may have doubled our brain size by giving us our first reliable access to meat, a very high-energy fuel source. We may have begun with pack hunting by ambush, which chimpanzees do today, and then graduated to persistence hunting, or running down our prey. We primates sweat across our entire bodies, not just through our mouths like other mammals. Humans have developed our sweating and cooling ability the best of all primates by far. As a result, two or three of us can run to heat exhaustion any animals that can’t sweat, if we hunt them in the mid-day sun.  Some people persistence hunt even today, as seen in this seven minute Life on Earth clip of San Bushmen running down a Kudu antelope in the Kalahari desert. Mankind Rising ends with Homo erectus (“human upright”), possibly the first language-using humans, 1.8 million years ago. We don’t yet have fossil evidence their larynx was anatomically modern, but there are indirect arguments.  Language, both a form of socially imitative behavior and a fundamental tool for information encoding and processing, was very likely the final technology needed to push our species from the animal to the human level.

In Evolutionism, the Universe is a Massive Set of Random Events, Randomly Interacting.

Unfortunately, there are serious shortcomings to Mankind Rising as an educational device. The show’s narrative, and the theory it represents, are the standard one-sided, dogmatically-presented story of life’s evolution, with no hint of life’s development. As a result, it treats humanity’s history as one big series of unpredictable accidents. This is the perspective of universal evolutionism, also called “Universal Darwinism” ”, which considers random selection to be the only process in universal change, ignoring the possibility of universal development. In evolutionism, all the great emergence events are told as happening randomly and contingently. The show even makes the extreme claim that life itself emerged “against the laws of probability.” The emergence of humanlike animals is also presented as a stroke of blind luck, because the K-T meteorite wiped out our predators, the dinosaurs. All of this is true in part, only from one set of perspectives, that of the individual, organism, or individual event. In other words this story, and evolutionism in general, is a dangerously incomplete half-truth.

When we look at the same events from the perspective of the universal system, the environment, or distributions of events over time, we can easily argue that many particular forms and functions appear physically predetermined to emerge. Consider two genetically identical biological twins, or two snowflakes. Most of what happens to them up close, at the molecular scale, is randomly, contingently, unpredictably different. The microstructure of all the twin’s organs, including their brain, fingerprints, and many other molecular features are as different as the designs on two snowflakes. But look at them from across the room, taking a system or environmental perspective, and you see that they achieve many of the same developmental endpoints over time. The twins have the same body and facial structure and many of the same personality traits, constrained by the organism’s developmental genes and the shared environment. The snowflake’s hexagonal structure is developmentally predetermined, constrained by the way water forms hydrogen bonds as it freezes.

Just like biological development, universal development happens because of the special initial conditions (physical laws, or “genes”) of our universe, the time constancy and environmental sameness (isotropy) of some physical law throughout the universe/system/environment, and the apparent commonness (ubiquity) of Earth-like planets in our universe, a suspicion that will hopefully be proven by astrobiologists in coming years. Examples of developmental processes and structures are easy to propose. We can see developmental physics in the motions of the planets, which are highly future-predictable, as Isaac Newton discovered. Other physical processes, such as the production of black holes in general relativity, the acceleration of entropy production, and of complexification in special locations, also appear highly predictable and universal. Other physics by contrast, such as nonlinear dynamics and quantum physics, looks highly evolutionary and unpredictable. As we move up the complexity hierarchy from physics to chemistry to biology, to society, our list of potential evolutionary and developmental forms and processes rapidly grows.

In Developmentalism, Certain Universal Forms and Functions are Statistically Fated to Emerge, as in Biological Development

Convergent form and function in placental and marsupial mammals – a famous example of convergent evolution, or better, convergent evolutionary development.

Other examples of inevitable, ubiquitous developments in our universe may include organic chemistry as the only easy path to complex molecular evo devo, iron-core, watery planets with carbon and nitrogen cycles and plate tectonics, and replicating nucleic acids, lipid membranes, amino acids, and proteins as the only easy path to cells, oxidative phosphorylation redox chemistry as the only easy path to high-energy chemical life, multicellular organisms, bilateral symmetry, eyes, skeletons, armor, jointed limbs, land colonization, wings, opposable thumbs, brains, language, social intelligence, written language, math, science, and various technology archetypes, from sharp rocks and clubs to levers, wheels, electricity, and computers. These potentially universal forms and functions may be destined to emerge, because of the particular initial conditions and laws of the universe in which evolution is occurring, and each are destined to become optimal or dominant, for their time, in environments in which accelerating complexification and intelligence growth are occurring.

On Earth, we have seen a number of these forms, such as eyes, emerge and persist independently in various separate evolutionary lineages and environments. Independent emergence, convergence, and optimization or dominance of developmental forms and processes is one good way to separate them from the much larger set of ongoing evolutionary experiments. Developmental forms and functions are those that will be more adaptive at each particular stage of environmental complexity, in more contexts and species. Think of two eyes for a predator, over three or one eye. Or four wheels for a car, over three or more than four wheels. Or all the body form and function types that converged in placental and marsupial mammals. Australia separated early from the other continents, yet produced many similar mammal types via marsupials, plus a few new ones, like the kangaroo. This is a classic example of convergent evolution, or more accurately, convergent evolutionary development, when we examine biological change from the planet and universe’s perspective.

Evolution is destined to randomly, contingently, and creatively but inevitably discover these optimal developmental forms and functions, in most environments. For more on evolutionary developmentalism, feel free to read my 50-page precis, Evo Devo Universe?, 2008, and let me know your thoughts. To see one place where evo devo theory may eventually lead us, you might also enjoy my 12-page paper, The Transcension Hypothesis, 2012, which argues that black holes are uniquely strong attractors for the future of intelligent life throughout the universe.

Would Raptors Have Led Inevitably to Dinosaur Humanoids (Dinosauroids), if the K-T Meteorite Hadn’t Hit 65 Mya? That is the Dinosauroid Hypothesis, a Developmentalist Proposal.

As another example ignored by the show, several evolutionary developmentalists have independently proposed that our very-easy-to-create-yet-general-purpose  “humanoid form”, a bilaterally symmetric bipedal tetrapod with two eyes and two opposable thumbs, is a very likely outcome for all biological intelligences that first achieve our level of sophistication on all Earth-like planets. If we saw such “early” alien intelligences from across a dimly lit room, if they somehow had access to a way to reach us very quickly, before they turned postbiological (they apparently don’t, and postbiologicals apparently have other interests) they would they look roughly like us, as the astrophysicist Frank Drake, author of the Drake equation, has argued. Our mammalian history may have given us a unique evolutionary pathway to our developmental humanity, one with its own irreplaceable value, but if the K-T meteorite hadn’t hit, it is easy for an evolutionary developmentalist to argue that the dinosaurs would have independently and inevitably discovered the humanoid form, rocks, language, and tools. Why?

If you have seen the movie Jurassic Park, or have read up on raptors like Troodon, you know that they had semi-opposable digits and hunted in packs, in both the day and the night. It is easy to bet that the first raptor descendants that also learned how to hold sharp rocks and clubs in their hands in close-quarters combat would have forever after owned the role of top biological species. It would be game over, and competitive exclusion, for all other species that wanted that niche. Once you are manipulating tools in your hands, and speaking with your larynx, it’s easy to imagine that your body is forced upright, and your tail is no longer useful. You are engaged in runaway complexification of your social and technical intelligence – you’ve become human, and the leading edge universal intelligence has jumped to a higher substrate. Dale Russell, author of the Dinosauroid hypothesis in 1982, was scoffed at by conventional evolutionists back then, and the model is still largely ignored today, see for example Wikipedia’s short and evolutionist-biased paragraph on it. This treatment is most likely because of the hornet’s nest of implications that evolutionary developmentalism introduces. [I need to do an ISI Web of Knowledge search at a University to find all papers that have referenced Russell's paper in the 30 years since its publication, to review everyone who has looked seriously at the issue of dinosauroid evolutionary development since. Anyone want to volunteer for that?]

The hand of Troodon inequalis, with a partly opposable digit.

Note the closeup of the hand of Stenonychosaurus (now called Troodon) inequalis, from Russell’s paper, “Reconstructions of the small cretaceous theropod Stenonychosauris inequalis and a hypothetical dinosauroid,” Dale A. Russell and Ron Séguin, Syllogeus, 37, 1982. The authors state the structure of the carpal block on Troodon’s hands argues that one of the three fingers partially opposed the other two as shown. The shape of the ulna also suggests its forearms rotated. It probably used its hands to snatch small prey, and to grab hold of larger dinosaurs while ripping into them with the raptorial claw on the inside of each of its feet. Troodon was a member of a very successful and diverse clade of small bipedal, binocular vision dinosaurs with one free claw on their feet, the Deinonychosaurs (“fearsome claw lizards”). These animals lived over the last 100 million years of the 165 million years of dinosaur existence, and were among the smartest and most agile dinosaurs known, with the highest brain-to-body ratios of any animals in the Mesozoic era. Most Deinonychosaurs had hands that were a useful combination of small wings and three long claws. Troodon was in a special subfamily that had lost the wings but retained the three long digits on each hand. According to Russell, Troodon’s brain-to-body ratio was the highest known for dinosaurs at the time. Because of their special abilities, I would argue that Deinonychosaurs  were not only members of an evolutionarily successful niche, they also occupied an inevitably successful developmental niche as well.

The assumption here, made by a handful of anthropologists and evolutionary scholars over the years, is that trees are a key niche, the “developmental bottleneck,” through which the first rock-throwing and club-wielding hominids will very likely pass, on a typical Earth-like planet. Swinging from limb to limb requires very dextrous hands, and just as importantly, a cerebellum and forebrain that can predict where the body will go in space. With their manipulative hands, with or without wings, their big, strong legs and multipurpose feet, yet their small size, Deinonychosaurs would have been impressive tree climbers, able to get rapidly up and down from considerable heights. If they were the largest and strongest animals physically capable of doing so, which seems likely, this argues that they would have permanently occupied the special niche that primates would later inhabit. Imagine primates trying to get into the trees later with Deinonychosaurs running about – I can’t. If  tree climbing and swinging is the fastest and best way to build grasping hands and predictive brains good at simulating complex trajectories and eventually, simulating and imitating the mental states of others in their pack, then if Deinonychosaurs dominated that niche, it is reasonable to expect a Deinonychosaur to be the first to make the jump to tool use. Troodon couldn’t swing in the trees, but he would have been very agile among them, able to use them for escape and evasion. He had two manipulative hands that would have been very useful both in killing and in avoiding being killed. This looks to me like a case for competitive exclusion. Tree environments may be the dominant developmental place on land to breed smart, socially-imitative and tool-using species, just as land appears to be the dominant developmental place for all species that use built structures, on any Earth-like planet, because water is a much less efficient and forgiving fluid than air. The irrepressible Jacques Cousteau discovered that octopi build small houses for themselves out of rocks, but tides and currents in water are very unforgiving, so it is logical that on any planet where land is also available, runaway tool use will happen on land first.

The universe, from its perspective, will use the first language-capable tool-using species to start selection for smarter and more social brains and ideas, in a new complexity space of memetic evo devo, using Richard Dawkin’s concept of the meme as any elemental mentally replicating behavior or idea, and also selecting for increasingly complex and self-aware technologies, in a new complexity space of technetic evo devo, using Susan Blackmore’s concept of the techneme as an elemental socially replicating technological form or algorithm. Once this explosive emergence happens, biological evo devo (genetic change) has now becomes so slow by comparison that its further changes are increasingly future irrelevant. Now memetic change (ideas) in concert with technetic change (tech forms and algorithms) drive the future.

Simon Conway Morris – A Leading Evolutionary Developmentalist (but he might not use that term)

In the years since Russell’s indecent proposal, hundreds of other scientists, including the paleontologist Simon Conway Morris (Life’s Solution, 2001 and The Deep Structure of Biology, 2008) have proposed that humanity’s most advanced features, including our morality, emotions, and tool use, have all been independently discovered, to varying degrees, in other vertebrate and invertebrate species on Earth. If something happened to us, we can be confident that another species would very quickly emerge to become the dominant “human” tool-users in our place. In other words, local runaway complexification seems well protected by the universe. There is a developmental immune system operating, to ensure that human emergence, and remergence if catastrophes like the K-T meteorite occur, is both an inevitable and an accelerating event. Only the quality of our present transition to postbiological (not “posthuman”) status seems evolutionary, based on the morality and wisdom of our actions. Our pathway to and our subtype of humanity my thus be special and unique, but our humanity itself, in many of its key features, seems to be a product of the universe, far more than a product of our own free choice. Learning to see, accept, and better manage all this hidden development is the great challenge of our era.

Fortunately, these and other developmentalist hypotheses can increasingly be tested by computer simulation, as our computing technology, historical data, and scientific theory get progressively better. Run the universe simulation multiple times, and anything that appears environmentally dominant time and again is developmental. The rest, of course, is creative and evolutionary. To recap our earlier example, hexagonal snowflake structure will be developmental on all Earth-like planets with snow. But the pattern on each snowflake  will be evolutionary, and unpredictably unique, both on Earth and everywhere else. Nature uses both types of processes to build intelligence.

In Evolutionary Development, the Universe is not just a Ladder of Nature (above), or a Random Experiment (standard Evolutionary theory), but some useful combination of the two simpler models.

Let me stress here that evolutionary development is no return to the Aristotelian scala naturae (Ladder of Nature, Great Chain of Being), where all important matter and process are predestined by God into some strict hierarchy of emergence. Only the developmental framework of universal complexification is statistically predetermined in evo devo models, not the evolutionary painting itself, which is the bulk of the work of art. Nor is it a Newtonian or Laplacian “clockwork universe” model, which proposed total physical predetermination. Each of these models of are universal development without meaningful evolution. They are as one-sided and incomplete as Darwinian theory is today. Nor is it the Blind Tinkerer that universal evolutionists like Richard Dawkins (The Blind Watchmaker, 1996) or the writers of Mankind Rising portray. It appears that our universe is more complex and interesting than these models suppose – it is predictable in certain critical parts that are necessary for its function and replication, and it is intrinsically unpredictable and creative in all the rest of its parts. Furthermore, unpredictable evolution and predictable development may be constrained to work together in ways that maximize intelligence and adaptation, both for leading-edge systems, and for universe as a system.

A Good Overview of Evo-Devo Biology

 Evo-devo biology is an academic community of several thousand theoretical and applied evolutionary and developmental biologists who seek to improve standard evolutionary theory by more rigorously modeling the way evolutionary and developmental processes interact in living systems to produce biological modules, morphologies, species, and ecosystems.  Books like From Embryology to Evo-Devo, 2009, and Convergent Evolution: Limited Forms Most Beautiful, 2011, are great intros to this emerging field. I expect most evolutionary developmental biologists would agree with the statement that evolution and development are in many ways opposite and equally fundamental processes in complex living systems, and that neither can be properly understood without reference to its interaction with the other.

If you doubt the idea of universal development, read this great 2011 book!

The best of this work realizes there are two key forms of selection and fitness landscapes operating in natural selection – evolutionary selection, which is divergent and treelike, with chaotic attractors, and developmental selection, which is convergent and funnel-like, with standard attractors. Thus evolutionary developmentalism is an attempt to generalize the evo-devo biological perspective to nonliving replicating complex adaptive systems as well, including solar systems, prebiological chemistry, ideas, technology, and in particular, to the universe as a system.

Let’s close this overview with one revealing example of the interaction of evolution and development. In biological systems, the vast majority of our genes, roughly 95% of them, are evolutionary, meaning they change randomly and unpredictably over macroscopic time, continually recombining and varying as species reproduce. Only about 3-5% of our genes control our developmental processes, and those highly conserved genes, our “developmental genetic toolkit“, direct predictable changes in the organism as it traces a life cycle in its environment. As I’ve argued before, as a 95%/5% Evo/Devo Rule, roughly 95% of the processes or events in a wide variety of complex adaptive systems, including organizations, societies, species, and the universe may turn out be creative bottom-up and evolutionary, and only 5% may be predictable top-down, and developmental, though this evo devo ratio must surely vary by system to some degree. The generic value of a 95/5 Rule in building and maintaining intelligent systems, if one exists, would explain why the vast majority of universal change appears to be evolutionary and unpredictable in complex systems, what systems theorist Kevin Kelly called Out of Control in his lovely 1994 book. Yet a critical subset of events and processes in these systems also appears to be developmental. Discovering that subset will make our world vastly more understandable, and show how it is constrained to certain future destinies, even as creativity and experimentation keep growing in the evolutionary domains.

So what do we gain from conditionally holding and exploring the hypothesis of universal evolutionary development? Quite a lot, I think:

First, we regain an open mind. Rather than telling humanity’s history from a dogmatic and one-sided perspective, and assuming that our past existence in the universe is predominantly a “random accident,” we remember that there are many highly predictable things about our universe, such as classical mechanics, the laws of thermodynamics, and accelerating change. This allows us to present life’s story as a mystery: What parts of its emergence are very highly probable, or statistically predetermined? What parts are improbable accidents? We lose our blind faith that neo-Darwinism explains all of life or the universe, and we realize that there appears to be a balance between evolutionary experiment and developmental predetermination in all things in the universe, as in life.

Second, we regain our humility. We no longer see ourselves as special, miraculous accidents. It is commonly suggested that we are incredibly unique in the universe, and that we emerged “against astronomical odds.” On the contrary, developmentalists suspect that many or all of the things we hold most dear about humanity, including our brains, language, emotions, love, morality, consciousness, tools, technology, and scientific curiosity, are all highly likely or even inevitable developments on Earth-like planets all across the universe. This kind of thinking, looking for our universals as well as our uniquenesses, moves us from a Western exceptionalism frame of mind to one that also includes an Eastern or Buddhist perspective. We may not only be unique and individual experiments, but we may also be members of a type that is as common as sand grains on a beach, instruments of a larger cycle of universal development and replication.

Third, we lose our unjustified fearfulness of and pessimism toward the future, and replace it with courage and practical optimism. The evolutionary accident story of humanity teaches us to be ever vigilant for things that could end our species at any moment. Vigilance is adaptive, but fear is usually not. We are constantly reminded by evolutionists that 99% of all species that ever lived are extinct (yes, but they were all necessary experiments, and their useful information lives on), and we live in a random, hostile and purposeless universe (no). Evolutionists conveniently forget that the patterns of intelligence in those species that died are almost all highly redundantly backed up in the other surviving organisms on the planet. Life is very, very good at preserving relevant pattern, information, and complexity, and now with science and technology, it is getting far better still at complexity protection and resiliency. When we study how complexity has emerged in life’s history, we gain a new appreciation for the smoothness of the rise of complexity and intelligence on Earth. Every catastrophe we can point to appears to have primarily catalyzed further immediate jumps in life’s accelerating intelligence and adaptiveness at the leading edge. Life needs regular catastrophe to make it stronger, and it is resilient beyond all expectation. What causes this resilience? Apparently a combination of evolutionary diversity and developmental immune systems, and we are still undervalue the former, and are mostly ignorant of the latter. If the universe is developmental, we can expect it has some kind of immune systems protecting its development, just as living systems do. The more we are willing to consider the idea that the universe may be both evolving and developing, the more we can open our eyes to hidden processes that are protecting and driving us toward a particular, predetermined future, even as each individual and civilization on Earth and in the universe will take its own partly unpredictable and creative evolutionary paths.

Fourth, we gain an understanding of universal purpose. Talk of purpose legitimately scares most scientists, who are so recently free of religion interfering in their work. They claim they don’t want to return to a faith-based view of the world, but we all have and should constantly revise a set of faiths, as our reason and intuition still leave so much unexplained. Unexamined faiths are the most dangerous kind. Evolutionists put a lot of unexamined and unrecognized faith in their purposeless universe model, so much that it can blind them to the value of admitting and exploring the unknown. Many scientists attack hypotheses of universal teleology wherever they find them – even as they live in a world that they clearly know is predictable in part. We must call that stance hypocrisy, as predictability is a basic form of teleology, or purpose. Evolutionary and behavioral psychologists are now proposing biologically-inspired scientific theories of human values. I recommend The Moral Landscape, by Sam Harris, 2011, which I’ve reviewed earlier.  But most of this work still is not deeply biologically-inspired, as it remains focused on evolution, ignoring development. We must recognize that a better understanding of universal evolution and development can help science derive more useful and more universal evolutionary and developmental values. I believe it is both the best definition and the purpose of humanity to use technology to continually reshape us, individually and collectively, into something more than our biological selves, and to do this in as deliberate and ethical a way as possible, using both evolutionary and developmental means. We can further realize that it appears to be our universal purpose to think, feel, act, and build in ways that maximize our intellectual and emotional intelligence, advancing our minds and hearts.

Fifth, we recognize that very important parts of the future are predictable. This benefit is the most useful to me as a professional futurist. Increasingly, we find foresight practitioners who accept the likelihood of developmental futures. Consider Pierre Wack at Royal Dutch/Shell’s foresight group, who proposed the inevitable TINA (There Is No Alternative) trends in economic liberalization and globalization in the 1980′s. Or Ron Inglehart and Christian Welzel, who have charted the inevitable developmental advance (with brief and partial evolutionary reversals) of evidence-based rationalism and personal freedom in all nations over the last 40 years.  Some leading recent books arguing for the inevitability of certain kinds of social development are Robert Wright’s Nonzero, 2000 (on positive sum rulesets), Steven Pinker’s The Better Angels of Our Nature (on violence reduction) and Ian Morris’s, The Measure of Civilization, 2013 (I have not read the latter but am very much looking forward to it). There are still far too many professional futurists who confidently and ignorantly claim that the future is entirely evolutionary (“cannot be predicted”). But a growing number of leaders, strategists, and futurists see regionally and globally dominant trends and inevitable convergences, make good predictions, and use increasingly better data and feedback to improve their models.

Great New Book on Prediction

For a good recent book on this, read Nate Silver’s excellent The Signal and the Noise: Why So Many Predictions Fail But Some Don’t, 2012. I may review this book in a future post. As we learn take an evolutionary developmentalist perspective, at first unconsciously and later consciously, we will greatly grow our predictive capacity in coming decades. More of us will foresee, accept, and start managing toward the ethical emergence of such inevitable coming technological developments as the conversational interface and big data, deeply biologically-inspired (evo and devo) machine intelligence and robotics, cybertwins/digitwins and the values-mapped web, lifelogs and peak experience summaries, the wearable web and augmented reality, teacherless education, and the metaverse. Professional futurists and forecasters are now developing our first really powerful tools and models that will keep expanding our prediction domains and horizons, and improving the reliability and accuracy of our forecasts. I believe evolutionary developmentalism is a foundational model that all long range forecasters and strategists need to embrace. Not only must we realize there are possible and preferable futures ahead of us, but we must be convinced that there are inevitable and highly probable futures as well, futures which can increasingly be uncovered as our intelligence, data, and methods improve. Such an effort, at a species level, is the only way we can map what remains truly unpredictable, at each level of our collective intelligence.

We’ve got a long way to go before modern science is willing to give the developmentalist perspective the same consideration and intellectual honesty that we presently give the evolutionist perspective. A lot of papers will have to be published. A lot of arguments will have to be made, and evidence marshaled. Courageous scientists will have to build the bridge from the developmentalist aspects of physics, chemistry, and biology to the highest aspects of our humanity, our ethics, consciousness, purpose, and spirituality. Convergent Evolution is one of several fields that will win lots of converts to developmentalism as it advances. Astrobiology will likely also play a big role, if it shows us just how common our type of life is in the universe, as many suspect it will.

A Classic in Business Foresight – Parts of the Future are Quite Predictable. Ignore at Your Peril.

Fortunately, as futurist Alvis Brigis noted to me in a recent conversation, many of the world’s leading companies are already surprisingly developmentalist in their strategy and planning. We can trace this shift back at least to Pierre Wack’s strategy group at Royal Dutch/Shell in the 1980′s, as discussed in Peter Schwartz’s The Art of the Long View, 1996, a classic in business foresight. Wack realized that in order to do good scenario planning (exploring “what could happen”, and the best strategic responses to major uncertainties) one should first constrain the possibility space by understanding what is very likely to continue to happen in the larger environment.  To restate this in evo devo language, Wack recommended starting with developmental foresight, and then doing evolutionary foresight (exploring alternative futures) within a testable developmental frame. Treating both evo and devo foresight perspectives seriously is a key challenge for strategy leaders. Many management and foresight consultancies are good at one, but not the other, as it’s a lot easier to pick one perspective as your dominant framework than to have to continually figure out how to integrate two opposing processes. Yet both are critical to understanding and managing change.

I do technology foresight consulting for several companies, and follow foresight work at the consultancies, and I’m convinced that those companies with the best predictions, forecasts, and foresight processes interfacing with their strategic planning groups are winning increasingly large advantages in their markets every year. All the most successful companies realize there are many highly predictable aspects of our future, and collectively our business and government leaders are now betting trillions annually on their predictions. A few are using good foresight processes, but most are still flying by the seat of their pants.

The executives leading our most successful companies don’t see the world as a random accident, like an evolutionist, or some naive and self-absorbed postmodernist who lives off the exponentiating wealth and leisure of the very same science and technology that he argues are “not uniquely privileged perspectives” on the universe. Let’s hope our young scientists in coming years have the courage to be as developmentalist in their research, strategy, and perspective as our leading corporations are today. And as our biologically-inspired intelligent machines, destined to be faster and better at pattern recognition than us, will be a few decades hence. Will modern science recognize the evolutionary developmental nature of the universe before human-surpassing machine intelligences arrive and definitively show it to us? That is hard to say. But I believe we can predict with high probability that as mankind continues its incredible rise, our leaders, planners, and builders must become evolutionary developmentalists if we are to learn to see reality through the universe’s eyes, not just our own.

Preserving the Self for Later Emulation: What Brain Features Do We Need?

Let me propose to you four interesting statements about the future:

1. As I argue in this video, chemical brain preservation is a technology that may soon be validated to inexpensively preserve the key features of our memories and identity at our biological death.
2. If either chemical or cryogenic brain preservation can be validated to reliably store retrievable and useful individual mental information, these medical procedures should be made available in all societies as an option at biological death.
3. If computational neuroscience, microscopy, scanning, and robotics technologies continue to improve at their historical rates, preserved memories and identity may be affordably reanimated by being “uploaded” into computer simulations, beginning well before the end of this century.
4. In all societies where a significant minority (let’s say 100,000 people) have done brain preservation at biological death, significant positive social change will result in those societies today, regardless of how much information is eventually recovered from preserved brains.

These are all extraordinary claims, each requiring strong evidence. Many questions must be answered before we can believe any of them. Yet I provisionally believe all four of these statements, and that is why I co-founded the Brain Preservation Foundation in 2010 with the neuroscientist Ken Hayworth. BPF is a 501c3 noprofit, chartered to put the emerging science of brain preservation under the microscope. Check us out, and join our newsletter if you’d like to stay updated on our efforts.

As one of the themes of this blog I’ll try to explain why I’m optimistic about these technologies, and to enlist your help in pushing forward their validation or falsification as fast as feasible. If validated, I’ll be pitching to you for help in making the brain preservation option accessible and affordable around the world, as fast as feasible. To these ends, thank you for any frank and constructive feedback you can leave in the comments.

In this post, I’d like to try to provisionally answer a question relevant to the first three statements above:

To preserve the self for later emulation in a computer simulation, what brain features do we need?

We can distinguish three distinct information processing layers in the brain:[1]

1. Electrical Activity (“Sensation, Thought, and Consciousness”)
These brain features are stored from milliseconds to seconds, in electrical circuits.
2. Short-term Chemical Activity (Short- and Intermediate-term Learning – “Synapse I”)
These brain features are stored from seconds to a few days in our neural synapses (synaptome), by temporary molecular changes made to preexisting neural signaling proteins and synapses.
3. Long-term Molecular Changes (Long-term Learning – “Nucleus and Synapse II”)
These are stored from years to a lifetime in our neuron’s connectome, nucleus (epigenome) and synaptome, by permanent molecular changes to neural DNA, the synthesis of new neural proteins and receptors in existing synapses, and the creation of new synapses.

At present, it is a reasonable assumption that only the third layer, where long-term durable molecular changes occur, must be preserved for later memory and identity reanimation. The following overview of each of these layers should help explain this assumption.

1. Electrical Activity (“Sensation, Thought, and Consciousness”)

Our electrical brain includes short-distance ionic diffusion in and between neurons and their supporting cells (i.e., calcium wave communication in astrocytes), action potentials (how neurons send signals from their dendrites to their synapses), synaptic potentials (how signals cross the gaps between neurons), circuits (loops and networks) and synchrony (neurons that fire in unison, though they are widely separated). Electrical features operate at very fast timescales, from milliseconds to a few seconds, and are variable (not exact), volatile, and easily disrupted.

Neural Synchrony – Our Leading Model of Higher Perception and Consciousness . Image: Senkowski et.al., 2008

These features certainly feel very important to us. They include our sensations (sensory memory) and current thoughts (commonly called “short-term” memory by neuroscientists). Recurrent loops, special electrical circuits that cycle back on themselves, hold our current thoughts (when you rehearse some information to avoid forgetting it, you are literally keeping it “in the loop”). Neural synchrony creates our conscious perceptions, and when it happens in the self-modeling areas of our brain, it gives us self-aware consciousness.

Yet electrical features are also fleeting. When you sleep, or are knocked unconscious, or are given an anesthetic, your consciousness disappears, only to be “rebooted” later, from more stable parts of your brain. Our memories aren’t even recalled with precision but are rather recreated, as volatile electrical processes, from these molecular long-term stores, in ways easily influenced by our mental state and cognitive priming (what else is on our mind). That’s why eyewitness testimony is so variable and unreliable.

The electrical features of our self are thus like the “foam” on the top of the wave of our long-term memories and personality. They make us unique for a moment, as they hold only our most immediate thinking processes.[2] Amazingly, people who undergo special surgeries that stop their heart, and some who drown in very cold water, can have no detectable EEG (electrical patterns) for more than thirty minutes, and their brains successfully reboot after rewarming them. Essentially, these individuals are recovering from clinical brain death. Not only do they not have consciousness during this period, they have no unconscious thoughts. Yet because their deeper layers aren’t too disrupted, they can restart their electrical activities.

An excellent book about neural spikes, loops, and synchrony is Rhythms of the Brain, Gyorgy Buzsaki, 2006. It explains the emergent properties and integrative functions of these “highest order” electrical features of our brain. See also this recent discovery of electric field coupling among neighboring neurons, by leading neuroscientists Henry Markram, Christof Koch, and others, and reported by Peter Hankins on his great cognitive science blog, Conscious Entities. Ephaptic coupling is a way for neurons to synchronize spike timing in neighboring neurons, via a mechanism completely independent of synapses. Neurons are much more versatile in modes of communication and synchrony than previously thought.

My late mentor at UCSD, Francis Crick, and his Caltech collaborator, Christof Koch, call this topic the search for the Neural Correlates of Consciousness. It’s a great phrase. Consciousness is not a mystery we’ll never solve, but according to a number of neuroscientists it is a physical process of neural synchrony, in particular regions of your brain. These brief, rhythmic synchronizations share information between groups of neurons in distant regions of the brain by tightening up (“binding”) their interdependent sequences of action potentials. The synchronizations are controlled by the inhibitory neurons in our brain, which use the GABA neurotransmitter. Disrupt gamma synch, as with anesthesia, and you take away consciousness. Give a drug like zolpidem, which activates GABA neurons and increases gamma synch, to patients who are in persistent vegetative state, and amazingly, you will wake 60% of them up from their comas, to varying degrees! Wikipedia doesn’t yet have a good explanation of the gamma synchrony model of consciousness, but they will in a few more years. Laura Colgin at Kavli has found two reliable gamma synch mechanisms in rat hippocampus. She speculates that slow gamma makes stored memories available to current consciousness, and fast gamma integrates sensations to create conscious perceptions.  Though neuroscientists don’t yet all agree on the details, many have found neural correlates of sensations, thoughts, emotions, and consciousness in the electrical features of our brains. In conjunction with the short-term chemical changes we will describe next, these processes represent both our “highest” and our most volatile and impermanent self.

2. Short-term Chemical Activity (Short- and Intermediate-term Learning – “Synapse I”)

Short-term chemical activity is the next layer down. It involves all our short- and intermediate term learning and memory, everything beyond our sensations, current thoughts, and consciousness, but not including our long-term memories. We can call this layer “Synapse I.”

As our electrical experiences and thoughts race around the various circuits in our heads, we make a number of short-term learning changes in our neural networks to capture, for the moment, what we’ve just experienced and learned. These involve changes to preexisting proteins in our preexisting synapses (communication junctions), changes that last for minutes (short-term) to days (intermediate-term). These are changes in both the mechanics of neurotransmitter release and short-term facilitation (strengthening) or depression (weakening) of synaptic effectiveness. Synapses are temporarily modified by the precise timing and frequency of electrical signals (action potentials) received by the postsynaptic neuron, a process called spike-timing dependent plasticity. There are short-term changes in signaling molecules (neurotransmitters, cAMP, Ca++, CamKII, PKA, MAPK), and membrane receptors (NMDA). Phosphorylation states (chemical tags) are altered on some of these molecules, and a temporary equilibrium between kinases (enzymes that add phosphates to key molecules) and phosphatases (enzymes that take them away) is established in the synapse. [Note: In late 2012, Ye et. al. showed in Aplysia how precise spatiotemporal signaling in the synapse involving PKA holds short-term memories in synaptic electrochemical networks, and the interaction of PKA and MAPK holds intermediate-term memories in these networks, in a process called synaptic facilitation.] If any of these short- or intermediate-term memories or thinking patterns are selected to become long-term, communication with the cell nucleus must now occur, and new membrane proteins and synapses are then built, involving new or altered circuits in the connectome. If not, the new memory or pattern dies out.[3]

Every night, when we sleep, our short- and intermediate-term brain writes important parts of its experiences to our long-term memory, building durable new synaptic connections, where this learning can now stay with us for years to life, in a process called memory consolidation. This process moves a subset of our recent learning and memories, apparently the most relevant parts, from temporary spatiotemporal signaling states to permanent new synaptic structures, anchored to the cytoskeleton of each neuron. We can think of these new proteins, synapses, and circuits established in neural synapses and nuclei in a way that is very roughly like DNA, as they are long-term stable structures, encoded in a partly digital form, that will endure all the flux and variability of the biochemistry within each neuron, over a lifetime.  It is these unique synaptic and epigenetic networks that we must preserve, scan, and upload in creating neural emulations, as we will discuss. Long-term memory formation happens best when we are in slow wave (deep and dreamless) sleep, which we get in cycles during the night (and especially well if our sleeping room is dark and quiet) and also during a good nap (a great way to “lock in” what you’ve learned, after a demanding learning period that will naturally make you sleepy).

Neural dendrites, cell body,  action potential, and synapses. Image: Gallant’s Biology.

All our neurons work in circuits, and strengthen or weaken their connections based on chemical and electrical activity, in a process called Hebbian learning. Just like your muscles, which come in two sets that oppose each other around every joint, neural circuits are both excitatory and inhibitory at many decision points in the network. Perhaps most important decision points are the cell bodies of each neuron, where the nucleus is. The electrochemical current from all the dendrites (“roots”) of each neuron flows toward its cell body, and action potentials (current waves) flow from the cell body to its synapses (“branches”), along the axon (“trunk”) of each neuron. Glutamate is the main neurotransmitter we use to send excitatory current from a synapse to the dendrite of the next neuron in a circuit (the postsynaptic neuron). Glutaminergic synapses are thus called “positive” in sign, and they promote electrical activity throughout the brain. GABA is the main neurotransmitter we use to let inhibitory current leak out of a postsynaptic dendrite. GABAergic synapses are thus called “negative” in sign, and they depress circuits throughout the brain.

Each neuron sums the net result of the positive and negative inputs it receives from its dendrites, over milliseconds to seconds. If the current exceeds that neuron’s threshold,  it sends an action potential (depolarizing electrochemical signal) to all its synapses. As the brain learns, our synapses enlarge or shrink, giving them greater or lesser excitatory or inhibitory effect, and we may grow more or lose our synapses. With few exceptions, each neuron also uses just one type of neurotransmitter (eg., glutamate or GABA), or the same small set of neurotransmitters, at all its synapses.

The architecture of memory, thought, emotion, and consciousness may thus be reducible to a surprisingly simple set of algorithms, connections, weights, signaling molecules and electrical features in each neuron, working together in a massively parallel way to create computational networks that are far more complex than the individual parts.

Hippocampus and frontal lobes. Image: NIH

In higher animals, the neurons in our hippocampi (two c-shaped areas of ancient, primitive, three-layer cortex in each hemisphere of our brain), and the connections they make to the rest of our cerebral cortex (especially to our frontal cortex), store all kinds of episodic (experiential) and declarative (fact-based) information, all from our last few days of life. At the same time, neurons in our cerebellum (a more primitive, “little brain” at the base of our skull) store procedural learning and memory (how to move our bodies in space). Experiments with rats and primates tell us that each hippocampus makes perhaps tens of thousands of new neurons every day, from neural stem cells. Other than for repair after certain kinds of injury, no other part of the adult brain is able to use stem cells in detectable numbers, as far as we know. The rest of our brain is postmitotic (unable to use cell division to maintain its structure), as neuroscientists demonstrated in an elegant experiment in 2006. Our neurons must be maintained by our immune and repair systems, and as they die via natural aging, or kill themselves in apoptosis, memories start to die.

Hippocampal dendritic spines. Image: Fiala & Harris, 2000.

Our hippocampal neurons have the very tough job of temporarily holding, in their uniquely dense synapses, and via their connections to the rest of the cortex, much of the new information we have learned over the last day or two, during our entire adult life. Here is a picture of a computer reconstruction of a small section of ten columns of synapse-rich “spiny dendrites”, from the CA1 (input) region of the hippocampus. CA1 contains areas like place cells, imprinted genetically with detailed maps of 3D space. Like the digestive cells lining our gut, and the skin cells at our fingertips, certain hippocampal neurons appear to get worn out on a regular basis by this demanding short-term memory holding function, and so some neuroscientists think new ones must regularly grow and mature to replace them.

People whose hippocampi are both surgically removed, like the memory disorder patient Henry Moliason, who had this done at the age of 27, can’t update their long-term episodic and declarative memories. H.M.’s long-term memory and personality was mostly “frozen” at 27. He could occasionally add bits of new information to long-term memories of the same type he’d built before the surgery, and he could learn new procedural (spatial and muscle) memories in his cerebellum, but he had no cerebral knowledge that he’d added these memories. H.M.’s amazing life suggests that if the brain preservation process damaged our hippocampi, but not the rest of our brain, we’d come back without our most recent experiences (two-day amnesia), but all our older memories and personality would still be intact.  Ted Berger at USC managed to build a simple version of an artificial electronic hippocampus for mice in 2005, so there’s a good reason to believe that this part of our brain, though important, isn’t irreplaceable. As long as you could install an artificial hippocampus in the computer emulation constructed from your scanned brain, you’d be back in business as a learning organism, with only some of your more recent memories and learning erased. This all helps us understand that what cognitive scientist Daniel Dennett would call our center of narrative gravity, our most unique self, is our long-term memory.

The fact that only special areas of our hippocampus can add new cells during life exposes a harsh reality about our biological brains. We are all born with a very large but fixed long-term memory capacity, and this capacity gets increasingly used up, pruned and potentiated, the older we get. Anyone over 40, like myself, knows they are considerably less flexible at learning new things than they were at 20. It’s far easier for older people to add more twigs to branches of knowledge we’ve previously built in our “tree of experience” than to form new branches. We can do it, but gets progressively tougher and slower the older we get.

This means, if we want to be lifelong learners in a world of accelerating technological and job change, it is critical to get an early education that is as categorically complete (global, cosmopolitan, and scientific), moral (socially good, positive sum) and evidence-based as possible. Our children need the best mental scaffolds they can get early on, or they’ll spend the rest of their lives trying to prune away harmful and untrue thoughts and beliefs acquired in their youth. Psychologists have long known that it is much easier to add increasing specificity to a neural network than it is to unlearn (depress) any branch, once it’s built. We need to be careful about what we allow into our memory palaces.

That said, children also benefit greatly from freedom, early on in life, to study what they themselves desire to learn, and to have a good degree of control over learning outcomes and style. This freedom, and appropriate rewards for effort of any kind, induce them to build intricate mental specializations in areas they are personally passionate about. For those who want to know how to implement a 50/50 balance of broad, state-mandated learning in future-critical STEM fields, analytical thinking, and civics (the “hilt of the sword”, basic protective world knowledge), and a personalized program of student-directed specialized learning, creativity, and play in the other half of the time, mastering whatever they can convince their teachers is worth studying (the “blade of the sword”, passionate specialization allowing them to cut their own unique path of new knowledge and value in the world), I strongly recommend The Finland Phenomenon, 2010 . This exceptional film, and to a lesser extent Tony Wagner’s book Creating Innovators, 2012, demonstrate key elements of the future of learning for enlightened societies, in my opinion. It may take 20 years for the evidence to be incontrovertible, but you can give it to your child now, if you find it appealing. The US will eventually realize that if the Finns did it, rejuvenating their previously failing education system over a twenty year period, we can too.

Cybertwin – Virtual Assistants With Simple Models of Our Interests Will Be Useful for Many of Us By the Early 2020′s. Image: MyCyberTwin.com

It is also liberating to realize that while our biological brains are less able to learn fundamentally new things as they age, all the digital technologies we use, technologies which will bring our emulations back at an affordable price later this century, will continue to get exponentially more powerful every year. Most of us don’t realize this, but everyone who uses a social network, email, or any other technology to capture things they say, see, and write about is also creating a digital simulation of themselves. By 2020 we’ll all be talking to and with our best search engines in complex sentences (the conversational interface), and shortly thereafter, we’ll all be able to use simple software agents, Cybertwins, or “Twins,” which will have crude maps of our interests and personality, so they can serve us better. Computational linguists know that if you capture what a person says for just two years, we are so repetitive about what we care about that a cybertwin could whisper into our ear the word that natural language processing algorithms predict we want when we are having a senior moment, and they’ll be right most of the time. That’s how repetitive we are, and how good web search will be by 2020. As I wrote in 2005, people who don’t run cybertwins will be much less productive, so they’ll be very popular, even though they’ll bring lots of new social problems in their first generation.

Now here’s the kicker: These simulations won’t be turned off by our loved ones when we die. Our children, friends, and colleagues will use them to interact conversationally, and only in appropriate contexts, with these semantic simulations of us, to keep the best of our thoughts, experiences and personalities accessible to them when desired. Once folks realize that their Twins really are a “digital immortalization” of parts of themselves, and once neuroscience has proven – in the next ten to twenty years perhaps – that we can read (“upload”) particular memories from preserved and scanned animal brains, even very primitive ones, at that point preserving one’s brain for later uploading into a Twin, to improve the quality of the simulation at least, and perhaps even to come back in a self-aware state, at best, will be an increasingly obvious and responsible choice for dying individuals, especially if the cost to do so is quite affordable. What’s more, recent advances in molecular scale MRI scanning strongly suggest that future scanning technologies should be able to nondestructively scan entire preserved brains, to upload their molecular states, memories, and higher functions into a Twin. So if the first scan isn’t perfect, it can always be updated later, from the preserved, “immortal” brain.

Given all this, we can see that teaching our children and ourselves to be digital natives and digital activists, to use the social web and the first affordable commercial lifelogs, like Google glass, when they arrive, is an important way for us to build an ever more capable cybertwin for ourselves and for our loved ones when we die (and ideally, are preserved), even as our biological self naturally slows down and simplifies (prunes away branches of knowledge and memories we once had ready access to) with advancing age.

Now we arrive at our truest self, the part we care most about preserving and sharing with our loved ones and society. It is this self that I expect will later merge with the Twin that many of us will leave behind in the 2020′s, as strange as that might sound today.

Experience-based learning. Image: Graham Paterson, Children’s Hospital Boston

3. Long-term Molecular Changes (Long-term Learning – “Nucleus and Synapse II”)

The production of long-term memory, personality, and identity requires all the short-term synaptic changes above, plus permanent molecular changes in the neuron’s Nucleus (DNA and its histones, or wrapping proteins), and the permanent creation of new cellular proteins, synapses, and circuits (Synapse II). Here’s a brief summary of our understanding of the process[4]:

Nucleus (“Genome, Transcriptome, and Epigenome”)
1. Retrograde transport and signaling from the synapse to the nucleus
2. Activation of nuclear transcription factors and induction of gene expression
3. Chromatin alteration and epigenetic changes in gene expression (gene-protein networks)
Synapse II (“Connectome and Synaptome”)
4. Synaptic capture of new gene products, local protein synthesis, and seeding of new synaptic sites
5. Permanent synaptic changes, activation of preexisting silent synapses, formation of new synapses.

We used several “-ome” words above. Let us briefly consider each. They are very roughly ordered below in terms of their likely contribution to our unique self, from least to most important:

The Genome. These are inherited genes and gene regulatory networks that control instinctual behaviors. Our genome includes the unique alleles we received from our parents. It is easy to preserve, as it is the same in all cells. With one tissue sample we can create a clone later, either physically, or far more likely, in a computer simulation. But this clone has only our inherited uniqueness. We’ll need contributions from the next four “omes” to add our life memories and learning to the emulation.

The Transcriptome. This is the set of proteins made (transcribed) by cells. While proteomics (another “ome” word) is in its infancy, scientists estimate each of our cells has the DNA to express ~20,000 basic protein types. Each type can be further modified after creation by adding or removing chemical tags like phosphate, methyl, ubiquitin, and other small molecules, so that more than a million protein subtypes may exist in a typical human body. Fortunately, each of our ~220 cell types only uses around 5,000 of these 20,000, and perhaps less than 2,000 of the 5,000 are unique to each cell type. Neurons and glia, the cell types we are most interested in, may use just a few hundred protein types to store our higher learning and memory in the nucleus and synapses. The other proteins are there to keep all of our cells alive, which is a critical precondition to being able to store long-term memories in a special subset of neural structures. All this suggests the proteomics of memory and identity, and of later memory and identity reconstruction from scanned brains, are not impossibly complex, but rather highly challenging, fascinating and eventually solvable problems.

The Epigenome. These are learning-based changes in gene-protein networks that happen in the nucleus of each neuron, mostly during the life of the organism. The Dutch famine of 1944 and the Överkalix study in Sweden tell us that some epigenetic changes can be inherited in humans, so we all should seek good nutrition and avoid toxin exposure, as we may pass some of that to our children in the form of compromised and undermethylated epigenomes. But there is a lot more to the epigenome story still to be uncovered, as this 2011 article on epigenetic regulation in learning and memory in Drosophila makes clear. Our epigenome is a gene-regulatory layer that involves chemical changes, mostly methylation, to DNA and to the histone proteins that wrap and expose DNA in the cell nucleus. These changes determine how DNA, RNA, and protein are expressed in the nucleus, and thus may affect, at least to some degree, how neurons uniquely manage their synapses as they grow and learn.

The Connectome. This is a map of our neural cell types, and how they connect. Our connectomes and much of our dendrite structure is very similar in all of us. This shared developmental structure makes it easy for us to communicate as collectives, for ideas or “memes” to jump from brain to brain. Yet with 100 billion neurons making an average of 1,000 connections to other neurons, and most of these not being developmentally controlled, we’ve got the ability to make 100 trillion connections, the large majority of which will be unique to each individual.

The Synaptome. These are key features of the ~1,000 synapses that each neuron makes to others. They are the particular long-term molecular features that determine the strength and type of each synapse, its signaling states and electrical properties, as we’ve described them above. The synaptome is the weight and type of the 100 trillion connections described above, and this information may be the most important “recording” of our unique self. Fortunately, because memories are stored in a highly redundant, distributed, and associative manner in our synaptic connections, our synaptome is to some degree fault tolerant to cell death. Both artificial and biological neural networks experience graceful degradation (partial recall, incremental death) of higher memories as individual neurons die. We also know the molecular code of long term memory is fault tolerant to the noise, deformations, and chaos of wet biology. The feedback loops between the electrical and gene-protein network subsystems interact somehow to stabilize long term memories in a special subset of durable molecular changes, in spite of all the other biochemistry furiously going on to keep the cell alive.

Single-celled animal. Image: Anthony Horth

I am sure the distinguished futurist and technologist Ray Kurzweil will have a lot more to say about these topics in his next book, How to Create a Mind, which comes out next month. You can preorder a copy here. To understand how these subsystems interact in a living organism, let’s start in as simple a model organism as we can find, single-celled animals, organisms that don’t even have nervous systems as we know them. Wetware, Dennis Bray, 2009 is a great tour of these animals. Single-celled eukaryotes like Stentor, Paramecium, and Amoeba do complex information processing, and hold short-term memories in their chemical networks. In 2008, we learned that Amoeba remember and anticipate cold shocks, for example. These networks include the cell’s genome, epigenome, cellular proteins, cytoskeleton, receptors, and cell membrane. They are true computational networks, with both neural-network like and Boolean logic properties. Genes and proteins integrate signals from other genes and proteins, and selectively switch and transmit signals, just like neurons do. The genes in each cell, via RNA, determine which proteins are made, when and where. Most protein changes are part of the short term computation being done in a cell, but a special few will lead to lasting changes in the epigenome and the cytoskeleton and receptors in and on the surface of the cell. These long-term changes are the ones we care most about, as they store the cell’s unique memory and identity.

Until computational neuroscience[5] can predictively model how the gene-protein networks in a Paramecium allows these animals to evaluate options, assign priorities, regulate their moment-by-moment computational attention, continually vary strategies for chasing prey and avoiding toxins, and chemically store their representations, habituations, and memories in an intracellular environment, all within a single cell that has no proper nervous system, the field will be missing its Rosetta Stone. Electrical waves exist in these single-celled animals, but with the exception of mitochondrial energy production, they are of the most primitive, diffusion-based kind. All the considerable intelligence in these animals is coursing, moment by moment, through their gene-protein networks.

In multicellular organisms with neurons, the cytoskeleton and receptors have specialized into the synaptome, the pre-and post-synaptic molecular modification of our synapses, including phosphorylation of switching proteins like calmodulin kinase II. While there are over 50 known neuromodulators and 14 neurotransmitters in our brain, only six neurotransmitters have been regularly implicated in long term learning and memory in our synaptome. It is these and their partner molecules in the synapse and nucleus that are probably most important to understand and model to crack the long-term memory code.

C. elegans connectome. Image: OpenWorm.org

Fortunately, even with our very partial molecular and functional maps today we have still managed to work out some basics of neural network interaction in very small neural ensembles, like the somatogastric nervous system (~30 neurons) in lobsters. We’ve even created early maps of very small whole-animal neural systems, like the nematode worm C. elegans, with its 302 neurons and ~6,000 synapses. We mapped the C. elegans connectome in 1986, but we still know just pieces of its synaptome and transcriptome, and even less about its epigenome. Fabio Piano et. al. give us an overview of the state of C. elegans gene-protein network knowledge in 2006. Note their subtitle is “A Beginning.” Jeff Kaufman has recently summarized the very early status today of whole brain emulation in nematodes. David Dalrymple in Ed Boyden’s lab at MIT is working on C. elegans simulation, and he is optimistic about new tools in neural state recording, optogenetics, and viral tagging for characterizing each neuron’s function. As Derya Unmatz reports in a blog post that sounds like science fiction,  Sharad Ramanathan et. al. at Harvard can now take control of C. elegans locomotion by firing precisely targeted lasers at individual neurons in an optogenetically modified worm’s brain, controlling its chemotactic behavior and convincing it that food is nearby.

A small international collaboration exists to emulate the C. elegans nervous system, called OpenWorm. There’s even a Whole (Human) Brain Emulation Roadmap, started in 2007 by Anders Sandberg and Nick Bostrom at Oxford, and a few other visionary folks in biology, computer science, and philosophy. These important projects are quite early and extremely underfunded at present. The biggest problem today is getting more funded people working on them.

To emulate how C. elegans, Drosophila, Aplysia, Danio, Mus, and other neural networks actually work, and to begin to extract even crude and partial memories from the scanned brains of any of these and other model organisms, we’ll need a better understanding of behavioral plasticity, and the way the synapse, the nucleus, and neuromodulators bias the pattern generators in neural circuits into a particular set of behavioral patterns. This may require not only better neural circuit maps, but better maps of several still partly-hidden intracellular systems involved in long-term memory formation: gene regulatory networks, the transcriptome, and the epigenome[6]. There are gene-protein networks controlling human neural development, neural evolution, and our long-term learning and memory. A special few of these regulatory networks, their proteins, and the epigenomic changes these networks store during a lifetime of human learning may be as important as the synapse, if not more, in determining how our brain encodes and stores useful information about the world.

A great textbook on gene regulatory networks is The Regulatory Genome: Gene Regulatory Networks in Development and Evolution, Eric Davidson, 2006. It will amaze you how much Davidson’s group has learned about these networks, primarily by studying the evolutionary development of one simple organism, the sea-urchin, over several decades. Last month, Isabelle Peter and others in Davidson’s group at Caltech published the first highly predictive model of how these networks control all the steps in sea urchin embryo development over the first 30 hours of its life. 50 genes are involved, and their regulatory interactions can be fully described in Boolean logic. Now they want to model all of development, and some of the networks controlling its variational processes. Consider the magnitude of their achievement: Davidson et. al. have reduced an incredibly complex biochemical process down to a far simpler algorithm. This is what must happen in long-term memory, if we are to use scanned brains to abstract the key subsets of molecular structures that reliably encode it in our neurons.

Protein Microarrays – An Exciting New Tool. Image:  Eye-Research.org

Neural proteomics and the transcriptome are entering an exciting new phase as we use DNA and RNA microarrays, and now protein microarrays to catalog neural transcriptomes and compare them to other types of human cells, and to other primate and mammal neurons. In August, Genevieve Konopka and colleagues published an exciting paper comparing human, chimpanzee, and rhesus monkey neural transcriptomes. We’re finding genes and proteins unique to particular areas in human brains, especially our frontal lobes. We’re building our first maps of the critical differences in the gene and protein regulatory networks that allowed us to wake up, make tools, and walk out of Africa less than two million years ago.

Epigenome (methylated DNA and modified histones). Image: RoadmapEpigenomics.org

We recently learned that what was long called “Junk” DNA, the 98% of each cell’s non-exonic DNA (DNA that doesn’t code directly for proteins), participates at various levels in gene regulatory networks, and through epigenomics these networks can change to some degree over the life of the cell. We’re learning now to map gene-protein interactions in these networks, including epigenomic changes, using tools like Chromatin ImmunoPrecipitation and sequencing (ChIP-seq). Unfortunately, this work is also seriously underfunded. We’ve known about the importance of the epigenome for over a decade. Epigenomic changes can be inherited (watch what you do with your body, as your kids will inherit a record of some of your bad or good life habits in their epigenome), and thus record unique learning in each cell over its lifetime, in ways we are still uncovering.

The NIH started a Roadmap Epigenomics Project for mapping the human epigenome in 2008, but the funding is a pittance, roughly $40 million a year. There is also a global collaborative research database, ENCODE, for sharing what is presently known about all the functional elements in the human genome. We give it roughly $20M/year, barely life support. There are also various Human Proteome Projects under way, but no one seems to be funding any of these seriously, either. None of the politicians or key philanthropists who could make the Human Proteome and Epigenome into national research priorities have proposed any big initiatives, as far as I know. Even our science documentaries don’t adequately convey the promise of these fields. The scientific community is tooling along as best it can in spite of the fact that the public still hasn’t gotten the clue on how much better medicine would be in ten years if we were spending a whole lot more money on this right now.

Recall by contrast the Human Genome Project, which began with fanfare in 1990 and was rough draft completed in 2000, for $3 billion, a price gladly paid by the U.S. and four other motivated nations. The Human Genome Project was, to put it in proper perspective, our planet’s Moon Shot in the 1990’s, our species latest great leap into “inner space.” As those who’ve read my Race to Inner Space post know, I think understanding the machinery of life and intelligence, and nanotechnology in general, is a destination far, far more valuable to us than outer and human scale (as opposed to cell and molecule-scale) space. We need an international Human Proteome and Epigenome Project race. With good funding and leadership, we might nail our first good maps of the neural gene-protein interaction layer in a decade. With business-as-usual, it will likely take much longer.

As we learn the languages of gene regulatory networks, the transcriptome, and the epigenome in coming years, we should learn how to influence these networks in many powerful ways. Do you think the trillion dollar global pharmaceutical industry is big now? Wait for the therapeutics that may start to arrive in the late 2020s, as we begin to learn how to intervene in these networks. I think it is only when we have good maps of these gene-protein networks that we can finally expect medical advances like better learning and memory formation, elimination of a vast range of diseases including cancer and Alzheimer’s, immune system boosting, aging reduction (epigenomics repair), and perhaps even the uncovering of genetically latent skills like tissue regeneration and hibernation. We are not talking about gene modification (inserting new genes in the germline, or in an adult), but rather about improving dysfunctional gene network regulation, and learning how to assay and minimize important parts of the network dysregulation that goes wrong in each of us as we get older and get various diseases.

Ken Hayworth

There’s a nice analogy here, pointed out by my Brain Preservation Foundation co-founder, Ken Hayworth. The Human Genome Project gave the world affordable gene sequencing in the mid-2000’s, and ten years later, we are beginning to see the major fruits: the uncovering the previously hidden worlds of gene regulation networks, the transcriptome, and the epigenome. Likewise, the Human Connectome Project and the still-unfunded Human Proteome and Epigenome Projects could get us affordable neural circuit tracing and functional gene regulatory network modeling in the late 2010s. Just as the Human Genome Project showed us we had a lot fewer genes than we thought (~21,000 rather than 100,000) the Human Epigenome Project may tell us that our gene regulatory networks are functionally simpler than we currently think, and that of the ~5,000 proteins in a typical cell, there are just a handful that matter to our long-term self. With luck, the remaining hidden layers of the neural transcriptome and epigenome will be functionally understood in the late 2020s. In that exciting time, our ability to understand memory and learning, to read memories from the scanned brains of model organisms, and to build biologically-inspired computer models, will all be greatly enhanced.

So to answer our original question, we need to find out if both chemical preservation and cryopreservation will preserve the connectome, the synaptome, and any long-term memory-related changes in the epigenome in a living brain.

Our Brain Preservation Technology Prize, which focuses on the connectome and many but not all features of the synaptome, is an important start down this road. As we understand better what molecular features in the synaptome and epigenome need to be preserved to capture and later retrieve memories, we’ll also need to find out if either chemical or cryopreservation, or ideally both, will reliably preserve those structures at the end of our biological lives, and whether it will be possible for future scanning algorithms to repair any damage done by the preservation process. We’re too early to answer such questions today, but it is encouraging to remember that long-term memory is a very redundant, resilient and distributed system.  Extensive neural destruction can occur in brains via Alzheimer’s, stroke, and other diseases before our memories are substantially erased and cognitive reserve is no longer available.

Sixty years of histology practice tells us that good perfusion of special chemical fixatives such formaldehyde and glutaraldehyde at death will immediately preserve everything we can see by electron microscopy in neurons. A great book on how this works is John Kiernan’s Histological and Histochemical Methods: Theory and Practice, 4th Ed., 2008. Kiernan has been publishing since 1964, and is a leader in the theory and practice of chemical fixation. There are even a few published fixation methods for whole mice brains. Here’s a 2005 paper by Kenneth Eichenbaum et.al. demonstrating a whole brain fixation technique that claims “complete preservation of cellular ultrastructure”, “artifact-free brain fixation” and “no signs of cellular necrosis” in an entire mouse brain. Presumably these methods also protect DNA methylation and histone modification in the epigenome, the phosphorylation of dendritic proteins like CamKII, the anchoring of AMPA receptors in the synapse, and any other elements of long-term memory formation. Presumably these molecules are protected today for years just by aldehyde fixation, if kept at low temperature (4 degrees).  Companies like Biomatrica have even developed ways to store human and bacterial DNA and RNA at room temperature for years. Long term storage of whole brain connectomes, synaptomes and epigenomes at room temperature, an ideal outcome for simplicity and affordability, may work today via additional chemical fixation steps like osmium tetroxide, a process that crosslinks fats and cell membranes, and plastination, a process that draws all the water out of a preserved brain and replaces it with resin.

But all this remains to be proven. If you know of experts who have done work in this area who would be willing to help BPF write position papers on these topics, and who can envision research projects that will answer these questions more definitively, please let me know, in the comments or by email at johnsmart{at}gmail{dot}com. Thanks.

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Footnotes:

1. There is a much older layer of unique learning in each of us that is also important, the intelligent behaviors that gene networks have recorded in each of us over evolutionary time, as instinctual programs, and the unique assortment and variants of genes we each received at birth. Such networks determine our inherited neural programs, instincts and behaviors that are executed mostly unthinkingly and robustly, and during which other forms of learning, like short-term learning, often does not even occur. To preserve this layer we just need a DNA sample of the preserved person, and that particular uniqueness can be incorporated in any future emulation, assuming future computers are up to the task.

2. Some scientists working on brain emulation, like BPF Advisor Randal Koene, suspect that measuring and modeling the brain’s electrical processes, a topic called Computational Neurophysiology, will give us powerful new insights into artificial intelligence. There are new tools emerging for in situ functional recording of electrical features of the neuron. These may be critical to establish the “reference class” of normal electrical responses, for each type of neuron and neural architecture, the class of electrical representations of information. But if the model I’ve presented here is correct, we won’t need to record any electrical features of individual brains in order to successfully reanimate them later. We’ll see.

3. In Aplysia (sea slug), the sensory neuron neurotransmitter serotonin (5-HT) binds to postsynaptic receptors, activates adenylyl cyclase (AC) in the cell to make the second messenger cAMP, causing a short-term facilitation (STF) in strength of the sensory to motor neuron connection. More of the excitatory neurotransmitter glutamate is released by the neuron to its follower motor cells, and Aplysia pulls away harder from its shock. The neuron is also sensitized: K+ channels are depressed, more Ca++ enters the presynaptic terminal, and the action potential spike broadens. Kinases and phosphatases (phosphate adding and removing enzymes) including cAMP-dependent PK, PKA, PKC, and CamKII control duration and strength of these changes. In facilitation, the spike broadens temporarily, as both pre- and post-synaptic Ca++ and CamKII make molecular changes that temporarily strengthen the electrical signal across the synapse. In short-term depression (STD), the same mechanism temporarily weakens the signal. If water is gently shot at Aplysia’s gills ten times in a row, it temporarily learns not withdraw them, via synaptic depression of motor circuits. This short-term memory lasts for ten minutes, and involves a short-term reduction in the number of glutamate vesicles that are docked at presynaptic release sites in sensory neurons (undocked vesicles can’t be immediately used). Repeat this training four times and the slug will turn this into an intermediate-term memory, making chemical and electrical changes in the synapse that now last for three weeks. Again, all this involves changes only to preexisting proteins and synaptic connections in neurons.

4. In rat and human hippocampus, the primary excitatory neurotransmitter is glutamate. This causes Ca++ influx through NMDA receptors at postsynaptic membranes, and activation of CamKII, PKC, and MAPK. Permanent synaptic changes (Early LTP) include increased insertion of AMPA receptors in the membrane, and phosphorylation of proteins to change the properties of the channel. These receptors are anchored to the neural cytoskeleton, so they have reliable long term effects. Later LTP involves recruitment of pre- and postsynaptic molecules to create new synaptic sites. A few key gene-regulatory networks are involved, with transcriptional and translational control at both the nucleus and the synapse, and control molecules including BDNF, mTOR, CREB, and CPEB. We’ve recently found a memory encoding master control gene, Npas4, that encodes nuclear transcription factors (the copying of other genes into messenger RNA) which interact with hippocampal neurons to encode episodic memory. When Npas4 is knocked out of mice, they can’t learn. We’ve found RNA binding proteins like Orb2, that bind to genes involved in long-term memory. A great and reasonably current text on the molecular basis of memory and learning is Mechanisms of Memory, David Sweatt, 2009. We’re still figuring out the epigenomic regulation that occurs in long-term learning and memory, so you’ll need to go to journals for most of that story, like this 2011 PloS Biology paper on epigenetic regulation of learning and memory in Drosophila. The full size of the memory puzzle is becoming clearer every day. Now we just need to fund the work to complete it. We sure could use this knowledge in all kinds of good ways today, if we had it. Here’s a cartoon of long-term memory formation in both Aplysia and rat hippocampus, from Learning and Memory, John Byrne (Ed.), 2008 (Vol 4., David Sweatt, p. 14):

5. Computational Neuroscience seeks to model brain function at multiple spatial-temporal scales. The brain uses a vast range of different schemes for representation and manipulation of information, and it passes some of this information from one system to another all the time. Consider the way neurons integrate signals from the receptors at their dendrites, the timing and shape of their action potentials, the way synapses interact with postsynaptic dendrites from other neurons, how neurons encode and store associative memory, specialize for perceiving and storing certain types of information (edge detection, grandmother cells), do inference and other calculations, work in functional subunits like cortical columns, and organize receptive fields. It all seems formidably complex, but useful simplifications exist, as we’ve described above.

6. Most folks in the neural emulation community don’t talk much about modeling gene regulatory networks or the epigenome and its interaction with the synaptome, and I think that’s their loss. Some focus only on easier stuff to see, like electrical features, and assume that might be enough to get a predictive model. But I think that’s like looking for your keys under the streetlights when they are in the shadows. If spikes, loops, and synchrony are a network layer that has grown on top of cell morphology and gene-protein networks, the way single-celled animals eventually grew neurons, we may learn surprisingly little by measuring and modeling electrical features. Attempting to do so may be like trying to infer the structure of hidden layers in a very large neural network [genome, epigenome, connectome, synaptome, and electrical features] by analyzing just the input/output layer, electrical features. We need all the hidden layers if we expect to have enough computational complexity to predictively characterize learning, memory, and behavior.