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

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

1. As I argue in this 2010 video, both chemical and cryogenic brain preservation technologies may soon be validated to inexpensively preserve the key features of our memories and identity at our biological death.

2. If either or both forms of brain preservation can be validated to reliably store retrievable and useful individual mental information, these medical procedures should be made available in all ethical societies as an option, for those who might want it, at biological death.

3. If neuroscience, biologically-inspired computer science, microscopy, scanning, and robotics keep improving as they have so far, preserved human memories and identity may be affordably reanimated by being “uploaded” into computer simulations, even before the end of this century. Such “uploading” is being attempted for specific memories in animals today, and new tools like optogenetics and expansion microscopy cause leading neuroscientists to expect they will soon decipher and be able to manipulate (removing and introducing new memories) the neural code.

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 may 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.

[2018 Update: For the latest on the neural code, the physical storage of memories in the brain, see this excellent paper by Carillo-Reid et al. (2018). It show how we are now imaging and optically manipulating neural ensembles, small spatiotemporally connected networks of neurons that link to other ensembles and are the building blocks of neural circuits. Neuroscientists believe these ensembles and circuits store engrams (memories and models) in the brain. With new optogenetics tools, we can find and manipulate these ensembles, and possibly create new ones, which could then be recalled by selectively stimulating any individual cell in the ensemble. This would be a true demonstration of memory creation, and major advance in deciphering the neural code.

More hints that neural synchrony in the claustrum is the root source of consciousness came in 2014, when a neurologist at GW School of Medicine, Mohamad Koubeissi, managed to turn off and on consciousness in a single patient by electrically stimulating her claustrum with deep brain electrodes. Electrical stimulation of brains has a long history in neurology and neuroscience, but it had never resulted in consciousness control, until now. Then in 2017, an fMRI study in awake and anesthetized rodents showed strong interhemispheric functional connectivity, via the claustrum, to both the prefrontal cortex and the thalamus in the awake state, dynamic connectivity that was lost in the anesthetic state. This was the first study to identify specific neural substrates by which the claustrum may “orchestrate” (to use Francis Crick’s term) consciousness. This work is a major vindication of Crick and Koch’s 2004 hypothesis that the claustrum is the central creator of consciousness. We are truly on the edge of understanding it as a fully mechanistic process. Understanding consciousness mechanistically will allow us to ask how we might go about instantiating it into nonbiological intelligence systems, perhaps even some time this century.

There are also some speculative hints, in 2018, of a possible epigenetic “backup” storage of memory. Some very old experiments by James McConnell in 1959, recently redone by Michael Levin at Tufts and David Glanzman at UCLA, tell us our memories may not only exist in our synapses and dendrites, they may also be stored, to some degree, epigenetically in our neural nuclei, via modification to the histone proteins that wrap DNA and regulate its folding. Having such a digital memory backup system would be clever, if brains and evolution figured out how to do that. A readable “backup” could could be used to reestablish the right connections if they are disrupted, and would our memories particularly robust to biophysical stresses that deform the analog morphology of neural networks in a living, moving brain. If some degree of epigenetic memory backup (to neural nets) exists and is extensive, it might mean a lot of a person’s memory could be retrieved and uploaded in the future, even if even if a person was preserved using poor methods (like most cryonics patients today), or preserved many hours after death (as their neural morphology starts to decompose). More research is going to be needed to determine if such a system exists, and if so, how many hours it might survive after death.]

For an excellent primer on the latest neuroscience theories and computer models in how the brain works, read the free wiki book, Computational Cognitive Neuroscience, O’Reilly and Munakata (2012). Chapter 8, Learning and Memory, is excellent. To understand how and why “you” might survive being uploaded into a computer or robotic body, if that was easiest way to bring you back to the world in the future, see BPF Advisor Mark Walker’s article, “Personal Identity and Mind Uploading,” JET, 22:37-51, 2012.

In this post, I’d like to give you my best provisional answer to 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- & 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, as well as temporary chnages to the neural epigenome (DNA transcription and inhibition machinery, an overlay on the human genome).

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, synaptome, and nucleus (epigenome), by permanent molecular changes to neural DNA, the synthesis of new neural proteins and receptors in existing synapses, and the creation of new synapses.

All three of these brain processes involve a still-imperfectly understood combination of both digital (on or off, discrete states, including epigenetic DNA states and neural firing states) and analog (continuous, spatially related, wavelike) information and computational processes, which integrate in a statistical, associational, competitive, and massively parallel manner. At present, it is a reasonable assumption that only the third layer listed above, where long-term durable molecular changes occur, must be preserved for later memory and identity reanimation.

The following overview of each of these three key information processing 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, 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 is my favorite current theory (among several currently in competition) for the medium that creates our conscious perceptions. 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 though very technical book about neural spikes, loops, and synchrony is Rhythms of the BrainGyorgy 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. There are some big mysteries still left to uncover regarding synchrony. 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 both modes of communication and synchrony than previously thought.

My late mentor at UCSD, Francis Crick, and his brilliant 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. The region Crick and Koch was most interested in was the claustrum, a thin sheet just above the putamen, near the center of the brain, found in all mammals, which  has many longitudinal tracts that connect to many areas of the cerebral cortex and to the thalamus, the central relay station in the brain. In 2005, the year after Crick’s death, Koch published their hypothesis that the claustrum is the central synchronizer of conscious experience.

These brief, rhythmic synchronizations share information between highly different, specialized 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 do have a good page on the claustrum. 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. 

In sum, though neuroscientists don’t yet all agree on many of 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” (most valued) and yet at the same time, our most volatile and least-unique self.

It is quite likely, in my view, that if we uploaded your brain into a computer, and then reestablished different consciousness network states than the ones that existed in your biology, at death, states that were an “average” of typical human networks, you would still wake up feeling like essentially the same “you”, and others would describe you as the same as well. Consider that we all have different kinds of consciousness on a daily basis.

In other words, the dynamic specifics of our consciousness networks (which we don’t yet deeply understand), as opposed to their structural connectivity with the rest of the brain, (which we are now identifying in areas like the claustrum, and know how to preserve) may be the least important contributors to our unique identity.

While for many of us, consciousness is the feature of our minds that we love the most, it appears its primary role is to be an “orchestra conductor” of much more carefully stored, and slower-changing “lower-level” layers of you. If you change orchestra conductors, you will still get a symphony that is beautiful, and largely the same.

Let’s look at those significantly more unique lower layers now.

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.]

Throughout our day, and particularly when we sleep, our short- and intermediate-term brain writes important parts of its experiences to our long-term memory (the subject of our next section), building durable new synaptic connections, so this learning can stay with us for years to life, in a process called memory consolidation. Memory consolidation seems to happen 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. That’s why a short nap after an intense learning session is considered a great way to “lock in” what you’ve learned.

When any of our short- or intermediate-term memories or thinking patterns are selected to be written into long-term memory, communication with the cell nucleus must 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]

Again, long-term memory formation 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 the new proteins, synapses, and circuits established in long-term memory 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, partly analog form, that will endure all the flux and variability of the biochemistry within each neuron, over a lifetime.  It is these unique long-term synaptic and epigenetic networks that we must preserve, scan, and upload in creating neural emulations, as we will discuss.

Now consider this key insight about short- and intermediate term (Synapse I) learning, which helps you appreciate it is not consciousness, and it is not your long-term memory, but something altogether different. Subjectively, we all know we can store both a far greater total number and a far greater temporal density of episodic (experiential) and declarative (factual) memories in our short-term memory, than we can in long-term memory. That’s what we do when we cram for a test, intensively read important information, engage in intense discussions, or pay for great experiences, as we have learned we can greatly alter our short term memory, and we know having a lot of relevant, motivating information “loaded” into our short term memory can be a great help to us as we work on managing both our long-term strategies and our daily tasks.

Most of this short-term information is not selected to be written into long-term memory, as far as both our subjective experience and neuroscience research knows today. It has a half-life of days, and it steadily decays after it is learned. But all that memory can still be highly useful in the moment. It plays a key role in regulating what we can think and do, on any particular day.

Given what we’ve seen so far, do you think our short-term (Synapse I) learning needs to be preserved for you to return to life as essentially the same person you were when you died? I don’t think so. Claiming it does seems to me to be an extreme claim.

Consider that if your NMDA receptor distributions aren’t recoverable from a future emulation, for example, you should only have lost the last couple days of your life experience prior to being preserved, in our present understanding of how these systems are most likely to have evolved and operate.

I would expect any good reanimation program could use any baseline (species average) version of such receptors and you’d wake up, as almost entirely the same “you”. Perhaps we’ll need another decade or two of neuroscience to definitively answer such questions, but we can already have good reasonable intuitions about them today.

Let’s look a bit closer at how neurons work to understand the amazing capacity of short-term memory in a bit more detail.

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. One of the most important decision points is 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. With few exceptions, neurons use just one type of neurotransmitter, or the same small set of neurotransmitters, at all their synapses.

Electrically, each neuron sums the net result of the positive and negative inputs it receives from all its dendrites, over milliseconds to seconds. As part of this summing process dendrites also make their own local and weak types of mini-action potentials, dendritic spikes, and we’ve recently learned they use these mini-spikes to do complex information processing prior to sending current to the cell body. This reminds us that computationalists still have a good ways to go before they can build neural network models sufficiently complex to honor reality. Then, if the current received at the cell body exceeds that neuron’s threshold,  it sends a traditional action potential (depolarizing electrochemical signal, or “spike”) to all its synapses. As the brain learns, our synapses enlarge or shrink, giving them greater or lesser excitatory or inhibitory effect, and we will either grow more or lose our synapses, depending on the value of the circuit.

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 may be the primary place where we temporarily hold, in the uniquely dense synapses of this evolutionarily older cortex, and via their connections to the rest of our evolutionarily newer cortex, much of the information we have learned over the last day or two, during our entire adult life.

At right 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 large but fixed memory capacity, both short-term and long-term, 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. That decreasing flexibility is simply a result of the physics of network formation in finite biological brains. We can still add new branches, and new connections, but it gets harder over time.

Now we arrive at our truest self, the part I will argue that 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 Digital Twin (to be discussed shortly) that many of us may leave behind for our loved ones after our deaths in the 2020’s and beyond, as strange as that concept 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]:

3A. 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)

3B. Synapse II (“Connectome and Synaptome”)
1. Synaptic capture of new gene products, local protein synthesis, and seeding of new synaptic sites
2. 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 groups, 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. Our epigenome is a gene-regulatory layer that involves chemical changes, mostly methylation and acetylation, 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 how neurons manage their synapses as they grow and learn. These are learning-based changes in gene-protein networks that occur during the life of the organism.  Epigenetic changes during biological development are responsible for the different transcription patterns that emerge as cells divide, turing them into the various cell and tissue types in our bodies. 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. 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.

 [2015, 2018 Updates: Dr. David Sweatt at U. Alabama, Birmingham has for the last few decades been one of the leaders in researching the epigenetics of memory. See this brief article by Sweatt (Exploring the Building Blocks of Memory, UAB’s The Mix, Oct 2015), for a recent update. Unfortunately, as a species and in our leading countries we spend a ridiculously small amount of money trying to uncover those building blocks, so progress has been far slower than it otherwise could have been. Also, read this great 2018 ScienceNews article, and see Wikipedia’s page on the epigenetics in learning and memory to track this ongoing story. Perhaps you’d like to become a researcher in this area yourself? You’d do us all a great service.

The human brain is the most complex piece of matter in the known universe. The question of how memory is stored is arguably the most interesting of all brain questions, and one whose answer will improve our civilization in countless ways. You’d think it would be among our top priorities, but unfortunately, it still isn’t. We are stumbling and dragging our feet into a better world, not running toward it. 

How much of the epigenome will we need to preserve to be able to recreate higher human memories in computers? That question isn’t yet clear. Epigenomic changes that direct permanent protein changes in the neural nucleus may very likely be a redundant form of memory storage.  I would currently bet that some (low?) level of epigenomic data, in concert with connectomes and synaptomes (discussed next), may be necessary to recreate our higher memories. We shall see, as they say.]

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. [2015: For more on synaptic diversity, a topic we still have not fully characterized, see synaptome researcher Dr. Stephen Smith’s excellent presentation, The Synaptome Meets the Connectome, YouTube, 2012. Understanding synaptic diversity is likely to be a key piece of the memory encoding puzzle. We finally have the research tools and a modicum of funding to investigate it, via such paths as the BRAIN initiative, which so far has been vastly underfunded and overhyped, and via much more effective yet relatively small independently-funded groups, like the excellent Allen Institute for Brain Science.]

Lifelong Learning, and Your Digital Twin 

Given all this, 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”, technical ability and broad 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), I strongly recommend The Finland Phenomenon, 2010 . This exceptional film (free on YouTube now too), along with Pasi Sahlberg’s Finnish Lessons: What Can the World Learn from Educational Change in Finland, 2011, and Tony Wagner’s Creating Innovators, 2012, all demonstrate key elements of the future of learning in 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.

Digital Twins – Virtual Assistants (Smart Agents) With Simple Models of Our Interests, Will Be Useful for Many of Us By the Early 2020’s. Image:

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.

Some time in the 2020s, I expect that many of us will be talking to and with our best search engines in complex sentences (the conversational interface), and will be using personalized smart agents, “Digital Twins” (hereafter, “Twins”), which will have crude maps of our interests and personality, so they can serve us better.

[2018 Note: I now call Digital Twins by a more generic name, Personal AIs (PAIs). see my Medium series on Personal AIs for more on this amazing, accelerating, and still-little-discussed aspect of our personal and social futures.

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 Twin 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 natural language understanding will be by 2020. As I wrote in 2005, people who don’t run Twins 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 a kicker: These simulations likely won’t be turned off by our loved ones when we die. It will cost little to keep them running all the time, watching what we’re doing, and compiling at-first-primitive “suggestions” for us, and our children, friends, and colleagues will occasionally use them to interact conversationally, and only in appropriate contexts, with these semantic simulations, to keep the best of our thoughts, experiences and personalities accessible to them when desired. Of course, many folks will be creeped out by this idea, but others will find it a way to reduce the great pain of losing our most loved individuals to biological death.

Once folks realize that their Twins really are a kind of “digital immortalization” of parts of themselves, and once neuroscience has proven that we can read (“upload”) simple memories from preserved and scanned animal brains, at that point preserving one’s brain for later uploading into a Twin may seem an increasingly obvious and responsible choice for dying individuals, especially if the cost to do so is quite affordable. It will eventually be covered by health care in our wealthiest societies.

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. So if the first scan isn’t perfect, it can always be updated later, from the preserved physical brain.

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 when they arrive, is one important way for us to build an ever more capable Twin for ourselves and for our loved ones (after we die), 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.

To Understand Intelligence and Learning – Start with Single-Celled Organisms

Single-celled animal. Image: Anthony Horth

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.

BPF Advisor Randal Koene likes to use the phrase “Substrate-Independent Minds” to talk about uploading. One big step to realizing how achievable uploading will likely be involves understanding the patternism hypothesis, and recognizing some of the ways nature has already built substrate-variant “minds” in complex organisms that arrived before brains. We’ve just discussed surprisingly smart neuron-independent “minds” in some single-celled organisms. Many species of plant also have very complex “thinking” abilities, all without the use of neurons.

Take a look at the Plant Intelligence entry on Wikipedia page for more. To understand the way nature builds intelligence from molecular biology on up, in an evo-devo manner, we will need to learn how gene-protein networks, the inheritable features of the cell, and the stable physical and chemical laws of the environment interact to store adaptive intelligence, and allow it undergo both evolutionary variation and developmental conservation and replication. All of this happened long before neurons.

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 implicated so far 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. Just as biochemistry is a small subset of all chemistry, learning and memory biochemistrry is a small subset of cellular chemistry. Finding that subset, and how it reliably works in the wet, chaotic, messy conditions of the brain, is the greatest goal of modern neuroscience. Those algorithms will increasingly be imported into machines as well.

C. elegans connectome. Image:

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. elegansDrosophilaAplysiaDanioMus, 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:

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:

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.

Biologists are tooling along as best they can while policymakers, media, and the public still have no idea how much better medicine would truly be in ten years if we were spending a lot more money on these Big Life Sciences projects 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 our species much longer to understand this critical aspect of ourselves.

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, a much better funded 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 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 other processes of both intermediate-term and 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.


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.

Chemical Brain Preservation: How to Live “Forever” – A Personal View

Here’s my 45 minute talk on Chemical Brain Preservation at World Future Society 2012. Given the progress we’ve seen in the relevant science and technologies it’s a topic I’m presently very optimistic about. I had a great audience with lots of questions at the end, but in the interest of brevity I’m just uploading the talk. Let me know your thoughts in the comments, thanks!

A number of neuroscientists, working today with simple model organisms, are investigating the hypothesis that chemical brain preservation may inexpensively preserve the organism’s memories and mental states after death. Chemically preserved brains can be stored at room temperature in cemeteries, contract storage, even private homes. Our 501c3 nonprofit organization, the Brain Preservation Foundation, is offering a $100,000 prize to the first scientific team to demonstrate that the entire synaptic connectivity (“connectome”) of mammalian brains can be perfectly preserved using either chemical preservation or more expensive cryopreservation techniques.

Such preserved brains may be “read” in the future, analogous to the way a computer hard drive is read today, so that either memories or the complete identities of the preserved individuals can be restored or “uploaded” in computer form. Chemical preservation techniques are already being used to scan and upload the connectomes of very small animal brains (C. elegans and OpenWorm, zebrafish, soon flies). Though these scans are not yet sufficiently complex to extract memories from the uploaded organisms, give them a little more time, we’re very close now to cracking long-term memory. We just need to know a bit more about this process at the protein/receptor/gene level:

Amazingly, if information technologies continue to improve at historical rates, a person whose brain is chemically preserved in 2020 might have their memories read or even fully return to the world in a computer form not centuries but just a few decades from now, while their children and loved ones are still alive. Given progress in electron microscopy and connectomics research to date, we can even forsee how this may be done as a fully automated and inexpensive process.

Today, only 1% of people in developed societies are interested in living beyond their biological death (see When I’m 164, David Ewing Duncan, 2012). With chemical brain preservation, this 1% may soon have a validated, low-cost method that will allow them to do just that. Once it becomes a real option, and recovery of simple memories has been demonstrated in model organisms, this 1% may grow larger as well.

I am particularly excited by chemical brain preservation’s ability to improve the social contract: what benefits we may reasonably expect from the universe and society when we choose to live a good and moral life. I believe that having the option of chemical brain preservation at death, if the science is validated, may help all our societies become significantly more science-, future-, progress-, preservation-, sustainability-, truth and justice-, and community-oriented in coming years.

Would you choose chemical brain preservation at death if it was widely available, validated, and inexpensive? If not, why not? Would you do it to donate your brain to science? Your memories to your children or others who might want them? Would you be willing to come back in person, if that turns out to be possible? If it is sufficiently inexpensive, would it be best to preserve your brain at death, and let future society decide if either your memories or your identity are “worth” reanimating? Please let me know what you think in the comments, thank you.

Review: When I’m 164: The New Science of Radical Life Extension

Those interested in Life Extension may enjoy this $3 TED eBook, When I’m 164: The New Science of Radical Life Extension, by David Ewing Duncan. [If you purchase on Amazon, consider taking delivery to your Kindle Cloud Reader, and reading it in your browser with the Kindle Chrome extension. The highlighting and notes features work decently there now.] Duncan notes that global life expectancy was just 31 in 1900, and has doubled to 65 today. In America, it’s near 80. In Monaco, it’s now 89. In Swaziland its still an inexcusable 32. When I’m 164 attempts to consider what the world would be like if our average lifespans moved a long way toward doubling yet again. It’s a well written and insightful read and I recommend it, but I also found a few areas where it fell short. I hope you agree and if not, please let me know in the comments.

1. Killing the Myth of Overpopulation

Any book on radical life extension should do its part to kill the persistent overpopulation myth we hear so commonly in our mass media. The longer this myth persists, the more shoddy thinking we’ll see in the public about the future. Human population growth was increasingly rapid until the early 1960′s, when it hit an inflection point, and the rate of growth has been saturating ever since. Population growth is now in a phase of rapid collapse. Folks love to argue about whether our accelerating population growth and industrialization up to 1960 were on balance good for our species and the planet. I think it was tremendously net good, and that having more potentially bright and hard working people on Earth was our most important planetary resource in the 20th century, as economist Julian Simon said so eloquently in Ultimate Resource in 1983 and again in Ultimate Resource 2 in 1998. I don’t believe having more human minds is the most important marginal resource today. But fortunately, neither does humanity. Population growth has been decelerating ever since 1962, and absolute numbers of people on the planet will soon go negative. It is the numbers and abilities of our machines that are now growing exponentially. It’s time we acknowledge these obvious trends, and move beyond the myth.

Duncan notes that the global percentage of kids 5 and under was 15% in the 1960′s, and is under 7% today, and still declining. This great piece of data comes early in the book, but he doesn’t go nearly far enough in exploring or explaining its implications. As demographer Hans Rosling reminds us in his “Peak Child” presentations, the absolute number of humans being born annually, 135 million, peaked in 1990 and has been roughly constant ever since, and will soon be declining. Furthermore, the fraction of our global population that is under 18 is now 27%, and this fraction will never go up again, only downward, every year forward. We are no longer a Planet of the Young, as we were when kids were an insurance policy against disease and misfortune. Birth rates are dropping precipitously almost everywhere, and our average lifespans creep up a bit every year as well.

There are 7 billion humans on the planet today, and some project we’ll reach 8 billion by 2025. Circa 2000, the most credible reports had us maxing out at 10 billion by or before 2100. The United Nations now projects 9 billion by 2050, and 10 billion by 2100, and a “high case” possibility of 14 billion by 2100. Unfortunately, I believe UN estimates are systematically biased to overstate the problem. More credible to me, but still probably overstated, are reports of us maxing out at 9 billion by 2050, then steadily declining thereafter, as deaths increasingly outstrip new births. I agree with Peter Diamandis and Steven Kotler in their excellent Abundance, 2012, that we should plan for the possibility of 1 billion more souls arriving in Asia and 1 billion in Africa between now and 2050, for a rough total of two billion more people, but I expect we’ll see even less growth than this.

Global population growth collapse is occurring not because of ecodisasters or food or other resource shortages, which are perennially oversold to us by the doomsayers, but because of a predictable shift in human values as social development proceeds. As Ron Inglehart and Chris Welzel have long documented, societies are gaining increasing personal freedoms and increasing evidence-based decisionmaking, which in turn is decreasing social fundamentalism and giving women many more desirable options besides childraising. If technological advances are the primary drivers of this values shift, as I believe they are, and as Inglehart argued in The Silent Revolution, 1977, it is then an obvious prediction that this values shift will continue.

Max Singer, one of the founders of the Hudson Institute, noted in “The Population Surprise,” in The Atlantic Online in 1999:

In only the past twenty years or so world fertility has dropped by 1.5 births per woman. Such a degree of change, were it to occur again, would be enough to turn a long-term increase in world population of one percent a year into a long-term decrease of one percent a year.

Given these trends, I would presently bet that “Peak Human” will arrive circa 2040 with perhaps 8.3 billion of us, a significantly lower number than most demographers now expect. In other words, our population future is most likely something close to the UN’s “low fertility” forecast in the chart below, with just six billion of us around at the end of the century, a billion less than our present state.

As we evaluate the likelihood of the low-fertility future, we must consider at least the following driving forces:

1. Society’s ever-increasing focus on sustainability as wealth grows,
2. The global web’s growing ability to quantify the negative environmental impacts of people,
3. The increasing social freedoms of women,
4. The decreasing marginal value of humans vs. machines to global productivity (see #2 below),
5. The inability to significantly extend average human lifespan before AI arrives (see #3 below),
6. Our likely growing lack of interest in remaining biological after AI arrives (see #4 below).

Consideration of these forces makes it very likely to me that global human population must shrink inexorably after we reach our peak. If true, our ascent and subsequent decline from the state of Peak Human should arrive a full generation before the technological singularity arrives, if the latter event happens circa 2060, as I would presently guess. To move beyond these guesses, we need more acceleration-aware (“accelaware”) forecasting, and more reference class forecasting to reduce the bias and deception in past population forecasts. Let’s hope this continues to happen.

2. Acknowledging Machine Productivity.

Duncan notes that one of the big social issues our governments will face in the future is funding our social security and health care, as these programs are ever more expensive and as the ratio of working youth vs. aging elders continues to decline. Health Care is now 17% of GDP in the US. This percentage will obviously only keep growing in the future, as medicine starts to actually work (remember it really didn’t, for centuries) and as more people want to live longer. In fact, growth in the health care industry as a fraction of GDP is one of the best indicators of our collective desire for longevity.

What is unfortunately not mentioned at this point in the book is that working humans are less and less economically productive vs. machines in our Presingularity Era. As Erik Brynjolfsson and Andy McAfee describe in Race Against the Machine, 2012, people are increasingly going to be thrown out of work by ever more productive machines. So having more humans working is increasingly irrelevant to funding our future programs, as this recent excellent article by John Markoff“Skilled Work Without the Worker,” NYT, 8.19.2012, reminds us. We want to give the existing humans the best jobs we can, and that will greatly stabilize our societies and improve human well being and happiness, but increasingly, those jobs won’t be of central importance to improving our planetary intelligence or capabilility. That role is rapidly moving to our machines.

We need to wake up and admit that the human exponentiation era is over. Biohumans are now in growth saturation, and will soon (circa 2040, just 28 years from now) be in permanent population decline. Our machines are just beginning their exponentiation phase, and we can expect trillions of physical and virtual machine intelligences on the increasingly miniaturized and evolvable hardware platforms of the near future. That’s good, because it is ever smarter and more efficient machines, not humans, that will most improve our precious social contract going forward. Let’s acknowledge and document this fact, so we can get better STEM education, entrepreneurship, funding, and policy going on in America now, not a decade from now.

Discussing how we will pay for all this health care and social security going forward, Duncan cites bioethicist Tom Murray, who states “It’s hard to see how we will be able to afford [health care for 120 year olds in the future] when we are struggling now to pay for people who live to be 80.” This would have been a great place to point out that the solution will happen in two ways. First, we’ll decide to reallocate more of our economy to health care. Our priorities will shift, driven by aging voters in all developed societies. Second, we’ll build a whole lot more smart machines, either now if we’re smart, or later if we’re dumb and unforesighted. Duncan reminds us that U.S. Social Security will be out of funds by 2033. That’s a good place to mention that robots will be driving our cars and wiping our ass for us on our toilets in 2030. Guess which of these two trends matters more. It’s the technical productivity and smartness of our machines that really matters. The rest is just politics.

3. Admitting the Difficulties of “Radical” Life Extension in Biology.

After many years of reporting on biotechnology, Duncan states: ”It’s unclear, however whether this [coming biotechnological] “fix” for aging will be a modest bump of perhaps 10 to 20 percent, or dramatic enough to take us to age 164 or beyond.” Duncan should read Jay Olshansky and Bruce Carnes’ The Quest for Immortality, 2002, one of many good books that clarifies just how difficult it will be for us to make aging interventions in human beings. Improving our lifespans by 10 or 20% over the next ten to twenty years is a very laudable and achievable goal. But anything significantly beyond that is simply fantasy, at least for the coming generation. With persistence, intellectual rigor, and a bit of luck will very likely discover a number of therapies that will bring impressive new longevity and wisdom to our species, adding ten, twenty, perhaps even thirty healthy years to the lifespans of those using them. It would be lovely and transformative to see more seasoned, pragmatic, and high-functioning 100 year olds operating in our social, economic, and political spheres, as my friend Sonia Arrison describes in 100 Plus, 2011.

But I can see no believable way mere human beings will develop therapies that push us significantly beyond our current maximum of 122 years of age. There are just too many independent ways that our molecular biology accumulates error and increasingly malfunctions with age. We fall apart at an accelerating rate from the inside out after we reach sexual maturity. All metazoans do this. I’m sure the AIs will eventually be able to figure out how to redesign our biochemistry and genetics to minimize error. But don’t expect humans to do this, it’s far too hard. The sooner we admit how difficult it is going to be for human-era medicine to push our lifespans much past 120, the sooner we can adjust our sights to achievable futures, and wake up to the fact that the universe is providing us with another option: moving our intelligence to machines.

4. Acknowledging the Advantages of Life in Silicon

This is tough stuff to discuss simply and seriously, as most audiences simply won’t believe it, but we’re already far enough into the machine era to point out the many advantages machines have over over biology in sensing, thinking, remembering, learning, adapting, self-improving, and resource efficiency. It’s most reasonable, when we are willing to overcome our humanocentric bias, to not expect more humans in the future, but rather the emergence of what Steven J. Dick calls a postbiological civilization. Duncan gives a brief nod to machine intelligence, uploading, and cryonics in his book, but he doesn’t mention chemical brain preservation or the Brain Preservation Foundation, the nonprofit I co-founded in 2010 with Ken Hayworth, to investigate whether the synaptic connections that store memories, personality, and identity in human brains can be reliably preserved at the end of life via either chemical preservation or cryopreservation.

It’s too bad he didn’t cover our work. If either or both of these processes can be proven to preserve and retrieve long-term memories in well-studied neural circuits in model organisms such as worms, flies, slugs, and mice, we’ll have objective evidence that these technologies preserve neural information. The training, preserving, scanning, and successful reconstructing of critical elements of a model organism’s long-term memory might happen within this decade in fact, and steady progress is being made in the neurobiology and simulation of memory. We would then have a serious rationale for doing brain preservation for science, for memory donation to our kids or future society, or to achieve full identity reanimation in the postsingularity future, per the patient’s preference. Most interestingly, if chemical preservation works, it could turn out to be a suprisingly inexpensive option, available at death for few thousand dollars in several countries within the next decade.

It is doubtful that more than a small fraction of humans prior to the singularity will be interested in trying out these technologies, even if they were nearly free. Perhaps only the 1% that are presently interested in “Immortality” in Duncan’s surveys. But I expect that if just 100,000 people elected to do this at death, in any society in which inexpensive and validated brain preservation was an option, I believe it would have a substantial effect on the values of that society, shifting it to become more science, progress, future, sustainability, preservation, truth and justice, and community oriented. In fact, I think brain preservation may be the single most powerful technology available to create such a social values shift in the decades prior to the singularity. That is main reason I think it’s worth investigating. Visit our website if you’d like more info on these technologies and the work ahead of us to verify them.

This review is getting long, so I’ll stop here. Those of you who’ve read all this way deserve a few bonuses. I hope you enjoy them!

Bonus 1: Stock Tip. As Duncan reports, based on a conversation with David Sinclair at Harvard, GlaxoSmithKlein (NYSE: GSK, P/E 14) is presently working on an oral compound that will activate SIRT1, a lifespan-regulating gene. If they get it, as seems reasonable, they’ll have a pill that could give humans as much as 10% or so of additional healthy lifespan, while protecting us against all kinds of age-related diseases. You may recall that in 2005, Sinclair increased the lifespan of mice 24% by giving them resveratrol. He formed a company, Sirtris Pharmaceuticals, which was bought by Glaxo for $720M in 2008. It looks like they’ve since discovered since then that it isn’t resveratrol that activates SIRT1 but some chemical analogue. They discontinued work on resveratrol in 2010 and have been working on an analogue since. It’s a reasonable bet that they’ll get something that does eventually work to some degree, and if they do, it will probably be the biggest single advance in longevity medicine we’ve seen yet. Even if they don’t, the stock looks to me like a good two to five year hold. Due your own due diligence first my friends!

Bonus 2: Eat, Fast, and Live Longer. This lovely 2012 BBC Horizon show introduces Intermittent Fasting, the most valuable single life extension practice available to humans today, in my opinion. I expect it will give you up to ten years of additional healthy life, and make you significantly smarter, more energetic, more disease-free, and more vibrant in the process. But regardless of how much life extension you get in the long run, the body and brain benefits (BDNF, neuronal autophagy) that you get every day will be immediately obvious to you. Unlike what Duncan says in his book, caloric restriction is actually quite easy to do if you do it the right way. Intermittent Fasting is one of the great ways to reduce your calories by just getting a little hungry for a few hours every day. Daily hunger does very similar things to your body as daily exercise. You need it, but only in moderate amounts. Doctors are today clueless on the value of being a little bit hungry on a daily basis, and our modern society pushes us to eat at the slightest twinge of hunger or the smallest imagined (not actual) dip in our blood sugar. We let our guts run us, but the science is now there to definitively show the amazing benefits of being driven by your brain, not your gut, which contains 100 million neurons and has a primitive “mind of its own”. In the next ten years, a lot more people will understand that “A Little Hunger Makes You Younger”. I’ll post more on this later on, but for now, enjoy the video. When you are ready to try what the video talks about in daily practice, to prove the benefits experimentally to yourself, I recommend starting with the free eBook at I’ve been doing it for 9 months now and will have more to post on this exciting topic when I hit 12 months.

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