{"id":1685,"date":"2016-02-06T08:21:06","date_gmt":"2016-02-06T08:21:06","guid":{"rendered":"http:\/\/www.brainpreservation.org\/?p=1685"},"modified":"2017-02-15T20:08:59","modified_gmt":"2017-02-15T20:08:59","slug":"dendritic-spines-memory-and-brain-preservation","status":"publish","type":"post","link":"https:\/\/www.brainpreservation.org\/zh\/dendritic-spines-memory-and-brain-preservation\/","title":{"rendered":"Dendritic Spines, Memory, and Brain Preservation"},"content":{"rendered":"

Let me start this post with a disclaimer: I am not a trained neuroscientist. As\u00a0VP and co-founder of the BPF, I enjoy following the scientific literature on neuroscience\u00a0topics.\u00a0I am instead a technology futurist<\/a>, with six\u00a0years of postbaccalaureate and graduate studies in biological sciences, computer sciences, and medicine at UCSD, and a\u00a0master’s degree in futures studies<\/a> from U. Houston. So\u00a0my mind\u00a0naturally goes to the longer-term implications of the advances\u00a0we are seeing\u00a0in these fields.\u00a0And what advances they are! It’s quite an exciting and optimistic time to be alive.<\/p>\n

This post will review some recent advances in the neurosciences, and what they may mean for human brain preservation in coming years. Speaking as a futurist, not a neuroscientist, some of this\u00a0will be partly incorrect\u00a0and speculative, but I hope you find it valuable nonetheless. I will try to indicate where the speculation starts, and please let me know when you disagree. It is only by explaining our models that others can help us correct them, and it is only by giving ourselves permission to tell plausible stories about the future, and to carefully criticize them, that our social foresight grows.<\/p>\n

\"Optical<\/a>

Optical Probe in Mouse Cortex. Source: SciAm 2010.<\/p><\/div>\n

The most interesting neuroscience\u00a0advance\u00a0I saw in 2015 was in optogenetics<\/a>. It\u00a0was the establishment, labeling and optical erasure of a long-term synaptic\u00a0memory in a mouse’s motor\u00a0cortex.\u00a0For the paper, see Hayashi-Takagi et al. (2015) in\u00a0the References below. Akiko Hayashi-Takagi is a professor of medicine at the University of Tokyo. His team\u00a0developed a transgenic mouse<\/a> that expressed an optogenetic protein preinserted into the mouse’s dendritic spines<\/a>, so they could see any of them\u00a0change as the mouse learned a motor task. They then observed synaptic remodeling and dendritic enlargement in a very small subset of cortical neurons during the task. Then they used a fiber optic probe to shrink just a few of those enlarged\u00a0dendrites, and the memory disappeared. This\u00a0work supported longstanding theories by many\u00a0neuroscientists that connectomics<\/a>, and specifically the sizes, shapes and types of connectivity among dendritic spines,\u00a0are the most fundamental way that long-term memories are stored in our brains. As Lu and Zuo at UC Berkeley speculate (see Yirka 2015), what Hayashi-Takagi’s team\u00a0may seek\u00a0to do next is learn\u00a0how to selectively enlarge spines with their probe, so the mouse can acquire motor tasks without ever having been taught them, just by rewiring their dendritic circuits.<\/p>\n

Dendritic spines are signal input channels that synapse\u00a0onto neural cell bodies, where their firing is integrated in space and time so that the cell can decide when and how to generate an action potential<\/a>. Generally, the larger the spine, the stronger the neural circuits and decision systems that it is a part of. An average of ten\u00a0thousand spines of varying sizes typically\u00a0synapse at various locations onto the cell bodies of each pyramidal neuron<\/a> in mammalian hippocampus and cortex. As recent work by Bartol et al. (2015)\u00a0at Salk argues, there may be as many as 26 different relevant sizes of these synapses, enabling a single human brain to store as much as a petabyte<\/a>\u00a0of information in its neural circuitry. A petabyte is a vast amount of information, but it is still small compared to the global web. Google, Amazon, Microsoft and Facebook together store about 1,200 petabytes between them. The web itself was estimated in 2009 to contain 500,000 petabytes. Keep in mind here that we are only referring to information stored in neural circuitry and synapses, which is presumably the highest order, most important information that we care about when we contemplate the prospect of\u00a0preserving our selves, and in particular,\u00a0our declarative and procedural memories.<\/p>\n

Looking beyond our connectome, with its dendrites, synapses, and circuits, a vast amount of additional\u00a0chemical and molecular information exists in any brain. Consider for example all the epigenetic and chemical information stored inside neurons, and in all their supporting cells, including glial cells, which are up to four times more plentiful than\u00a0neurons in our cerebral cortex (you may have learned in\u00a0Kandel’s Principles of Neural Science<\/em>, that glia are\u00a0“up to 50 times more plentiful than neurons”, but that no\u00a0longer looks correct<\/a>).\u00a0All of this information is surely much\u00a0greater than a petabyte per human being.<\/p>\n

But how unique<\/em> is this information\u00a0to each of us, as individuals? How much would it hurt you if you lost that information in a future upload of your present self into a machine? Your glia are preserved in brain preservation, and all those chemicals and molecules as well. So if we need them in a future emulation, we’ll be able to upload those, too. But will we need to? Some folks in the neuroelectrodynamics<\/a> community, who are trying to build a theory of mind based on the physics of electrical interaction in the brain, from the molecular level on up, might argue we will need a lot more than a unique connectome to capture our individual minds. Others, though, including several neuroelectrodynamicists, would argue that we may need such information in a generic<\/em> human brain emulation, but not\u00a0the unique versions of it in each of our brains.\u00a0A lot of chemical and electrical information is constantly coursing through a brain, but most of it is there to keep the system alive. Only a small amount of this information supports our\u00a0mind, and only a fraction of that information records our unique memories and personality.\u00a0My current thinking, based on my read of the current literature, is almost all of our\u00a0higher selves is likely to be stored in\u00a0our connectome, including its unique morphology and receptor types and densities.<\/p>\n

Here’s a thought experiment for you: If your connectome was preserved and uploaded into a generic brain emulation in a computer in the future, do you think you would still be mostly you? I would say yes, at present. I expect\u00a0you would likely quickly grow into new personality going forward, as your new “substrate” would give you different learning abilities and proclivities. But I also expect your higher memories, and your memories of your past personality would be very well preserved, and you’d still feel very much like “you.” I don’t have a lot of evidence for this view at present. But as neuroscience and computational neuroscience continue to advance, and as we upload (emulate) simple animals neural circuits into computers in coming years, I bet we will gain a very\u00a0good understanding on these issues long before the first human uploading occurs.<\/p>\n

So let’s look closely now at this connectome, and at one particular aspect of them, dendritic spines, which are a key way to understand\u00a0how our memories and personalities grow. Dendritic spines\u00a0are typically excitatory at their synapses, using the neurotransmitter Glutamate<\/a>, but a small number\u00a0are inhibitory, using the neurotransmitter GABA<\/a> instead. Add all this together and you’ve got tremendous stored neural complexity merely\u00a0in the connectomics, the connectivity, shape and size and “sign” (usually excitatory, but\u00a0occasionally\u00a0inhibitory) of the “arbor<\/a>” or tree-and-network structures of\u00a0these neurons.<\/p>\n

\"Types<\/a>

Types of Dendritic Spines. Source: Wikipedia.<\/p><\/div>\n

Again, a number of neuroscientists working to understand \u957f\u65f6\u7a0b\u589e\u5f3a<\/a> (stable long-term changes in synaptic strength) and synaptic plasticity<\/a> (the ways\u00a0synapses change their strength) have long suspected that is primarily\u00a0the sizes and connectivity patterns of these arbors that store our precious episodic memories<\/a> (memories of self-related events and concepts). There’s another massive set of arbors that use\u00a0inhibitory (GABAergic) rather than excitatory (Glutamatergic) synapses. These include\u00a0spiny neurons<\/a> in the basal ganglia\u00a0and\u00a0purkinje neurons<\/a> in the cerebellum (our “little brain”). Just as pyramidal arbors\u00a0store our episodic memories (autobiographical and conceptual learning), purkinje arbors are involved in filtering down a vast\u00a0set of potential\u00a0motor actions, and storing\u00a0our procedural memories<\/a>\u00a0(motor\u00a0learning).\u00a0In both cases, the\u00a0connectomics of intricate dendritic arbors may be the primary way that long-term stable learned information is stored. See this great Singularity Hub article, How the Brain Makes Memories<\/a>, Jul 2015, by neuroscientist Shelly Fan<\/a> of UCSF, describing how episodic memories are encoded (“incepted”)\u00a0by single neurons, from single presentations of faces and places, and how these “engrams<\/a>”\u00a0interact with other neurons to add detail to a memory.<\/p>\n

This arbor-based approach to understanding memory encoding, for all its value,\u00a0may still be an\u00a0oversimplification.\u00a0For example, some neuroscientists now believe\u00a0that parts of the neuron’s epigenome <\/a>(in the cell nucleus) may be involved in learning and memory.\u00a0Again, according to Ken, I’ve heard that the epigenome, according to the literature so far (and not by our own assessments), also appears well-preserved by both of the protocols presently\u00a0competing for our prize. That is rather to be expected, as DNA, like the cytoskeleton of cells, is quite a stable macromolecule.<\/p>\n

Most importantly however, the synapse, at the end of each of these spines has\u00a0massive molecular complexity, and its essential features must be preserved too. One of the world’s\u00a0leading synaptomics researchers, Stephen Smith (2014) estimates there are hundreds of protein species in every mammalian synapse, and\u00a0he reminds us that our theories of how learning and memory work are still not deeply evidence-based. We may think<\/em> we know most of the\u00a0key\u00a0neurotransmitters involved (Glutamate, GABA, 5-HT, Dopamine, acetylcholine, norepinephrine) and since the mid-1990’s we think we’ve now discovered several of\u00a0the\u00a0key plasticity proteins (such as\u00a0Arc<\/a>) that stably store our memory engrams, stitching them into the cytoskeleton of the synaptome. But we’re clearly still missing basic pieces of the story, and we\u00a0haven’t yet built the big data sets and trained our\u00a0machine learning systems to tell us, from the bottom up, what all the critical\u00a0synaptic systems are.<\/p>\n

\"Smith<\/a>

Synaptaesthesia (Machinery of Mind)<\/em><\/a>, Smith Lab, Stanford U (2012)<\/p><\/div>\n

To address this problem, Smith’s team at Stanford pioneered a new proteomic imaging method, array tomography<\/a>, that makes\u00a0ultrathin slices of chemopreserved (plastic-embedded) neural tissue, transfers\u00a0them to a coverslip, flourescent antibody stains and images them for a variety of key proteins and neurotransmitters, then uses software to reconstruct the most detailed 3D images of the synaptome and connectome that we have yet seen. Watch the Smith Lab’s mesmerizing brief\u00a0video, Synaptaesthesia<\/a>\u00a0(Machinery of Mind)<\/a><\/em>\u00a0(picture right), for an array tomography-reconstructed section of one piece of a mouse’s whisker barrel, showing the six layers of somatosensory cortex and a bit of striatum below it. It’s an awe-inspiring demonstration of neural\u00a0complexity. Smith estimates this is roughly 1\/150 millionth of the human brain’s volume.<\/p>\n

Clearly there’s enough complexity in these arbors and their synapses to represent\u00a0a lifetime of human memory and identity. Fortunately, tools like array tomography show us that we now have the ability to capture that complexity at the time of death, using either chemopreservation (“plastination”) or aldehyde-stabilized cryopreservation, and store it for an arbitrary number of years until we can figure out how it works. That’s a very exciting realization. This new ability, when reliably scaled to whole brains, and combined with the rapid progress we are seeing in automation and\u00a0computational neuroscience<\/a>, makes it a reasonable bet\u00a0that we’ll be able to affordably “upload” all this information into a much faster, more flexible, and efficient virtual form, perhaps even within this century. As deep learning continues to accelerate in its abilities (Google DeepMind’s AlphaGo<\/a>\u00a0will go up against the top Go player in the world in March) more of the\u00a0doubters on this particular point may be\u00a0convinced. Fortunately, we’ll have plenty of years before uploading arrives to see if machine learning continues to improve much faster, more flexibly, and more efficiently than human learning has over evolutionary history. Consider this simple observation: Biological neurons think and learn at roughly 100 miles per hour, using chemical action potentials, and artificial neural networks “think”, or\u00a0learn from rapidly growing electronic data, at the speed of light, using electrons. The latter system is vastly more “densified and dematerialized” in its critical computational processes, as I discuss in my forthcoming book, The Foresight Guide<\/a><\/em> (2016).<\/p>\n

The big win, from my perspective\u00a0(more speculation here) comes when people start to realize that our information technologies, including both the global web and our personal sims<\/a>–conversational personal software agents, arriving as soon as the end of this decade, that model our interests and\u00a0help us advance\u00a0them–are\u00a0rapidly becoming a natural extension of our biological selves. We human beings are becoming \u00a0continually growing and learning\u00a0“informational entities,” a mix of both our biological and technological networks. This fortuitous development may happen in every corner of the universe when\u00a0biological intelligence develops information technology, so this fusion may be less a matter of human creativity than human discovery of natural efficiencies in our universe’s physics of computation. What mix we are of the two (biology and technology) seems much less important than that our important patterns are both preserved from degradation and always susceptible to feedback, renewal, and improvement.<\/p>\n

We must remember that neural networks, whether biological or artificial, eventually become\u00a0overtrained and brittle. The only way out of that trap is\u00a0rejuvenation and retraining. We are a long way from figuring out how to do that with human biology, but we are already learning how to do that renewal with\u00a0our deep learning machines. Therefore, the more technological we become, the more every one of us can reunderstand ourselves,\u00a0at\u00a0at least the technological portions of ourselves, as lifelong learners, as perpetual children, experimenters, and investigators. I’ll have more to say on that topic as well in The Foresight Guide.\u00a0<\/em>I believe that our emerging personal digital learning abilities will make us far less inflexible, dogmatic and judgmental of others. When it isn’t so very hard to change our views, every position\u00a0becomes more lightly held, able to be improved\u00a0as new theories and data come in.<\/p>\n

I\u00a0also believe (more speculation) that there will be many more\u00a0big simplifications we’ll discover in the neuroscience of learning and memory, both in the top-down, theory driven way that has dominated the field so far, and in the bottom-up, machine-learning, data-driven way that Smith advocates. These simplifications will tell us just what features of all this synaptic and connectomic diversity are key to individual memory and identity, allowing us to ignore a much larger set of cellular and physiological processes that\u00a0are there\u00a0to keep our neurons alive. I think it is a special\u00a0subset of our neural\u00a0complexity that stores, in a stable way, the higher information we care about. That’s clearly what neuroscience has been\u00a0arguing so far. You and I can understand each other in conversation not because of the diversity in our synapses, but because of some deep developmental commonalities to our neural arbors. Figure out those commonalities, our “baseline brain”, and we’ll also get a much better picture of the systems that store our precious individual differences in thinking, emotion, and personality on top of that brain architecture\u00a0and function we all share.<\/p>\n

There is also a very old idea in the neuroscience of learning and memory that short-term memories, in both hippocampus and cortex, are mediated by lots of short-term protein changes in the synapse (recall Smith’s synaptic diversity), but long-term memories require either\u00a0new synapse formation, synapse enlargement, or\u00a0other obvious morphological\u00a0connectomics changes. If that simplification turns\u00a0to be true,\u00a0then the first benefit that we may\u00a0see in\u00a0brain preservation technologies will be the ability to know that our long-term memories, at least, can be read and uploaded into virtual form in the forseeable future, as morphology scanning and reconstruction is a particularly tractable computational task. We are already doing morphology scanning and selective protein tagging using human-supervised machine learning approaches,\u00a0with very\u00a0small portions of mammalian brains today, as the movie above shows. Fully automated and unsupervised machine learning\u00a0classification approaches are clearly coming in the future as well. So as we will now discuss,\u00a0long-term memory retrieval from model organisms may turn out to be the first proof of principle of the value of the brain preservation choice.<\/p>\n

\"YusteDendriticSpines2010\"<\/a>

Yuste 2010<\/p><\/div>\n

Some scientists are already offering some elegantly simple morphological memory hypotheses for long-term memory. The\u00a0distinguished neuroscientists Bourne and Harris (2007) tell us\u00a0that in adult human hippocampus and cortex, roughly 65% of our spines are ‘thin’, about 25% are ‘mushroom’ spines, and the remaining 10% are stubby, branched, or another\u00a0variety of ‘immature’ forms. See the picture at left\u00a0for the different shapes. In their paper, restating them in layman’s terms,\u00a0Bourne and Harris\u00a0propose that thin spines are what we do our thinking with, interpreting\u00a0our sensory data and relating it to our memories and motor outputs, and mushroom spines are where we store our stable long-term memories. Many of these mushroom spines are strongly\u00a0attached\u00a0to their postsynaptic cell body via anchoring\u00a0proteins called cadherins<\/a>\u00a0(cell-to-cell adhesion proteins), which presumably makes a morphological memory so stable that it will persist for a lifetime, through all the normal cellular and extracellular biochemical and osmotic changes that constantly occur in living cells. A special kind of\u00a0supramolecular assembly called a cadherin-catenin complex<\/a> has been implicated in the signal transmission process that controls synaptic plasticity.\u00a0Again, to oversimplify a bit, if the Bourne and Harris\u00a0model proves true, about one quarter of the circuitry of the\u00a0human\u00a0brain is dedicated to memory storage, and the rest is dedicated to thinking, both about our outside world, and our own memories. We may be\u00a075% thinking, and 25% memory machines. Pretty neat, huh?<\/p>\n

Is the architecture of mushroom dendritic spines in fact the “hard drive” of our\u00a0brains? Are all of our\u00a0spines and their synapses, in cortex, thalamus, and a few other key brain areas, what we really need to most carefully preserve, in brain preservation protocols, in order to later read out long-term memory? Perhaps it’s too early to say for certain, but I’m now a big fan of them. A good\u00a0book on spines, which explores\u00a0how they form\u00a0neural circuits and networks to do memory storage, computation and pattern recognition, is Rafael Yuste’s Dendritic Spines<\/a><\/em> (2010).<\/p>\n

\"A<\/a>

A mouse. A laser beam. A manipulated memory<\/a>. Liu and Ramirez, TEDxBoston 2013 (TED.com, 15 min)<\/p><\/div>\n

Looking back two years ago now, you may\u00a0also have heard about\u00a0the Liu\u00a0and Ramirez (Dec 2013) paper, where a mouse’s memory was erased and then “incepted” (introduced) back into its\u00a0hippocampus, again using optogenetics with a very small population of neurons. These scholars did a\u00a0great TED talk in 2013<\/a>, which generated 1 million views (it should have generated 20 million!). See also this nice writeup of their work by Noonan (2014). Via optical probes, they\u00a0were even able to alter connections from the hippocampus to the amygdala, which modulates emotions, and turn a mouse’s fear memory into positive memory, in relation to a particular place.\u00a0Unfortunately Liu, just 37, tragically died of a heart attack last year, but Ramirez continues this amazing work.<\/p>\n

While the\u00a0Liu and Ramirez work is bold and ingenious, I also found it difficult to interpret, as\u00a0our\u00a0hippocampus apparently stores both short-term memories (the last few days) and pointers to our long-term memories (stored in cortex), and it does so in a way that doesn’t always involve spine enlargement and making new synapses, but is often just restricted to molecular changes to existing synapses. So hippocampal arbors may need to be\u00a0more multifunctional than cortical arbors, and may work a little differently. The hippocampus is also the only place in our brain where neural stem cells are constantly budding, to replace damaged neurons and their dendritic trees, presumably because they are so heavily used for short-term memory storage and “pointing” to other places in our brains. For more on the hippocampus, and why preserving it, while\u00a0certainly\u00a0ideal, may actually not be as important as our cortex and the\u00a0rest of our brain, see my 2012 post, Preserving the Self for Later Emulation: What Brain Features Do We Need?<\/a>\u00a0So for me, it took\u00a0the\u00a0Hayashi-Takagi experiment\u00a0to finally become optimistic about\u00a0how simple and morphological long-term memory storage in human cortex may\u00a0turn out to be.<\/p>\n

According to Ken Hayworth, President of the BPF, as far as we can tell today, dendritic size, shape and connectivity information appears well-preserved in the protocols being used\u00a0by both of the current competitors for the \u8111\u4fdd\u5956<\/a>. Of course, in addition to morphology, some additional amount of\u00a0molecular, and receptor information in the\u00a0synapses (remember Arc and those cadherin-catenin complexes, for example) and perhaps some epigenetic\u00a0information in the cell nucleus, may be necessary to reconstruct memories. Whatever information that turns out to be, we will have to ensure it is protected.<\/p>\n

\"Zeiss<\/a>

Zeiss AURIGA Desktop\u00a0FIB-SEM, 2012.<\/p><\/div>\n

One way we’ll know we have protocols that are up to the task is when neuroscientists\u00a0have successfully preserved, scanned\u00a0(“uploaded”), and retrieved long-term memories from well-studied neural circuits in model organisms. I think this “memory retrieval” challenge is one of the frontiers of computational neuroscience. Neuroscientists are already using tools like FIB-SEM<\/a>, and semiautomated multibeam scanning systems, to “upload” very small animal brains, like nematodes, zebrafish, and portions of fly brains. Ziess, one of the leading manufacturers of FIB-SEM machines, is now making a 61-beam machine that can parallelize connectomic scanning, and even more parallelized and automated scanning systems will emerge as connectomics scales. These brains and brain sections are on the order of the size of a tip of a pencil. We don’t yet understand how long-term memories are stored in these brains, at least not episodic and conceptual memories. So\u00a0computational neuroscientists don’t yet have the knowledge needed to read these uploaded circuits. But all this recent progress in neuroscience\u00a0and related progress in deep learning<\/a> in computer science (a topic we will leave to another time) give me hope that we will get that knowledge, and episodic memory retrieval\u00a0from a well-studied set of circuits in a scanned animal brain will be demonstrated in a computer within\u00a0this decade (more speculation).<\/p>\n

\"memory-place-grid-cells\"<\/a>Once we can successfully predict, by looking either at\u00a0live or\u00a0uploaded versions of neural\u00a0arbors and their synapses, how a complex animal\u00a0has been trained in a spatial or conceptual task, in blinded\u00a0experiments, given a large variety of initial training options, we’ll know we’ve cracked the long-term memory problem. An impressive version of memory retrieval from scanned brains might happen\u00a0first in relation to the animals’ spatial model of the world, a complex neural representation system that\u00a0we’ve recently begun to understand.\u00a0\u00a0See Wikipedia’s pages on\u00a0hippocampal place<\/a> \u548c grid cells<\/a>\u00a0for more. The team that discovered grid cells, how animals use special networks\u00a0of\u00a0neurons to represent their position in space, won the 2014 Nobel prize in Physiology and Medicine<\/a>.<\/p>\n

When we can retrieve one of these high-level memories out of preserved and scanned brains, we’ll know that brain preservation protects\u00a0something very valuable, our long-term memories, and the ways we tend to think about them. Knowing whether\u00a0these protocols will preserve other things, like our unique personality and consciousness, whether they\u00a0can bring “us” back, I think we will eventually get there, but more slowly\u00a0. Consciousness, for example, is still not well\u00a0understood in neuroscience. Though neuroscientists offer promising materialist models such as neural synchrony<\/a> (see Buzsaki 2011)<\/span>, no model is yet validated or widely agreed upon in the neuroscience community, and the mechanisms of consciousness, which may include little-understood phenomena like\u00a0ephaptic coupling<\/a><\/span>, are still unclear. <\/span><\/p>\n

So for me at least, memory preservation is the first proof of value for these technologies. In addition to the great value of brain preservation work for neuroscience, medical science, and computer science, it seems likely to also have\u00a0great personal value, for those who think their life’s memories\u00a0are\u00a0worth preserving and giving to future loved ones, to science, or to society in virtual form. If I think\u00a0a protocol is likely to preserve\u00a0my long-term memories and thinking patterns at the time of my death, I will be very interested in it, both for myself and my loved ones, if it can be done both affordably and sustainably. If it preserves\u00a0anything beyond that, if “I” feel like I have come back in that future world, that will be a great bonus, but not necessary to establish its basic value.<\/p>\n

As further progress\u00a0occurs in the neuroscience of learning and memory, as well as in the computational neuroscience that uses neural network and other approaches to store increasingly biologically-inspired “memories” in computers, the BPF will continue to investigate whether the best protocols available to neuroscientists, computational scientists and medical professionals are fully up to the task of preserving our own memories and identity, for all who might desire to have the preservation choice available to them at the time of their biological death. Thanks for reading.<\/p>\n

References<\/strong><\/p>\n

Bartol et al. (2015) Nanoconnectomic upper bound on the variability of synaptic plasticity (Full Text)<\/a>. eLife<\/em> 2015 Nov 30;4:e10778.<\/p>\n

Bourne and Harris (2007) “Do thin spines learn to be mushroom spines that remember? (PDF)<\/a>, Curr. Opin. Neurobi<\/em>o 2007, 17:1-6.<\/p>\n

Buzsaki, Gyorgy (2011)\u00a0Rhythms of the Brain<\/a>,<\/i>\u00a0Oxford U. Press.<\/p>\n

Hayashi-Takagi et al. (2015) Labeling and optical erasure of synaptic memory traces in the motor cortex (Abs)<\/a>. Nature<\/em> 2015 Sep 17;525(7569):333-8.<\/p>\n

Liu et al. (2013) Inception of a false memory by optogenetic manipulation of a hippocampal memory engram (Full Text)<\/a>. Phil. Trans. Royal Soc. B<\/em> 2013 Dec 2;369:20130142.<\/p>\n

Noonan, David (2014) Meet the Two Scientists Who Implanted a False Memory Into a Mouse<\/a>, Smithsonian<\/em>, Nov 2014.<\/p>\n

Ramirez and Liu (2013) A mouse. A laser beam. A manipulated memory<\/a>. TED.com<\/em>,\u00a0TEDx Boston,<\/em>\u00a0Jun 2013 (15 mins)<\/p>\n

Smart, John (2012) Preserving the Self for Later Emulation: What Brain Features Do We Need?<\/a>, EverSmarterWorld.com<\/em> Sep 24, 2012.<\/p>\n

Smith, Stephen (2012) The Synaptome Meets the Connectome: Fathoming the Deep Diversity of CNS Synapses<\/a>, Talk, SCI Institute, 6 Mar 2012 (YouTube, 69 min)<\/p>\n

Smith Lab (2012) Synaptaethesia<\/a>, Stanford U. School of Medicine\u00a0(YouTube, 5 min)<\/p>\n

Yirka, Bob (2015) Researchers erase memories in mice with a beam of light<\/a>. MedicalExpress.com<\/em> Sep 11, 2015.<\/p>\n

Yuste, Rafael (2010)\u00a0Dendritic Spines<\/a>, <\/em>MIT Press.<\/p>","protected":false},"excerpt":{"rendered":"

Let me start this post with a disclaimer: I am not a trained neuroscientist. As\u00a0VP and co-founder of the BPF, I enjoy following the scientific literature on neuroscience\u00a0topics.\u00a0I am instead a technology futurist, with six\u00a0years of postbaccalaureate and graduate studies in biological sciences, computer sciences, and medicine at UCSD, and a\u00a0master’s degree in futures studies […]<\/p>\n","protected":false},"author":7,"featured_media":1698,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,4,6,7,27],"tags":[],"coauthors":[25],"class_list":["post-1685","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-brain-preservation","category-mind-uploading","category-cryopreservation","category-electron-microscopy","category-chemopreservation"],"_links":{"self":[{"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/posts\/1685","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/comments?post=1685"}],"version-history":[{"count":50,"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/posts\/1685\/revisions"}],"predecessor-version":[{"id":1977,"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/posts\/1685\/revisions\/1977"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/media\/1698"}],"wp:attachment":[{"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/media?parent=1685"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/categories?post=1685"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/tags?post=1685"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.brainpreservation.org\/zh\/wp-json\/wp\/v2\/coauthors?post=1685"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}