Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, of
Speaker 1: the podcast that looks at the future and says I'll
Speaker 1: stay the same pack up, don't straight. I'm Jonathan Strickland
Speaker 1: and I'm Joe McCormick. So I was reading this story, Lauren,
Speaker 1: you're just giggling over there. Do you recognize the Yeah, Yeah,
Speaker 1: it's from the Yes, it is from the A as
Speaker 1: you know what the song's name is, right. Maps. We're
Speaker 1: gonna talk about maps today, but but not the kind
Speaker 1: what with the mountains and the oceans. Now, we're not
Speaker 1: gonna talk about the political borders. I'm not going to
Speaker 1: talk about cartographers here. We're going to talk about maps
Speaker 1: of nervous systems, which is has a specific name, a
Speaker 1: connect home. Now what a connect home that I don't know.
Speaker 1: It's CEO in an E C T O M E.
Speaker 1: What on earth is that thing? It looks like it's
Speaker 1: connect to me? But it's what I was thinking. Honestly,
Speaker 1: I actually watched a Ted talk and I'm just going
Speaker 1: to pronounce it the same way the fellow did, so
Speaker 1: connect them. What it is is, it's a a complete
Speaker 1: neural map of an organism's neurons. So think of it
Speaker 1: like a wiring diagram. If you were an electrician and
Speaker 1: you need to have a diagram of a wiring system.
Speaker 1: It's like that, except for the nervous system of an
Speaker 1: actual organism. Now, to have a true connect dome, you
Speaker 1: need it to be three dimensional, because we are not
Speaker 1: two dimensional creatures, you know, so you wouldn't want to
Speaker 1: have just a flat representation. Most of us. See, most
Speaker 1: of us are are well rounded individuals. Everyone in this
Speaker 1: room is, certainly, So you want to have a three
Speaker 1: dimensional uh ability, you know, you want to be able
Speaker 1: to depict it in three dimensions so that you have
Speaker 1: an accurate look at what this nervous system is. And
Speaker 1: it's supposed to show all the neurons and connections between
Speaker 1: them within our an organism. So surely we have not
Speaker 1: done this to an actual organism as we have. Only
Speaker 1: one has a complete connect dome of all of its neurons,
Speaker 1: and that would be Senor Rhabditis elegance or see elegans
Speaker 1: also known as worm teeny tiny worm. Um. Yeah, it
Speaker 1: only has three hundred two neurons, so yeah, not not
Speaker 1: You're like totally like, I'm not impressed by this groundbreaking
Speaker 1: scientific discovery. Take that scientists deal with my puff. Three
Speaker 1: neurons is a tiny amount comparatively speaking, but we'll get
Speaker 1: to that. So scientists began to study these worms in
Speaker 1: an effort to understand neurology better, among other things. I mean,
Speaker 1: of course, scientists all over the place like like worms.
Speaker 1: I mean, who doesn't, but they these particular scientists were
Speaker 1: interested in studying the worms to to create a connect home,
Speaker 1: and they figured that this was a good place to start.
Speaker 1: So back in the nineteen seventies you had a team
Speaker 1: that was looking into it, and it wasn't until nineteen
Speaker 1: eighties six that they were able to publish the first
Speaker 1: prelimit arey connect home for this worm. And more than
Speaker 1: twenty years after that you had a second team produce
Speaker 1: a much more complete connect home, a more accurate representation
Speaker 1: of the connect home. So it is a very slow process.
Speaker 1: And that's just three neurons, right, It's not that many
Speaker 1: comparatively speaking. And then this year two thou fourteen, in May,
Speaker 1: another team published a paper about creating a real time
Speaker 1: three D map of this worm's neural system and action,
Speaker 1: meaning you could see the impulses moving up and down
Speaker 1: the length of this worm as its nervous system was
Speaker 1: taking over for any anything like from detecting stimuli to
Speaker 1: sending the information up to the ganglia to reacting to it.
Speaker 1: Uh it was. This is what's really the groundbreaking. I mean,
Speaker 1: all of its groundbreaking, but this is really ground baking
Speaker 1: breaking stuff. You're talking about being able to get a
Speaker 1: real time view of what's going on within the nervous
Speaker 1: system of an organism. I can't even imagine how one
Speaker 1: would do that. Well, you don't have to imagine, because
Speaker 1: someone I will tell you. Uh no, this was really
Speaker 1: an interesting idea. So within this worm's nervous system, essentially
Speaker 1: what's going on is neurons are passing information in the
Speaker 1: form of calcium ions. So these calcium ions are passing
Speaker 1: from one neuron to the next, right chemical electrochemical transaction exactly.
Speaker 1: So if you go with this calcium ion, if you're
Speaker 1: able to make it stand out in some way and
Speaker 1: then you're able to actually capture that information, you can
Speaker 1: see what's going on. So they developed some fluorescent bio markers.
Speaker 1: So essentially this is just meaningless information that glows. So
Speaker 1: it's it's not meant to harm the organism in any way.
Speaker 1: You want it to to keep everything intact, but it
Speaker 1: ends up marking those calcium in It glows specifically when
Speaker 1: it binds with a calcium ion exactly, so that way,
Speaker 1: when this is going on, the scientist can see it now.
Speaker 1: Of cour, just making the worms have little calcium ions
Speaker 1: like glow doesn't mean that they're going to be able
Speaker 1: to see everything that's going on, right, I mean, you're
Speaker 1: not gonna be standing there looking at a worm thinking, well,
Speaker 1: there's a flash of light. It's thinking something that's That's
Speaker 1: another way it works that I would have gotten to
Speaker 1: be fair. To be fair, worms aren't really thinking that
Speaker 1: much anyway. So if it wasn't sparking, you'd just be thinking, well,
Speaker 1: we got a particularly stupid worm. Oh look, he's contemplating
Speaker 1: consubjection to like this. This worm is obviously a fan
Speaker 1: of Nietzchi, a scholar. I like that. We're all just
Speaker 1: gonna start let's just keep on naming various thinkers. So
Speaker 1: what are you going to do about that? You know,
Speaker 1: if the worm doesn't think, it just disappears anyway. The
Speaker 1: scientists decided that the way to capture all this information
Speaker 1: because obviously if you used a regular light microscope, even
Speaker 1: if it was powerful enough to detect those calcium ions
Speaker 1: that are glowing, you would still only have a two
Speaker 1: dimensional representation of what was happening. Right, You're not getting
Speaker 1: any depth there. So how do you get the depth?
Speaker 1: And that they managed to do through a technique called
Speaker 1: light field deconvolution micross scope. What I know, I love
Speaker 1: different kinds of microscope. I do too. No, I'm not joking.
Speaker 1: I really do believe that was my cross could be
Speaker 1: treats a snap crack on pop. Well no, okay, no,
Speaker 1: I'm serious. So I grew up with a little, uh
Speaker 1: kind of cruddy micro microscope that I would stare through.
Speaker 1: I'd i'd stay in a little pain and put some
Speaker 1: kind of junk on there and look at it. But
Speaker 1: everything just looked like gray squiggles, you know, because it
Speaker 1: was that back lip microscope we've all seen before. It's
Speaker 1: you got the light coming up the tube from the
Speaker 1: bottom and you see this two dimensional cross section that's
Speaker 1: not very colorful. It's wonderful now that we have all
Speaker 1: these techniques to see three dimensional shapes at the micro level, right,
Speaker 1: and light field. This is something you guys might have
Speaker 1: heard about, in fact that we may have even talked
Speaker 1: about in the past. Uh, you've heard of light field photography,
Speaker 1: which is what the lightrous camera uses. This is the
Speaker 1: idea of upturing information by not just the fact that
Speaker 1: there's light there, but capturing the direction of light, the
Speaker 1: rays of light within a scene. So you're capturing all
Speaker 1: the light that is within the scope of that camera. Now,
Speaker 1: with a litros camera, what this means is that when
Speaker 1: you take a picture, you can change the focal point
Speaker 1: of that picture after it's been taken. So i have
Speaker 1: Lauren standing in the foreground, and I've got Joe way
Speaker 1: off in the background, and I take a picture, and
Speaker 1: at first laurens and focused, Joe's not. But if I
Speaker 1: tap on Joe, he suddenly becomes in focus and Lauren
Speaker 1: goes out of focus, and it's it's dynamic. I can change.
Speaker 1: I can pick the focal point within the picture and
Speaker 1: change it because it's captured all of the information, as
Speaker 1: opposed to your classic photography, which is stuck with whatever
Speaker 1: the focal point of the lens was at the moment
Speaker 1: you took the picture. Sure um in the specific case,
Speaker 1: of this light field. Deconvolution microscopy the the ideas that
Speaker 1: you're using, rather than the single lens that Joe is
Speaker 1: talking about through traditional microscopy, an array of lenses that
Speaker 1: refract and collect a single point of light, thereby letting
Speaker 1: you get a good idea of where that little blip
Speaker 1: is in three dimensional space. Right, So now you've got
Speaker 1: this more complete look at all these different blips within
Speaker 1: this three dimensional space while you do next, Well, that
Speaker 1: doesn't really help you out that much unless you then
Speaker 1: send it to a computer running special software which is
Speaker 1: taking all this information, crunching it and then forming a
Speaker 1: three dimensional map of that worm's nervous system and then
Speaker 1: showing the actual activity via the little florescent calcium ions.
Speaker 1: While the worm, you know, does normal worms stuff or
Speaker 1: um or abnormal worms, or or is it supposed to
Speaker 1: a to a sort or a smell of some kind.
Speaker 1: So yeah, if you were to give some kind of
Speaker 1: stimuli that ends up making the worm react in some way, right,
Speaker 1: it's it's exposed to a book of Nietzsche that has
Speaker 1: decomposing pages that can eat. That would be probably the
Speaker 1: best kind of Nietici for this particular type of worm.
Speaker 1: But yes, so you're exactly right. You would be able
Speaker 1: to see by the reaction in the nervous system what
Speaker 1: is going on inside the worm. Not necessarily understand it,
Speaker 1: but you would be able to see it. You know,
Speaker 1: keep in mind these are this is an important step,
Speaker 1: but it's just a step. Well, we'll go into more
Speaker 1: about the implications in a little bit, but this complex
Speaker 1: software has to crunch all that put up the map,
Speaker 1: and it's more or less it's it's on a millisecond scale.
Speaker 1: It's something like fifty images per second, which is pretty cool, yeah,
Speaker 1: but still not if you think of time as resolution
Speaker 1: in the sense it's a low resolution image. So it's
Speaker 1: not resolution in the sense of of how many pixels
Speaker 1: or whatever, or how sharp the picture is, but rather
Speaker 1: how how accurate is it over time? And it's a
Speaker 1: time scale that's too slow for it to be uh,
Speaker 1: you know, it's not advanced enough for it for us
Speaker 1: to call it true real time. It's really good. But
Speaker 1: I think what we're going to see is we're going
Speaker 1: to see more development in computer software and this technology
Speaker 1: where we'll start to have an even uh more elongated
Speaker 1: time scale, will get down to like nanosecond level. It's
Speaker 1: which will be especially important for things more complex than worms. Now,
Speaker 1: in order to do all that number crunching, you can't
Speaker 1: just use one computer because it actually is so um
Speaker 1: intense that they have to use grid computing for it.
Speaker 1: So they're using using multiple computers to solve problems in parallel,
Speaker 1: so each computer is taking a little bit of the work.
Speaker 1: And we've talked about this in the past two about
Speaker 1: how you use a distributed computing system in order to
Speaker 1: divide up a big task into lots of smaller tasks,
Speaker 1: which makes it more manageable. Otherwise you would have to
Speaker 1: have the world's fastest computer, and even that might be
Speaker 1: slower than a grid of computers working in this way.
Speaker 1: But as long as your problems don't have to be
Speaker 1: solved sequentially like that, that solving number two doesn't depend
Speaker 1: on solving number one, you can split them up, right.
Speaker 1: So that's pretty awesome, this idea of being able to
Speaker 1: see what's going on with the neurons inside a worm.
Speaker 1: But um, what else could we look at? So so
Speaker 1: three neurons, yeah, what about something that has like a
Speaker 1: hundred billion neurons or conservatively eighty billion neurons? Okay, Uh,
Speaker 1: that's a tall order because this this this team the
Speaker 1: best that they've done beyond the worm, and not to
Speaker 1: I'm not trying to suggest that they haven't done their work.
Speaker 1: This is yeah. But the other thing they did was
Speaker 1: they imaged about half of a zebra fish's neurons larva's
Speaker 1: larva's thank you. Yes, the zebrafish lava not an actual
Speaker 1: zebra fish, but the zebra fish larva and the zebrafish
Speaker 1: lava have about ten thousand neurons, so they were able
Speaker 1: to image five thousand of those. Um And there's some
Speaker 1: other examples of organisms that we've managed to create partial connectomes,
Speaker 1: for example, the retina's and visual cortex of a mouse.
Speaker 1: Well those have been completed or mostly complete, I shouldn't
Speaker 1: say completed, partially completed. When you're talking about humans, it's
Speaker 1: eighty two hundred billion neurons, something like a hundred trill
Speaker 1: allion synapses connecting all those neurons and various configurations. Orders
Speaker 1: of magnitude does not do this justice, right. You can't
Speaker 1: just say it's a couple of orders of magnitude. No,
Speaker 1: it's it's an enormous jump from worm to a person
Speaker 1: for most people anyway, would it be fair to say
Speaker 1: that the problem represented by doing the same kind of
Speaker 1: thing with a brain on the scale of a human
Speaker 1: brain is not just like a scaling up problem. It's
Speaker 1: like it's another ballpark entirely. It's not like we just
Speaker 1: need a little more power. It's like we're not We're
Speaker 1: not even close. Yeah, I mean, the closest we've got
Speaker 1: is is working our way towards building a connect them.
Speaker 1: Not a real time three dimensional moving picture, right, you know,
Speaker 1: the one with the worm you're actually able to watch
Speaker 1: it's not thought, but you're able to watch impulses as
Speaker 1: they move throughout the worm's body. With the humans, the
Speaker 1: closest we're getting is the Human Connectome project is the
Speaker 1: best known one. But this is just to create that
Speaker 1: neural map. And I think it's something like participants for
Speaker 1: the entire program for this particular approach. And this is
Speaker 1: a five year mission, which is all of our Star
Speaker 1: Trek fans out there, which began in two thousand nine,
Speaker 1: July two thousand nine, So we're getting up there kissing
Speaker 1: the connect home. So at any rate, Uh, this is
Speaker 1: a collaboration between a few different um research facilities, the
Speaker 1: two main ones being the Laboratory of neuro Image Imaging
Speaker 1: at u c l A and the Martino Center for
Speaker 1: Biomedical Imaging at Massachusetts General Hospital and UH it's sponsored
Speaker 1: in part by the National Institutes of Health, and so
Speaker 1: this is an interesting approach again trying to create a
Speaker 1: neural map of human beings. It's not again, it's not
Speaker 1: a real time representation, but just to actually map out
Speaker 1: all the neurons and synapses at alone is an incredibly
Speaker 1: huge task. So they had to start commissioning new types
Speaker 1: of technology and software to be able to try and
Speaker 1: tackle this problem, and one of those was a super
Speaker 1: scanner from Siemens, and Siemens makes all sorts of different
Speaker 1: UH high grade technology equipment. In this case, they were
Speaker 1: using a m r I that did does three different
Speaker 1: types of m r I in one go. It does
Speaker 1: a diffusion tensor imaging, diffusion spectrum imaging, and high angular
Speaker 1: resolution diffusion imaging also known as HARDY, So all of
Speaker 1: that's different types of magnetic resonance and imaging. Mri I
Speaker 1: type stuff does it so that it can get the
Speaker 1: most um complete picture of a person's nervous system. And
Speaker 1: UH the literature that is available on the human Connectome
Speaker 1: Projects shows pictures of these things and they do look
Speaker 1: like something straight out of you know, Star Trek or
Speaker 1: Star Wars or something like that. Uh, And they talk
Speaker 1: about how this this equipment, while it's very useful for
Speaker 1: this regular project, will also be incredibly useful for numerous
Speaker 1: medical applications from this point forward. So it's not like
Speaker 1: this was a piece of equipment that was made for
Speaker 1: this one and only one thing and after that, you know, that's, well,
Speaker 1: that was cool. It's actually gonna be useful long after
Speaker 1: this project has concluded, so that's kind of cool. Um. Now,
Speaker 1: on the computer side, they're also using grid computing to
Speaker 1: run all this stuff and make sure that they can
Speaker 1: process all the information in order to actually come up
Speaker 1: with the results. So you know, you think about it,
Speaker 1: if you're looking to map eighty to a hundred billion
Speaker 1: uh little tiny neurons, like you need to map all
Speaker 1: of those and then all their connections, even if it's not,
Speaker 1: you know, as complex as you would first imagine, just
Speaker 1: the number alone means that that's a lot of data.
Speaker 1: So that's why you have to have this kind of
Speaker 1: grid computing approach or else it just wouldn't work. So
Speaker 1: that's kind of interesting. I'd like to, you know, read
Speaker 1: up on it more. Um, there is a website for
Speaker 1: the Human Connect Home project. It is not perhaps the
Speaker 1: most flashy or um sophisticated website I've ever seen, but
Speaker 1: it's certainly effective. So I am interested to see what
Speaker 1: I mean. I was able to understand everything, so that
Speaker 1: that was good. There wasn't a little image of a
Speaker 1: man in a construction hat saying, you know, like, website
Speaker 1: under development, so that was good. But there was a
Speaker 1: dancing baby. There was a midi of a sa base.
Speaker 1: I don't know why. No, I'm just kidding. I'm just kidding.
Speaker 1: But yeah, I'll be really curious to see what all
Speaker 1: the the outcomes are of this project when it concludes.
Speaker 1: I am assuming they're going to publish a lot. So
Speaker 1: I want to play Devil's advocate because, as you all know,
Speaker 1: I like all I'm someone who likes building weird things
Speaker 1: that don't have an obvious benefit to the lay person,
Speaker 1: or sometimes an obvious detriment. Crunch kicking robot was that
Speaker 1: was that? It was? I didn't know alright, no, no, no no, okay, no,
Speaker 1: imagine I'm the lay person who says, what are you
Speaker 1: going to use that for all right, Well, that's a
Speaker 1: good question. What difference does it make. I mean, it's
Speaker 1: a worm brain, so from just going from the worm
Speaker 1: so we're not even talking about human connect Home project.
Speaker 1: We're just talking about that three dimensional look at the
Speaker 1: worm's nervous system. It tells us more about how nervous
Speaker 1: systems in general work. This is an area that we
Speaker 1: know very little about in the grand scheme of things
Speaker 1: we know. What we know is that we don't know
Speaker 1: a lot, right. We do understand some things. We understand
Speaker 1: some of the very basic things that are happening, but
Speaker 1: we don't understand how they're working necessarily across an entire
Speaker 1: system all at once. The way I'd phrase it, though
Speaker 1: obviously I'm no neuroscientists, the way I understand it is
Speaker 1: that we have a lot of observations, but we don't
Speaker 1: know what they mean. And this is another step getting
Speaker 1: closer towards understanding the meaning behind what we've observed. Doesn't
Speaker 1: mean that we're obviously that the lightbulb has come on
Speaker 1: and now everything makes sense, but it's the only way
Speaker 1: for us to get to that understanding is to continue
Speaker 1: making observations and to continue doing experimentation in order to
Speaker 1: really get an idea of what's going on and why
Speaker 1: it's happening the way it is. And the closer we
Speaker 1: get to mapping out nervous systems, the more will understand
Speaker 1: everything from basic functions of a healthy uh, you know,
Speaker 1: human brain to any sort of dysfunctional activity. So whether
Speaker 1: that's from brain damage or from any of a number
Speaker 1: of neurological diseases or disorders, or or or even from
Speaker 1: from mental health disorders. Yeah, I've seen a lot of
Speaker 1: discussion about the Human connect On project. One of its
Speaker 1: goals is to study things like, uh, people who suffer
Speaker 1: from schizophrenia and to see what what is different about
Speaker 1: their nervous systems compared to someone who does not suffer
Speaker 1: from schizophrenia, and perhaps through understanding these sort of dysfunctions,
Speaker 1: we might be able to address them or perhaps even
Speaker 1: cure them in the future. Now granted all of that. Yeah,
Speaker 1: and when you say in the future, you're talking about
Speaker 1: a good distance into the future. We're not talking about
Speaker 1: something that's going to turn around and happen within the
Speaker 1: next six months. I want to be clear about that, because, yeah,
Speaker 1: we mean whenever you read these reports. You know, people
Speaker 1: like the people who write the reports. Not everybody, but
Speaker 1: some people fall into that kind of easy journalism trap
Speaker 1: where you say this will lead to curing mental illness.
Speaker 1: It's it's possible that let me put it this way,
Speaker 1: if we don't do these steps, it will be a
Speaker 1: lot harder to do it. But this, this may or
Speaker 1: may not lead us down that pathway, but certainly if
Speaker 1: we don't do the work, we won't get there. So
Speaker 1: another thing that could possibly happen way off into the future, sure,
Speaker 1: is that by understanding more about how these neural pathways
Speaker 1: work in organisms, we could create a synthetic creature, whether
Speaker 1: that's biological or whether it's compurely electronic. We might be
Speaker 1: able to create a synthetic intelligence by learning more about
Speaker 1: how our intelligence works, or or build that that brain
Speaker 1: like computer that we were talking about a few episodes ago. Yeah,
Speaker 1: so that's another thing that's a possibility. Again, it is
Speaker 1: not necessarily true that what we're doing today or what
Speaker 1: these scientists are doing today, will be a direct path
Speaker 1: from A to B that leads there, but it could
Speaker 1: very well be. That's an important step along the journey. So, uh,
Speaker 1: you know, I always hate making big predictions and saying, oh,
Speaker 1: and in ten years, we're going to have these thinking
Speaker 1: computers and mental mental illness will be a thing of
Speaker 1: the past. I certainly hope that is true. But what
Speaker 1: I am really excited about is seeing more developments in
Speaker 1: this kind of research and finding out what else we learned,
Speaker 1: because again, as we've said multiple times on this podcast,
Speaker 1: learning is great. I mean, the more you learn, the
Speaker 1: better off you are. And so, uh, we were fully
Speaker 1: in favor of it, even if it is something as
Speaker 1: silly as mapping out a worms nervous system and learning
Speaker 1: that it doesn't have a taste for Nietzsche, even if
Speaker 1: the book is rotting so at any rate, Uh, just
Speaker 1: those chemical treated pages there, I tell you. You know,
Speaker 1: the odd thing was totally went for the Kindle version.
Speaker 1: So uh yeah, that wraps up this discussion on just
Speaker 1: the mapping of the nervous system. It's a really cool project.
Speaker 1: Go ahead and check out the Human connect Home project
Speaker 1: and also look around at some of the stories about
Speaker 1: the the various projects that have gone into trying to
Speaker 1: map out this worm's nervous system. I mean that that
Speaker 1: alone is interesting. I mean, imagine they started in the seventies,
Speaker 1: and it wasn't until they were able to publish the
Speaker 1: first rudimentary connect home that just with three two neurons.
Speaker 1: It just proves how complex this is. And then you know,
Speaker 1: again that leap to human big leap for most people.
Speaker 1: I know a few people who comparison to worms not
Speaker 1: that off base. Alright, So guys, if you have any
Speaker 1: suggestions for future episodes a Forward Thinking, I'm not looking
Speaker 1: at anyone in this room. I just love how you
Speaker 1: always pepper our optimistic view of the future with some
Speaker 1: really antisocial commentary. I made the same I made the
Speaker 1: same joke in the video so um, but I've never
Speaker 1: stopped me before, right, I'll tell the same jokelations. Thank you.
Speaker 1: I have a very specific role to fill in this
Speaker 1: world now. If you have any suggestions for future episodes
Speaker 1: off Forward Thinking, if you want to hear more about
Speaker 1: the amazing brain, or there's just something else that really
Speaker 1: fascinates you and you wonder what it's going to be
Speaker 1: like in the future, you should let us know and
Speaker 1: we will be happy to tackle that subject. You can
Speaker 1: contact us through Twitter or Facebook or Google Plus are
Speaker 1: handled all three f W Thinking, or you can send
Speaker 1: a good Ole Fashion Email That address is FW thinking
Speaker 1: at discovery dot com and we'll talk to you again
Speaker 1: really soon. For more on this topic and the future
Speaker 1: of technology, visit forward thinking dot com, brought to you
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