WEBVTT - Mapping the Nervous System

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<v Speaker 1>Brought to you by Toyota. Let's go places. Welcome to

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<v Speaker 1>Forward Thinking. Hey there, and welcome to Forward Thinking, of

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<v Speaker 1>the podcast that looks at the future and says I'll

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<v Speaker 1>stay the same pack up, don't straight. I'm Jonathan Strickland

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<v Speaker 1>and I'm Joe McCormick. So I was reading this story, Lauren,

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<v Speaker 1>you're just giggling over there. Do you recognize the Yeah, Yeah,

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<v Speaker 1>it's from the Yes, it is from the A as

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<v Speaker 1>you know what the song's name is, right. Maps. We're

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<v Speaker 1>gonna talk about maps today, but but not the kind

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<v Speaker 1>what with the mountains and the oceans. Now, we're not

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<v Speaker 1>gonna talk about the political borders. I'm not going to

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<v Speaker 1>talk about cartographers here. We're going to talk about maps

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<v Speaker 1>of nervous systems, which is has a specific name, a

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<v Speaker 1>connect home. Now what a connect home that I don't know.

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<v Speaker 1>It's CEO in an E C T O M E.

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<v Speaker 1>What on earth is that thing? It looks like it's

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<v Speaker 1>connect to me? But it's what I was thinking. Honestly,

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<v Speaker 1>I actually watched a Ted talk and I'm just going

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<v Speaker 1>to pronounce it the same way the fellow did, so

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<v Speaker 1>connect them. What it is is, it's a a complete

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<v Speaker 1>neural map of an organism's neurons. So think of it

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<v Speaker 1>like a wiring diagram. If you were an electrician and

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<v Speaker 1>you need to have a diagram of a wiring system.

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<v Speaker 1>It's like that, except for the nervous system of an

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<v Speaker 1>actual organism. Now, to have a true connect dome, you

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<v Speaker 1>need it to be three dimensional, because we are not

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<v Speaker 1>two dimensional creatures, you know, so you wouldn't want to

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<v Speaker 1>have just a flat representation. Most of us. See, most

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<v Speaker 1>of us are are well rounded individuals. Everyone in this

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<v Speaker 1>room is, certainly, So you want to have a three

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<v Speaker 1>dimensional uh ability, you know, you want to be able

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<v Speaker 1>to depict it in three dimensions so that you have

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<v Speaker 1>an accurate look at what this nervous system is. And

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<v Speaker 1>it's supposed to show all the neurons and connections between

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<v Speaker 1>them within our an organism. So surely we have not

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<v Speaker 1>done this to an actual organism as we have. Only

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<v Speaker 1>one has a complete connect dome of all of its neurons,

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<v Speaker 1>and that would be Senor Rhabditis elegance or see elegans

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<v Speaker 1>also known as worm teeny tiny worm. Um. Yeah, it

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<v Speaker 1>only has three hundred two neurons, so yeah, not not

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<v Speaker 1>You're like totally like, I'm not impressed by this groundbreaking

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<v Speaker 1>scientific discovery. Take that scientists deal with my puff. Three

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<v Speaker 1>neurons is a tiny amount comparatively speaking, but we'll get

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<v Speaker 1>to that. So scientists began to study these worms in

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<v Speaker 1>an effort to understand neurology better, among other things. I mean,

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<v Speaker 1>of course, scientists all over the place like like worms.

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<v Speaker 1>I mean, who doesn't, but they these particular scientists were

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<v Speaker 1>interested in studying the worms to to create a connect home,

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<v Speaker 1>and they figured that this was a good place to start.

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<v Speaker 1>So back in the nineteen seventies you had a team

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<v Speaker 1>that was looking into it, and it wasn't until nineteen

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<v Speaker 1>eighties six that they were able to publish the first

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<v Speaker 1>prelimit arey connect home for this worm. And more than

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<v Speaker 1>twenty years after that you had a second team produce

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<v Speaker 1>a much more complete connect home, a more accurate representation

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<v Speaker 1>of the connect home. So it is a very slow process.

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<v Speaker 1>And that's just three neurons, right, It's not that many

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<v Speaker 1>comparatively speaking. And then this year two thou fourteen, in May,

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<v Speaker 1>another team published a paper about creating a real time

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<v Speaker 1>three D map of this worm's neural system and action,

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<v Speaker 1>meaning you could see the impulses moving up and down

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<v Speaker 1>the length of this worm as its nervous system was

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<v Speaker 1>taking over for any anything like from detecting stimuli to

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<v Speaker 1>sending the information up to the ganglia to reacting to it.

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<v Speaker 1>Uh it was. This is what's really the groundbreaking. I mean,

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<v Speaker 1>all of its groundbreaking, but this is really ground baking

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<v Speaker 1>breaking stuff. You're talking about being able to get a

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<v Speaker 1>real time view of what's going on within the nervous

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<v Speaker 1>system of an organism. I can't even imagine how one

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<v Speaker 1>would do that. Well, you don't have to imagine, because

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<v Speaker 1>someone I will tell you. Uh no, this was really

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<v Speaker 1>an interesting idea. So within this worm's nervous system, essentially

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<v Speaker 1>what's going on is neurons are passing information in the

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<v Speaker 1>form of calcium ions. So these calcium ions are passing

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<v Speaker 1>from one neuron to the next, right chemical electrochemical transaction exactly.

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<v Speaker 1>So if you go with this calcium ion, if you're

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<v Speaker 1>able to make it stand out in some way and

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<v Speaker 1>then you're able to actually capture that information, you can

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<v Speaker 1>see what's going on. So they developed some fluorescent bio markers.

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<v Speaker 1>So essentially this is just meaningless information that glows. So

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<v Speaker 1>it's it's not meant to harm the organism in any way.

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<v Speaker 1>You want it to to keep everything intact, but it

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<v Speaker 1>ends up marking those calcium in It glows specifically when

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<v Speaker 1>it binds with a calcium ion exactly, so that way,

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<v Speaker 1>when this is going on, the scientist can see it now.

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<v Speaker 1>Of cour, just making the worms have little calcium ions

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<v Speaker 1>like glow doesn't mean that they're going to be able

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<v Speaker 1>to see everything that's going on, right, I mean, you're

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<v Speaker 1>not gonna be standing there looking at a worm thinking, well,

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<v Speaker 1>there's a flash of light. It's thinking something that's That's

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<v Speaker 1>another way it works that I would have gotten to

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<v Speaker 1>be fair. To be fair, worms aren't really thinking that

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<v Speaker 1>much anyway. So if it wasn't sparking, you'd just be thinking, well,

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<v Speaker 1>we got a particularly stupid worm. Oh look, he's contemplating

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<v Speaker 1>consubjection to like this. This worm is obviously a fan

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<v Speaker 1>of Nietzchi, a scholar. I like that. We're all just

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<v Speaker 1>gonna start let's just keep on naming various thinkers. So

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<v Speaker 1>what are you going to do about that? You know,

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<v Speaker 1>if the worm doesn't think, it just disappears anyway. The

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<v Speaker 1>scientists decided that the way to capture all this information

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<v Speaker 1>because obviously if you used a regular light microscope, even

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<v Speaker 1>if it was powerful enough to detect those calcium ions

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<v Speaker 1>that are glowing, you would still only have a two

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<v Speaker 1>dimensional representation of what was happening. Right, You're not getting

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<v Speaker 1>any depth there. So how do you get the depth?

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<v Speaker 1>And that they managed to do through a technique called

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<v Speaker 1>light field deconvolution micross scope. What I know, I love

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<v Speaker 1>different kinds of microscope. I do too. No, I'm not joking.

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<v Speaker 1>I really do believe that was my cross could be

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<v Speaker 1>treats a snap crack on pop. Well no, okay, no,

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<v Speaker 1>I'm serious. So I grew up with a little, uh

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<v Speaker 1>kind of cruddy micro microscope that I would stare through.

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<v Speaker 1>I'd i'd stay in a little pain and put some

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<v Speaker 1>kind of junk on there and look at it. But

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<v Speaker 1>everything just looked like gray squiggles, you know, because it

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<v Speaker 1>was that back lip microscope we've all seen before. It's

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<v Speaker 1>you got the light coming up the tube from the

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<v Speaker 1>bottom and you see this two dimensional cross section that's

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<v Speaker 1>not very colorful. It's wonderful now that we have all

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<v Speaker 1>these techniques to see three dimensional shapes at the micro level, right,

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<v Speaker 1>and light field. This is something you guys might have

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<v Speaker 1>heard about, in fact that we may have even talked

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<v Speaker 1>about in the past. Uh, you've heard of light field photography,

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<v Speaker 1>which is what the lightrous camera uses. This is the

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<v Speaker 1>idea of upturing information by not just the fact that

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<v Speaker 1>there's light there, but capturing the direction of light, the

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<v Speaker 1>rays of light within a scene. So you're capturing all

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<v Speaker 1>the light that is within the scope of that camera. Now,

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<v Speaker 1>with a litros camera, what this means is that when

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<v Speaker 1>you take a picture, you can change the focal point

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<v Speaker 1>of that picture after it's been taken. So i have

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<v Speaker 1>Lauren standing in the foreground, and I've got Joe way

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<v Speaker 1>off in the background, and I take a picture, and

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<v Speaker 1>at first laurens and focused, Joe's not. But if I

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<v Speaker 1>tap on Joe, he suddenly becomes in focus and Lauren

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<v Speaker 1>goes out of focus, and it's it's dynamic. I can change.

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<v Speaker 1>I can pick the focal point within the picture and

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<v Speaker 1>change it because it's captured all of the information, as

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<v Speaker 1>opposed to your classic photography, which is stuck with whatever

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<v Speaker 1>the focal point of the lens was at the moment

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<v Speaker 1>you took the picture. Sure um in the specific case,

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<v Speaker 1>of this light field. Deconvolution microscopy the the ideas that

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<v Speaker 1>you're using, rather than the single lens that Joe is

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<v Speaker 1>talking about through traditional microscopy, an array of lenses that

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<v Speaker 1>refract and collect a single point of light, thereby letting

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<v Speaker 1>you get a good idea of where that little blip

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<v Speaker 1>is in three dimensional space. Right, So now you've got

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<v Speaker 1>this more complete look at all these different blips within

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<v Speaker 1>this three dimensional space while you do next, Well, that

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<v Speaker 1>doesn't really help you out that much unless you then

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<v Speaker 1>send it to a computer running special software which is

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<v Speaker 1>taking all this information, crunching it and then forming a

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<v Speaker 1>three dimensional map of that worm's nervous system and then

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<v Speaker 1>showing the actual activity via the little florescent calcium ions.

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<v Speaker 1>While the worm, you know, does normal worms stuff or

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<v Speaker 1>um or abnormal worms, or or is it supposed to

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<v Speaker 1>a to a sort or a smell of some kind.

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<v Speaker 1>So yeah, if you were to give some kind of

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<v Speaker 1>stimuli that ends up making the worm react in some way, right,

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<v Speaker 1>it's it's exposed to a book of Nietzsche that has

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<v Speaker 1>decomposing pages that can eat. That would be probably the

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<v Speaker 1>best kind of Nietici for this particular type of worm.

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<v Speaker 1>But yes, so you're exactly right. You would be able

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<v Speaker 1>to see by the reaction in the nervous system what

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<v Speaker 1>is going on inside the worm. Not necessarily understand it,

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<v Speaker 1>but you would be able to see it. You know,

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<v Speaker 1>keep in mind these are this is an important step,

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<v Speaker 1>but it's just a step. Well, we'll go into more

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<v Speaker 1>about the implications in a little bit, but this complex

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<v Speaker 1>software has to crunch all that put up the map,

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<v Speaker 1>and it's more or less it's it's on a millisecond scale.

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<v Speaker 1>It's something like fifty images per second, which is pretty cool, yeah,

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<v Speaker 1>but still not if you think of time as resolution

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<v Speaker 1>in the sense it's a low resolution image. So it's

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<v Speaker 1>not resolution in the sense of of how many pixels

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<v Speaker 1>or whatever, or how sharp the picture is, but rather

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<v Speaker 1>how how accurate is it over time? And it's a

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<v Speaker 1>time scale that's too slow for it to be uh,

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<v Speaker 1>you know, it's not advanced enough for it for us

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<v Speaker 1>to call it true real time. It's really good. But

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<v Speaker 1>I think what we're going to see is we're going

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<v Speaker 1>to see more development in computer software and this technology

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<v Speaker 1>where we'll start to have an even uh more elongated

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<v Speaker 1>time scale, will get down to like nanosecond level. It's

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<v Speaker 1>which will be especially important for things more complex than worms. Now,

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<v Speaker 1>in order to do all that number crunching, you can't

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<v Speaker 1>just use one computer because it actually is so um

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<v Speaker 1>intense that they have to use grid computing for it.

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<v Speaker 1>So they're using using multiple computers to solve problems in parallel,

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<v Speaker 1>so each computer is taking a little bit of the work.

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<v Speaker 1>And we've talked about this in the past two about

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<v Speaker 1>how you use a distributed computing system in order to

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<v Speaker 1>divide up a big task into lots of smaller tasks,

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<v Speaker 1>which makes it more manageable. Otherwise you would have to

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<v Speaker 1>have the world's fastest computer, and even that might be

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<v Speaker 1>slower than a grid of computers working in this way.

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<v Speaker 1>But as long as your problems don't have to be

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<v Speaker 1>solved sequentially like that, that solving number two doesn't depend

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<v Speaker 1>on solving number one, you can split them up, right.

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<v Speaker 1>So that's pretty awesome, this idea of being able to

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<v Speaker 1>see what's going on with the neurons inside a worm.

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<v Speaker 1>But um, what else could we look at? So so

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<v Speaker 1>three neurons, yeah, what about something that has like a

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<v Speaker 1>hundred billion neurons or conservatively eighty billion neurons? Okay, Uh,

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<v Speaker 1>that's a tall order because this this this team the

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<v Speaker 1>best that they've done beyond the worm, and not to

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<v Speaker 1>I'm not trying to suggest that they haven't done their work.

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<v Speaker 1>This is yeah. But the other thing they did was

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<v Speaker 1>they imaged about half of a zebra fish's neurons larva's

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<v Speaker 1>larva's thank you. Yes, the zebrafish lava not an actual

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<v Speaker 1>zebra fish, but the zebra fish larva and the zebrafish

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<v Speaker 1>lava have about ten thousand neurons, so they were able

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<v Speaker 1>to image five thousand of those. Um And there's some

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<v Speaker 1>other examples of organisms that we've managed to create partial connectomes,

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<v Speaker 1>for example, the retina's and visual cortex of a mouse.

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<v Speaker 1>Well those have been completed or mostly complete, I shouldn't

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<v Speaker 1>say completed, partially completed. When you're talking about humans, it's

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<v Speaker 1>eighty two hundred billion neurons, something like a hundred trill

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<v Speaker 1>allion synapses connecting all those neurons and various configurations. Orders

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<v Speaker 1>of magnitude does not do this justice, right. You can't

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<v Speaker 1>just say it's a couple of orders of magnitude. No,

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<v Speaker 1>it's it's an enormous jump from worm to a person

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<v Speaker 1>for most people anyway, would it be fair to say

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<v Speaker 1>that the problem represented by doing the same kind of

0:12:20.559 --> 0:12:22.280
<v Speaker 1>thing with a brain on the scale of a human

0:12:22.320 --> 0:12:25.840
<v Speaker 1>brain is not just like a scaling up problem. It's

0:12:25.840 --> 0:12:29.600
<v Speaker 1>like it's another ballpark entirely. It's not like we just

0:12:29.679 --> 0:12:32.120
<v Speaker 1>need a little more power. It's like we're not We're

0:12:32.120 --> 0:12:34.840
<v Speaker 1>not even close. Yeah, I mean, the closest we've got

0:12:35.040 --> 0:12:37.960
<v Speaker 1>is is working our way towards building a connect them.

0:12:38.040 --> 0:12:42.960
<v Speaker 1>Not a real time three dimensional moving picture, right, you know,

0:12:43.040 --> 0:12:44.839
<v Speaker 1>the one with the worm you're actually able to watch

0:12:45.880 --> 0:12:48.320
<v Speaker 1>it's not thought, but you're able to watch impulses as

0:12:48.320 --> 0:12:51.600
<v Speaker 1>they move throughout the worm's body. With the humans, the

0:12:51.600 --> 0:12:55.760
<v Speaker 1>closest we're getting is the Human Connectome project is the

0:12:55.800 --> 0:12:58.000
<v Speaker 1>best known one. But this is just to create that

0:12:58.160 --> 0:13:02.200
<v Speaker 1>neural map. And I think it's something like participants for

0:13:02.320 --> 0:13:05.560
<v Speaker 1>the entire program for this particular approach. And this is

0:13:05.600 --> 0:13:09.559
<v Speaker 1>a five year mission, which is all of our Star

0:13:09.600 --> 0:13:12.120
<v Speaker 1>Trek fans out there, which began in two thousand nine,

0:13:12.200 --> 0:13:15.640
<v Speaker 1>July two thousand nine, So we're getting up there kissing

0:13:15.640 --> 0:13:20.720
<v Speaker 1>the connect home. So at any rate, Uh, this is

0:13:21.120 --> 0:13:25.599
<v Speaker 1>a collaboration between a few different um research facilities, the

0:13:25.960 --> 0:13:29.200
<v Speaker 1>two main ones being the Laboratory of neuro Image Imaging

0:13:29.400 --> 0:13:31.560
<v Speaker 1>at u c l A and the Martino Center for

0:13:31.640 --> 0:13:36.760
<v Speaker 1>Biomedical Imaging at Massachusetts General Hospital and UH it's sponsored

0:13:37.240 --> 0:13:40.600
<v Speaker 1>in part by the National Institutes of Health, and so

0:13:40.640 --> 0:13:43.720
<v Speaker 1>this is an interesting approach again trying to create a

0:13:43.760 --> 0:13:46.680
<v Speaker 1>neural map of human beings. It's not again, it's not

0:13:46.920 --> 0:13:50.440
<v Speaker 1>a real time representation, but just to actually map out

0:13:50.480 --> 0:13:55.000
<v Speaker 1>all the neurons and synapses at alone is an incredibly

0:13:55.080 --> 0:14:00.320
<v Speaker 1>huge task. So they had to start commissioning new types

0:14:00.400 --> 0:14:03.160
<v Speaker 1>of technology and software to be able to try and

0:14:03.200 --> 0:14:06.760
<v Speaker 1>tackle this problem, and one of those was a super

0:14:06.800 --> 0:14:10.640
<v Speaker 1>scanner from Siemens, and Siemens makes all sorts of different

0:14:10.960 --> 0:14:15.160
<v Speaker 1>UH high grade technology equipment. In this case, they were

0:14:15.240 --> 0:14:19.320
<v Speaker 1>using a m r I that did does three different

0:14:19.360 --> 0:14:21.440
<v Speaker 1>types of m r I in one go. It does

0:14:21.480 --> 0:14:26.640
<v Speaker 1>a diffusion tensor imaging, diffusion spectrum imaging, and high angular

0:14:26.760 --> 0:14:31.560
<v Speaker 1>resolution diffusion imaging also known as HARDY, So all of

0:14:31.560 --> 0:14:34.240
<v Speaker 1>that's different types of magnetic resonance and imaging. Mri I

0:14:34.360 --> 0:14:37.240
<v Speaker 1>type stuff does it so that it can get the

0:14:37.280 --> 0:14:43.040
<v Speaker 1>most um complete picture of a person's nervous system. And

0:14:43.240 --> 0:14:47.400
<v Speaker 1>UH the literature that is available on the human Connectome

0:14:47.440 --> 0:14:49.800
<v Speaker 1>Projects shows pictures of these things and they do look

0:14:49.840 --> 0:14:52.040
<v Speaker 1>like something straight out of you know, Star Trek or

0:14:52.080 --> 0:14:55.120
<v Speaker 1>Star Wars or something like that. Uh, And they talk

0:14:55.200 --> 0:14:59.720
<v Speaker 1>about how this this equipment, while it's very useful for

0:14:59.720 --> 0:15:03.720
<v Speaker 1>this regular project, will also be incredibly useful for numerous

0:15:03.760 --> 0:15:07.120
<v Speaker 1>medical applications from this point forward. So it's not like

0:15:07.200 --> 0:15:09.520
<v Speaker 1>this was a piece of equipment that was made for

0:15:09.560 --> 0:15:13.560
<v Speaker 1>this one and only one thing and after that, you know, that's, well,

0:15:13.880 --> 0:15:17.760
<v Speaker 1>that was cool. It's actually gonna be useful long after

0:15:18.120 --> 0:15:21.760
<v Speaker 1>this project has concluded, so that's kind of cool. Um. Now,

0:15:21.760 --> 0:15:24.840
<v Speaker 1>on the computer side, they're also using grid computing to

0:15:25.000 --> 0:15:27.640
<v Speaker 1>run all this stuff and make sure that they can

0:15:27.920 --> 0:15:31.080
<v Speaker 1>process all the information in order to actually come up

0:15:31.120 --> 0:15:34.400
<v Speaker 1>with the results. So you know, you think about it,

0:15:34.440 --> 0:15:38.600
<v Speaker 1>if you're looking to map eighty to a hundred billion

0:15:39.480 --> 0:15:43.720
<v Speaker 1>uh little tiny neurons, like you need to map all

0:15:43.760 --> 0:15:47.840
<v Speaker 1>of those and then all their connections, even if it's not,

0:15:48.520 --> 0:15:51.040
<v Speaker 1>you know, as complex as you would first imagine, just

0:15:51.120 --> 0:15:54.280
<v Speaker 1>the number alone means that that's a lot of data.

0:15:54.840 --> 0:15:56.600
<v Speaker 1>So that's why you have to have this kind of

0:15:56.640 --> 0:15:59.360
<v Speaker 1>grid computing approach or else it just wouldn't work. So

0:15:59.400 --> 0:16:02.680
<v Speaker 1>that's kind of interesting. I'd like to, you know, read

0:16:02.760 --> 0:16:05.440
<v Speaker 1>up on it more. Um, there is a website for

0:16:05.480 --> 0:16:08.640
<v Speaker 1>the Human Connect Home project. It is not perhaps the

0:16:08.640 --> 0:16:13.360
<v Speaker 1>most flashy or um sophisticated website I've ever seen, but

0:16:13.400 --> 0:16:16.360
<v Speaker 1>it's certainly effective. So I am interested to see what

0:16:17.160 --> 0:16:19.880
<v Speaker 1>I mean. I was able to understand everything, so that

0:16:19.880 --> 0:16:22.880
<v Speaker 1>that was good. There wasn't a little image of a

0:16:22.920 --> 0:16:26.280
<v Speaker 1>man in a construction hat saying, you know, like, website

0:16:26.360 --> 0:16:28.120
<v Speaker 1>under development, so that was good. But there was a

0:16:28.200 --> 0:16:31.920
<v Speaker 1>dancing baby. There was a midi of a sa base.

0:16:32.040 --> 0:16:35.160
<v Speaker 1>I don't know why. No, I'm just kidding. I'm just kidding.

0:16:35.840 --> 0:16:38.800
<v Speaker 1>But yeah, I'll be really curious to see what all

0:16:38.880 --> 0:16:41.880
<v Speaker 1>the the outcomes are of this project when it concludes.

0:16:41.960 --> 0:16:45.480
<v Speaker 1>I am assuming they're going to publish a lot. So

0:16:45.560 --> 0:16:49.080
<v Speaker 1>I want to play Devil's advocate because, as you all know,

0:16:49.240 --> 0:16:54.800
<v Speaker 1>I like all I'm someone who likes building weird things

0:16:54.840 --> 0:16:58.880
<v Speaker 1>that don't have an obvious benefit to the lay person,

0:16:59.360 --> 0:17:07.160
<v Speaker 1>or sometimes an obvious detriment. Crunch kicking robot was that

0:17:07.280 --> 0:17:12.480
<v Speaker 1>was that? It was? I didn't know alright, no, no, no no, okay, no,

0:17:12.640 --> 0:17:15.119
<v Speaker 1>imagine I'm the lay person who says, what are you

0:17:15.200 --> 0:17:17.800
<v Speaker 1>going to use that for all right, Well, that's a

0:17:17.800 --> 0:17:20.800
<v Speaker 1>good question. What difference does it make. I mean, it's

0:17:20.840 --> 0:17:25.359
<v Speaker 1>a worm brain, so from just going from the worm

0:17:25.440 --> 0:17:27.200
<v Speaker 1>so we're not even talking about human connect Home project.

0:17:27.240 --> 0:17:30.159
<v Speaker 1>We're just talking about that three dimensional look at the

0:17:30.200 --> 0:17:33.159
<v Speaker 1>worm's nervous system. It tells us more about how nervous

0:17:33.160 --> 0:17:35.840
<v Speaker 1>systems in general work. This is an area that we

0:17:35.880 --> 0:17:38.680
<v Speaker 1>know very little about in the grand scheme of things

0:17:38.720 --> 0:17:41.119
<v Speaker 1>we know. What we know is that we don't know

0:17:41.200 --> 0:17:44.560
<v Speaker 1>a lot, right. We do understand some things. We understand

0:17:44.560 --> 0:17:47.879
<v Speaker 1>some of the very basic things that are happening, but

0:17:47.960 --> 0:17:50.960
<v Speaker 1>we don't understand how they're working necessarily across an entire

0:17:51.040 --> 0:17:54.240
<v Speaker 1>system all at once. The way I'd phrase it, though

0:17:54.320 --> 0:17:57.199
<v Speaker 1>obviously I'm no neuroscientists, the way I understand it is

0:17:57.240 --> 0:18:00.560
<v Speaker 1>that we have a lot of observations, but we don't

0:18:00.560 --> 0:18:03.480
<v Speaker 1>know what they mean. And this is another step getting

0:18:03.560 --> 0:18:08.520
<v Speaker 1>closer towards understanding the meaning behind what we've observed. Doesn't

0:18:08.520 --> 0:18:11.479
<v Speaker 1>mean that we're obviously that the lightbulb has come on

0:18:11.560 --> 0:18:14.760
<v Speaker 1>and now everything makes sense, but it's the only way

0:18:14.800 --> 0:18:17.760
<v Speaker 1>for us to get to that understanding is to continue

0:18:17.800 --> 0:18:21.600
<v Speaker 1>making observations and to continue doing experimentation in order to

0:18:22.200 --> 0:18:24.640
<v Speaker 1>really get an idea of what's going on and why

0:18:24.800 --> 0:18:27.639
<v Speaker 1>it's happening the way it is. And the closer we

0:18:27.680 --> 0:18:30.960
<v Speaker 1>get to mapping out nervous systems, the more will understand

0:18:31.000 --> 0:18:34.919
<v Speaker 1>everything from basic functions of a healthy uh, you know,

0:18:35.040 --> 0:18:39.560
<v Speaker 1>human brain to any sort of dysfunctional activity. So whether

0:18:39.600 --> 0:18:42.600
<v Speaker 1>that's from brain damage or from any of a number

0:18:42.680 --> 0:18:46.520
<v Speaker 1>of neurological diseases or disorders, or or or even from

0:18:46.520 --> 0:18:48.959
<v Speaker 1>from mental health disorders. Yeah, I've seen a lot of

0:18:49.119 --> 0:18:51.480
<v Speaker 1>discussion about the Human connect On project. One of its

0:18:51.480 --> 0:18:55.520
<v Speaker 1>goals is to study things like, uh, people who suffer

0:18:55.600 --> 0:18:59.719
<v Speaker 1>from schizophrenia and to see what what is different about

0:18:59.760 --> 0:19:02.720
<v Speaker 1>their nervous systems compared to someone who does not suffer

0:19:02.760 --> 0:19:07.760
<v Speaker 1>from schizophrenia, and perhaps through understanding these sort of dysfunctions,

0:19:07.800 --> 0:19:10.760
<v Speaker 1>we might be able to address them or perhaps even

0:19:10.800 --> 0:19:15.320
<v Speaker 1>cure them in the future. Now granted all of that. Yeah,

0:19:15.359 --> 0:19:18.440
<v Speaker 1>and when you say in the future, you're talking about

0:19:18.480 --> 0:19:20.919
<v Speaker 1>a good distance into the future. We're not talking about

0:19:20.960 --> 0:19:23.159
<v Speaker 1>something that's going to turn around and happen within the

0:19:23.200 --> 0:19:27.640
<v Speaker 1>next six months. I want to be clear about that, because, yeah,

0:19:27.960 --> 0:19:30.280
<v Speaker 1>we mean whenever you read these reports. You know, people

0:19:30.520 --> 0:19:33.480
<v Speaker 1>like the people who write the reports. Not everybody, but

0:19:33.600 --> 0:19:36.879
<v Speaker 1>some people fall into that kind of easy journalism trap

0:19:36.960 --> 0:19:41.040
<v Speaker 1>where you say this will lead to curing mental illness.

0:19:42.520 --> 0:19:44.800
<v Speaker 1>It's it's possible that let me put it this way,

0:19:44.880 --> 0:19:46.720
<v Speaker 1>if we don't do these steps, it will be a

0:19:46.760 --> 0:19:49.800
<v Speaker 1>lot harder to do it. But this, this may or

0:19:49.840 --> 0:19:52.479
<v Speaker 1>may not lead us down that pathway, but certainly if

0:19:52.480 --> 0:19:55.520
<v Speaker 1>we don't do the work, we won't get there. So

0:19:56.320 --> 0:20:00.240
<v Speaker 1>another thing that could possibly happen way off into the future, sure,

0:20:00.280 --> 0:20:03.920
<v Speaker 1>is that by understanding more about how these neural pathways

0:20:03.920 --> 0:20:08.840
<v Speaker 1>work in organisms, we could create a synthetic creature, whether

0:20:08.920 --> 0:20:14.159
<v Speaker 1>that's biological or whether it's compurely electronic. We might be

0:20:14.200 --> 0:20:18.159
<v Speaker 1>able to create a synthetic intelligence by learning more about

0:20:18.200 --> 0:20:21.720
<v Speaker 1>how our intelligence works, or or build that that brain

0:20:21.800 --> 0:20:24.920
<v Speaker 1>like computer that we were talking about a few episodes ago. Yeah,

0:20:25.119 --> 0:20:28.320
<v Speaker 1>so that's another thing that's a possibility. Again, it is

0:20:28.359 --> 0:20:31.560
<v Speaker 1>not necessarily true that what we're doing today or what

0:20:31.640 --> 0:20:34.800
<v Speaker 1>these scientists are doing today, will be a direct path

0:20:34.920 --> 0:20:37.960
<v Speaker 1>from A to B that leads there, but it could

0:20:38.080 --> 0:20:42.240
<v Speaker 1>very well be. That's an important step along the journey. So, uh,

0:20:42.400 --> 0:20:45.480
<v Speaker 1>you know, I always hate making big predictions and saying, oh,

0:20:45.720 --> 0:20:48.120
<v Speaker 1>and in ten years, we're going to have these thinking

0:20:48.160 --> 0:20:50.399
<v Speaker 1>computers and mental mental illness will be a thing of

0:20:50.440 --> 0:20:54.600
<v Speaker 1>the past. I certainly hope that is true. But what

0:20:54.720 --> 0:20:58.159
<v Speaker 1>I am really excited about is seeing more developments in

0:20:58.160 --> 0:21:01.280
<v Speaker 1>this kind of research and finding out what else we learned,

0:21:01.640 --> 0:21:05.159
<v Speaker 1>because again, as we've said multiple times on this podcast,

0:21:05.760 --> 0:21:07.879
<v Speaker 1>learning is great. I mean, the more you learn, the

0:21:08.240 --> 0:21:11.800
<v Speaker 1>better off you are. And so, uh, we were fully

0:21:11.840 --> 0:21:13.639
<v Speaker 1>in favor of it, even if it is something as

0:21:13.640 --> 0:21:16.960
<v Speaker 1>silly as mapping out a worms nervous system and learning

0:21:16.960 --> 0:21:19.000
<v Speaker 1>that it doesn't have a taste for Nietzsche, even if

0:21:19.040 --> 0:21:23.119
<v Speaker 1>the book is rotting so at any rate, Uh, just

0:21:23.160 --> 0:21:27.479
<v Speaker 1>those chemical treated pages there, I tell you. You know,

0:21:27.920 --> 0:21:30.600
<v Speaker 1>the odd thing was totally went for the Kindle version.

0:21:31.040 --> 0:21:34.520
<v Speaker 1>So uh yeah, that wraps up this discussion on just

0:21:34.600 --> 0:21:37.639
<v Speaker 1>the mapping of the nervous system. It's a really cool project.

0:21:37.920 --> 0:21:41.280
<v Speaker 1>Go ahead and check out the Human connect Home project

0:21:41.280 --> 0:21:43.760
<v Speaker 1>and also look around at some of the stories about

0:21:43.840 --> 0:21:47.159
<v Speaker 1>the the various projects that have gone into trying to

0:21:47.240 --> 0:21:49.439
<v Speaker 1>map out this worm's nervous system. I mean that that

0:21:49.520 --> 0:21:52.679
<v Speaker 1>alone is interesting. I mean, imagine they started in the seventies,

0:21:52.840 --> 0:21:55.280
<v Speaker 1>and it wasn't until they were able to publish the

0:21:55.280 --> 0:21:59.680
<v Speaker 1>first rudimentary connect home that just with three two neurons.

0:21:59.720 --> 0:22:03.760
<v Speaker 1>It just proves how complex this is. And then you know,

0:22:04.040 --> 0:22:08.080
<v Speaker 1>again that leap to human big leap for most people.

0:22:08.359 --> 0:22:11.280
<v Speaker 1>I know a few people who comparison to worms not

0:22:11.440 --> 0:22:14.480
<v Speaker 1>that off base. Alright, So guys, if you have any

0:22:14.480 --> 0:22:17.800
<v Speaker 1>suggestions for future episodes a Forward Thinking, I'm not looking

0:22:17.800 --> 0:22:19.760
<v Speaker 1>at anyone in this room. I just love how you

0:22:19.760 --> 0:22:22.600
<v Speaker 1>always pepper our optimistic view of the future with some

0:22:22.680 --> 0:22:25.840
<v Speaker 1>really antisocial commentary. I made the same I made the

0:22:25.880 --> 0:22:29.440
<v Speaker 1>same joke in the video so um, but I've never

0:22:29.440 --> 0:22:33.480
<v Speaker 1>stopped me before, right, I'll tell the same jokelations. Thank you.

0:22:33.600 --> 0:22:35.639
<v Speaker 1>I have a very specific role to fill in this

0:22:35.680 --> 0:22:39.120
<v Speaker 1>world now. If you have any suggestions for future episodes

0:22:39.160 --> 0:22:42.320
<v Speaker 1>off Forward Thinking, if you want to hear more about

0:22:42.560 --> 0:22:46.120
<v Speaker 1>the amazing brain, or there's just something else that really

0:22:46.160 --> 0:22:48.040
<v Speaker 1>fascinates you and you wonder what it's going to be

0:22:48.080 --> 0:22:50.159
<v Speaker 1>like in the future, you should let us know and

0:22:50.200 --> 0:22:52.840
<v Speaker 1>we will be happy to tackle that subject. You can

0:22:53.080 --> 0:22:56.960
<v Speaker 1>contact us through Twitter or Facebook or Google Plus are

0:22:56.960 --> 0:22:59.480
<v Speaker 1>handled all three f W Thinking, or you can send

0:22:59.480 --> 0:23:02.879
<v Speaker 1>a good Ole Fashion Email That address is FW thinking

0:23:03.040 --> 0:23:06.159
<v Speaker 1>at discovery dot com and we'll talk to you again

0:23:06.480 --> 0:23:13.399
<v Speaker 1>really soon. For more on this topic and the future

0:23:13.400 --> 0:23:27.479
<v Speaker 1>of technology, visit forward thinking dot com, brought to you

0:23:27.520 --> 0:23:29.800
<v Speaker 1>by Toyota. Let's Go Places,