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Networks and the Nervous System

00:00:06.19 My name is Daniel Colon-Ramos, I am an assistant
00:00:09.18 professor at Yale University and my lab is interested
00:00:12.22 in understanding how the architecture of structures
00:00:16.11 like the brain forms. And I spend most of my time thinking about
00:00:22.21 these questions, but today I wanted to do something a little bit
00:00:25.26 different. It actually doesn't have to do directly with research,
00:00:30.10 but I wanted to use the brain and you know, some general
00:00:34.25 musings, to basically to think about what can we learn
00:00:39.01 about the brain with regards to how we learn, how we generate
00:00:44.25 knowledge, and also how science works. So what I'm going to be doing
00:00:48.29 today, I'm going to jump into different topics. And again, this is not
00:00:52.27 a science talk, it's more like a general thought talk.
00:00:55.22 But I'm going to illustrate how we think about science and
00:01:00.15 through collaborations, through creating networks of knowledge,
00:01:04.29 we basically, as scientists, generate new knowledge. And in that,
00:01:08.10 I'm going to illustrate the importance of cooperativity and the importance
00:01:12.16 of collaboration, which is a topic that's seldom discussed
00:01:14.28 in the sciences. But is essential for creating new knowledge
00:01:19.17 and for furthering the sciences, in general.
00:01:23.17 So, the brain is made out of cells that are called neurons.
00:01:30.00 And there are more neurons in a single human brain
00:01:36.07 than there are stars in the whole Milky Way. So there are about
00:01:40.28 a hundred billion neurons. So if there are some out there that are
00:01:43.21 watching this talk, next time that you are sitting down and looking at
00:01:46.02 the sky, think that there are more neurons in your brain than there
00:01:49.14 are stars you are seeing in the skies. And basically, there are a lot
00:01:54.27 of them. And they connect in a very specific way. And they actually
00:01:59.11 underlay human behaviors, they underlay the capacity to communicate
00:02:05.06 for you to understand the stock, and the organization is critical in that
00:02:10.13 aspect. So we're interested in this question, in how we
00:02:14.23 said that the architecture of the brain develops. And my lab
00:02:19.09 in particular, pursues this question by using the nematode, C elegans,
00:02:23.13 as a model organism. C. elegans is a tiny, tiny worm. It's about the size
00:02:27.25 of a comma in a sentence. So some of you might be asking me, how
00:02:31.06 is it that you can claim that you understand how the human brain,
00:02:34.09 which is so complex, works by looking at such a tiny worm?
00:02:37.05 And I want to explain a little bit how we do that, because
00:02:40.14 this is actually how -- the way that we do it, and the way that
00:02:43.12 other colleagues in the field that use C. elegans or use other
00:02:46.10 model organisms do it, actually highlights how science is done and how
00:02:51.10 knowledge is generated. So, C. elegans as I mentioned is really
00:02:56.11 small. And the reason that we use it is because scientists have
00:03:00.16 created tools that allow us to examine questions that will otherwise
00:03:05.16 be very, very complicated to examine. Questions like how is it that
00:03:09.01 you deconstruct the architecture of a structure like the brain.
00:03:13.10 And in particular, we use C. elegans because it's the only animal
00:03:17.19 for which we know the wiring diagram of the whole nervous system.
00:03:21.00 So, somebody sat down and mapped out all of the neurons, all of the
00:03:25.17 cells that form part of the nervous system, and so we know who they're
00:03:29.20 connecting to, where they're positioned, and for many of them,
00:03:32.23 we know what behaviors they're important for. And all that knowledge
00:03:35.13 is actually very important, because we use that knowledge
00:03:38.04 to generate and create new knowledge. So why is that knowledge
00:03:41.16 important? And the reason that that knowledge is important is
00:03:43.11 because of evolution. So it's thanks to evolution that a lot of
00:03:46.15 the concepts that we find in, for example, C. elegans, they're actually
00:03:50.28 applicable to other organisms, to other animals, including ourselves.
00:03:55.06 And so much so, that in the past 10 years, 6 Nobel prizes
00:03:59.25 have been awarded to people working in C. elegans that have
00:04:02.17 made fundamental discoveries in C. elegans, which were then
00:04:05.12 widely applicable to other systems, which furthered our general
00:04:11.21 knowledge of how biology works. And they were recognized with
00:04:15.21 Nobel prizes. But the important part beyond the prize is that
00:04:18.25 because of evolution, we can use model organisms, be it
00:04:21.18 C. elegans or yeast or flies or mice, to be able to generate fundamental
00:04:26.10 knowledge that then can benefit humanity. So, that knowledge has
00:04:34.03 told us something fundamental, very important, about how the nervous
00:04:37.26 system works. Be it C. elegans or our own brains, neurons connect
00:04:43.27 to each other and cooperate to form networks. And that cooperativity
00:04:48.27 between these individual cells is what basically underlies the
00:04:52.21 power of the nervous system, the power of our own brains.
00:04:55.19 So neuron isolation is not that powerful, but in combination with
00:04:59.23 other neurons, it's capable of creating all these circuits which then
00:05:02.19 are capable of doing amazing things, like creating a concept of
00:05:06.20 reality and allowing communication. So, but one important aspect
00:05:12.25 is that the number of neurons is important, but what is more critical
00:05:19.07 than the number of the neurons is how they're organized.
00:05:22.00 So, much like having a bunch of cables does not make a computer,
00:05:26.18 having a bunch of neurons that are disorganized does not make
00:05:30.05 a nervous system. They have to be organized in a particular
00:05:32.23 way. And again, I want to bring as an example, C. elegans.
00:05:35.03 C. elegans only has 302 neurons, so that's far fewer neurons
00:05:40.05 than 100 billion neurons that you find in a single human brain.
00:05:42.19 But those 302 neurons are organized in such a way that allows
00:05:47.01 the animal to create a representation of its world to find mates,
00:05:50.18 to avoid predators, to find food. And that's what we're interested
00:05:55.22 in. We're interested in understanding how is it that those neurons
00:05:58.18 get organized, and how that organization then underlies the behavior
00:06:01.18 of the nematode. The human brain is obviously organized in a different
00:06:08.17 way, because we don't have the same pressures. The same evolutionary
00:06:11.28 pressures or the same needs as C. elegans. So we are -- our brains
00:06:15.00 are wired in a different way than the C. elegans brain, thankfully.
00:06:18.25 And one critical aspect of our brain, which is fascinating, at least to me,
00:06:23.21 is that our brains are actually wired to be wired. And what I mean
00:06:27.10 by that is that when C. elegans are born, or when reptiles are born,
00:06:32.19 they know what to do. It's almost like their behaviors are wired
00:06:36.05 and they know where to go. But when we're born, a new baby is born,
00:06:40.21 it's actually -- it looks like what Aristotle used to call a tabula rasa.
00:06:46.10 It looks like the mind is empty. It's not really empty, it's wired to achieve
00:06:51.16 something very important. And that something very important is to connect
00:06:54.22 to other humans, and to learn from other humans. That's what we
00:06:56.27 are connected to. And that capacity of our brain to connect to
00:07:01.20 other people, to be able to learn from others, to be able to
00:07:04.07 build on knowledge that was generated by others before us,
00:07:07.02 is fundamental for the sciences. So, knowledge to a certain extent,
00:07:12.29 can be considered a network of networks. Because our brain is
00:07:17.06 a network, and the knowledge that we're generating by connecting our
00:07:20.17 brains to other people is a network of networks. So, let me give you
00:07:25.08 an example of how this works in the real science world.
00:07:28.08 So, I became interested in understanding how the nervous system
00:07:33.07 of the nematode develops in embryos. But I didn't have the tools to
00:07:38.05 be able to do that, so I collaborated -- I linked my brain to
00:07:43.00 other experts that had tools that were very beneficial to my
00:07:46.29 research, and my research was beneficial to theirs.
00:07:49.12 One of them is Zhirong Bao, who is at Sloan Kettering.
00:07:52.18 He's a computer scientist turned developmental biologist.
00:07:56.16 And he developed a computer that he trained to be able to
00:08:00.10 observe, basically here you have a nematode embryo, where the nuclei
00:08:04.22 in the cells, the center of the cells, are labeled in green. And the computer
00:08:08.29 is going to basically visualize this embryo as it's developing
00:08:12.24 and it's going to keep track of every single cell, and the lineage
00:08:15.23 of every cell. So at any given time, the computer -- because we
00:08:18.27 for C. elegans, we know the lineage of every single cell, it knows
00:08:22.11 what each cell is and what it's going to become. So, you have this
00:08:26.13 tool on one hand, and on the other hand, we also collaborated,
00:08:29.22 Zhirong and I, with another scientist, he's a microscopist at the
00:08:33.18 NIH called Hari Shroff. And Hari developed a microscope that was much more
00:08:37.20 powerful than any microscope that existed at the time.
00:08:40.00 And allowed us to image the embryo continuously and seamlessly
00:08:45.01 for long periods of time without damaging the embryo. That work was
00:08:48.26 very important, and in combination with these computer tools,
00:08:51.11 allowed us to do things that we couldn't do before. So here's the
00:08:54.06 microscope at work, looking also at the development of this
00:08:57.10 embryo, but you can see it's happening much faster because
00:08:59.15 this microscope that Hari developed is 30 times faster than the
00:09:03.12 microscopes that we had before. So that all of a sudden, by linking
00:09:06.15 our brains, we were able to do more than we could have each
00:09:08.29 done individually. And this is actually very important because this
00:09:13.00 is how science is done at all levels, even if you were claiming
00:09:15.28 to be a scientist and work completely alone, you're actually
00:09:18.27 -- you're receiving knowledge from other colleagues that are
00:09:23.25 actually colleagues who could be contemporaries, or it could
00:09:26.12 be colleagues that passed away, that generated knowledge before
00:09:29.15 you came along, and you're building on that knowledge. So
00:09:32.17 in that sense, science is actually a network of networks, and it's
00:09:36.06 a community where we're continuously building on other people's
00:09:39.16 knowledge. And in our case here, we were able to then link
00:09:44.00 what Zhirong was doing, what I was doing, and what Hari was
00:09:46.28 doing, to then generate videos like the one that I'm showing you
00:09:50.04 here, of an embryo that is developing in real time, and you can see
00:09:55.09 the neurons in green, the nuclei in red. And all of a sudden,
00:09:59.23 when this embryo starts moving, you can see that we can actually
00:10:02.19 keep track of the individual cells because the microscope that Hari
00:10:06.10 developed is so efficient and so fast, and we can observe neural
00:10:11.06 developmental events that were previously inaccessible.
00:10:14.05 And in that way, answer questions that we couldn't answer
00:10:17.08 before. So I hope that with these examples, I have illustrated
00:10:21.12 to you a little bit how science works, how collaborations work,
00:10:24.15 the importance of collaborations in sciences, which is
00:10:27.14 fundamental but seldom discussed. And if you're interested
00:10:30.22 in these topics and you want to hear more, I invite you to
00:10:33.02 do a Google search of my last name with "ted blog,"
00:10:36.15 I have a blog on this topic, and I also have a Ted-x talk
00:10:39.09 that discusses in more depth, the importance of basic
00:10:43.03 research in biomedicine. Thank you.

This Talk
Speaker: Daniel Colon Ramos
Audience:
  • Student
  • Researcher
Recorded: February 2014
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Talk Overview

Can a single human brain ever understand how a human brain works? Colón-Ramos argues not – but a network of brains might! Networks are an essential part of science, whether it is at the level of single neurons connecting to form a brain, or networked brains forming a scientific community. Colón-Ramos studies neural networks in the nematode C. elegans to uncover how neurons organize and communicate with each other to form the nervous system. Using the nervous system as a metaphor, he explains how our own brains, similar to neurons, are “wired to be wired”, and how scientists connect their brains to weave new knowledge.

Speaker Bio

Daniel Colon Ramos

Daniel Colon Ramos

Associate Professor of Neuroscience and Cell Biology; Associate Professor of Neuroscience
Yale School of Medicine Continue Reading

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