WORM ATLAS: An in-progress 3D reconstruction of the C. elegans connectome. Dots represent the cell bodies of neurons; long lines represent the neurons' axons and dendrites.
Image: The OpenWorm Project, image generated by neuroConstruct
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In the 1970s biologist Sydney Brenner and his colleagues began preserving tiny hermaphroditic roundworms known as Caenorhabditis elegans in agar and osmium fixative, slicing up their bodies like pepperoni and photographing their cells through a powerful electron microscope. The goal was to create a wiring diagram—a map of all 302 neurons in the C. elegans nervous system as well as all the 7,000 connections, or synapses, between those neurons. In 1986 the scientists published a near complete draft of the diagram. More than 20 years later, Dmitri Chklovskii of Janelia Farm Research Campus and his collaborators published an even more comprehensive version. Today, scientists call such diagrams "connectomes."
So far, C. elegans is the only organism that boasts a complete connectome. Researchers are also working on connectomes for the fruit fly nervous system and the mouse brain. In recent years some neuroscientists have proposed creating a connectome for the entire human brain—or at least big chunks of it. Perhaps the most famous proponent of connectomics is Sebastian Seung of the Massachusetts Institute of Technology, whose impressive credentials, TED talk, popular book, charisma and distinctive fashion sense (he is known to wear gold sneakers) have made him a veritable neuroscience rock star.
Other neuroscientists think that connectomics at such a large scale—the human brain contains around 86 billion neurons and 100 trillion synapses—is not the best use of limited resources. It would take far too long to produce such a massive map, they argue, and, even if we had one, we would not really know how to interpret it. To bolster their argument, some critics point out that the C. elegans connectome has not provided many insights into the worm's behavior. In a debate* with Seung at Columbia University earlier this year, Anthony Movshon of New York University said, "I think it's fair to say…that our understanding of the worm has not been materially enhanced by having that connectome available to us. We don't have a comprehensive model of how the worm's nervous system actually produces the behaviors. What we have is a sort of a bed on which we can build experiments—and many people have built many elegant experiments on that bed. But that connectome by itself has not explained anything."
Because a lone connectome is a snapshot of pathways through which information might flow in an incredibly dynamic organ, it cannot reveal how neurons behave in real time, nor does it account for the many mysterious ways that neurons regulate one another's behavior. Without such maps, however, scientists cannot thoroughly understand how the brain processes information at the level of the circuit. In combination with other tools, the C. elegans connectome has in fact taught scientists a lot about the worm's behavior; partial connectomes that researchers have established in the crustacean nervous system have been similarly helpful. Scientists are also learning how to make connectomes faster than before and to enhance the information they provide. Many researchers in the field summarize their philosophy like this: "A connectome is necessary, but not sufficient."
"Some people say we don't know anything about how C. elegans's brain works and I am like, 'Yes, we do!'" says Cornelia Bargmann of The Rockefeller University, who has studied the nematode for more than two decades and attended the Columbia debate. "A lot of what we know about C elegans's rapid behaviors we have learned through and with the connectome. Every time we do an experiment, we look at those wiring diagrams and use them as a starting point for generating hypotheses."
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Add CommentTypical, this new thing hasn't created any big discoveries therefore it is useless. People speaking in absolutes about complex subjects. A circuit diagram of a computer doesn't tell me what a specific program will do but saying the circuit diagram is useless as a result is idiotic. It is also one of those situations that you don't understand how relevant it is until you have it and start trying to apply it. Even if you discovered that the connectome is less important than say spike timing at least you now know something about organisation. It is similar to genetics and how our understanding of the genome revealed how important the non-coding regions are.
Reply | Report Abuse | Link to thisThanks for speaking up. This is a drive-by review of the literature, with no particular conclusions, passing as a state of affairs. I give the author opportunity to respond.
Reply | Report Abuse | Link to thisI've been following this matter for years. I'm also very interested in brain simulation.
Reply | Report Abuse | Link to thisBlue Brain Project conducted by Henry Markram is attempting to do it. Cognitive Computing leaded by ibmer Dharmendra Modha is also trying it.
However, nobody (as far as I know) has ever try to simulate C. elegans nervous system. ¿Why?
To my understanding there must be enormous advantages in achieving it. First in test computer simulations. Second in understanding C. elegans behavior.
Thanks to put light on this.
Worth it? I have no idea.
Reply | Report Abuse | Link to thisThere are limited research dollars so each discipline needs to decide how they get the most value out of those funds.
It's like space exploration. 10 billion dollars...do you spend it on a,b or c? They may all have benefits but funding for one mission means less for another.
Antonio Orbe -- that is just what we are trying to do at the OpenWorm project mentioned above. Please see openworm.org for more details. Thanks for your interest!
Reply | Report Abuse | Link to thisThe discussion which the debaters had in this situation was not nearly so black and white as "should we build a connectome or not?" Rather, the implicit debate was over whether it is useful to have such a highly detailed connectivity map as a "connectome" implies.
Reply | Report Abuse | Link to thisWhen this frame is taken, Movshon's argument clearly wins; which is to say that, as the author of this article alluded to, a static, highly detailed connectome will very quickly lose value as much of that detail changes (as would be seen in a mouse brain). Indeed, the C. Elegans "brain" is probably not a good analogy specifically because it is organized in a highly static fashion (connections are virtually identical across two different animals), which makes this issue of dynamicism much less problematic. In contrast, when trying to understand a more plastic mammalian brain, a more general, statistically reliable (but not perfectly detailed) connection map, as current tract-tracing techniques have sought to build in non-human mammals, suffers less from loss of detail due to circuit plasticity because it doesn't map out many of the connections undergoing dynamic restructing.
It is this argument, comparing a "full", highly detailed connectome to a partial connection map which is what we should actually be framing this debate about. And, when we look at the topic from this perspective, I think it is clear that Seung's argument for a detailed connectome map is flawed.
Finding software in worms
Reply | Report Abuse | Link to thisSoftware means information in memory. Any system that can learn must have software. That’s because software only exists in memory, and learning always means to modify that software. Now, in simple systems, there might not be a box labeled “memory system”, but if the system is learning you know a memory must be emerging. In any computing system with memory (almost all have it) you have to ask, what is the software system doing? This is important because in most systems it’s the software that actually solves the highest level problems and determines complex behavior.
In simple systems like a flatworm most the evolutionary design pressure will be on neuron hardware because that’s most of what there is to the system. As species complexity ramps up, there should be a simplification of their operation, because the design focus moves to a higher level of abstraction (like making big memories – to hold a bigger software system). In other words, it’s a lot more valuable to the organism to make a big memory system than to add more neuron features.
Remember that the software design doesn't come from biology. Software systems in computers don’t come from electronics either. Software is an independent system made of information (typically piped in from the outside), not particles. The genome is another kind of software system, but it’s not really made of DNA. Software is the contents of the memory, not the hardware memory system itself.
Understanding minds and genomes means understanding software systems.
Folks, you are really doing it. Not only neurons but the complete set of cells. Including interaction with the environment. I think you are in the right direction. Actually the most promising project I can imagine. I'm following you. Good luck.
Reply | Report Abuse | Link to thisi vote yes if for no other reason, it can lead to perhaps some understanding of the people who watch the fox news channel.
Reply | Report Abuse | Link to this@jsweck, biological nervous systems are not von Neumann machines and neurons aren't Turing machines. It is a completely different computational paradigm.
Reply | Report Abuse | Link to this"The genome is another kind of software system, but it’s not really made of DNA. Software is the contents of the memory, not the hardware memory system itself." not really sure what you mean by that but the genome is encoded DNA and RNA. Again, you seem to be applying the model of a PC to DNA which is not how DNA works though there are some similarities to Turing machines. Realize that biology is electro-chemical so you really have a hard time differentiating between hardware and software, which is partly why they refer to it as single entity when they call it wetware.
Modeling an entire nervous system, or organism for that matter, would be useful - if done in timeslices a few nanoseconds apart.
Reply | Report Abuse | Link to thisToo bad the tools necessary to perform this task are too costly for a single scientist to afford. This may not be the case many years hence.
Cutting edge science being affordable only via government grant is necessarily shackled and hindered.
C. Elegans is an animal with a complete nervous system and I'm gonna go out on a limb and suggest that there's something it's like to be one of these animals. I mean that it has experience, perhaps including touch/pressure, temperature and chemical discrimination; and that these experiences might involve direct sensation or maybe some kind of emotional feeling. Obviously (?) any such experice would be minimal, and probably wouldn't include any sense of self but in the search for a solution to the hard problem I'd sure like to see the researchers root around in the molecules and proteins that form this animals neurons to look for something that relates the operation of the non-motor neurons to the expression of any kind of experience.
Reply | Report Abuse | Link to thisHi RSchmidt,
Reply | Report Abuse | Link to thisSoftware means information in memory, and that’s it. This doesn’t imply any particular computational paradigm. Any computation (or no computation) can be used to stir the software pot. Software design cares about memory, not so much computation.
Wherever you have a hardware memory system, you must have software to fill it. Software is not some sort of option in computational systems – it must exist. Any system of any complexity has software running at its core, even if the people working with it don’t call it that. When stored in memory, here are some synonyms for the software: state, configuration, data, experience, etc.
I’m not applying a PC model to DNA, I am applying my definition of software, and so I’m able to differentiate the hardware of the DNA memory system from the software of the genome. The genome is information in memory, which means software. The DNA is part of the chemical hardware.
Hardware and software are never a grey area. In any computational system they both must exist separately and independently, like oil and water. The hardware/software boundary is why there is a “nature/nurture” boundary in all computational systems. All software is made of information – not chemicals. With software you create your own completely decoupled and novel structures – in effect your own universe. This is why you can create artificial universes with it.
I hope I’ve answered your questions.
The first known work by Sigmund Freud was a neurologic study of the movement of worms. When you start following a path, you never know where the first step will finally lead you.
Reply | Report Abuse | Link to thisSoftware with no computation
Reply | Report Abuse | Link to thisLet me give an example of software without computation: a book. A book has a hierarchy of hardware that makes it a kind of memory system. A book with the text removed is empty and contains no software, but keeps all of its hardware memory system. The author’s story is normally the content of that memory system. Any memory content is software. So stories in books are software entities that don’t change, and “no change” means “no computation”. Informationally, stories are like genomes, in that they are fixed blocks of information in memory. They simply have different memory system hardware containers.
You can see the separation of hardware and software simply by observing how easy it is to cross the information streams to utterly different hardware - making for example a book filled with the genome, or a DNA strand filled with your favorite story. The software doesn't care because it’s made of information.
Bye.