(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. Credit: The OpenWorm Project, image generated by neuroConstruct)
Early birds
As soon as Brenner and his colleagues at the University of Cambridge completed the 1986 draft of the C. elegans connectome, a few things became clear. First, scientists were able to label every one of the 302 neurons as either a sensory neuron (one that collects information from the environment, such as temperature or pressure); a motor neuron that controls muscles; or an interneuron, which connects the two. Scientists had already identified some neurons as motor or sensory by destroying them with lasers and observing what abilities the worm lost or retained. With the connectome, they could categorize all of C. elegans's neurons by referencing the number and types of connections between them. On average, sensory neurons make more presynaptic connections (sites where neurons spit out chemical messages) and fewer postsynaptic connections (where neurons receive chemical messages) because sensory neurons are mainly in the business of sending information to other cells. Motor neurons show the inverse trend. Each type of neuron constituted about one third of the C. elegans nervous system. The wiring diagram also allowed scientists to immediately identify how a neuron of interest was linked to other neurons. If a researcher zapped a neuron near the worm's head and discovered that the nematode no longer inched toward food, he could look up that neuron in the connectome and see exactly how it was connected to motor neurons.
In the 1980s, as a postdoctoral student in Brenner's lab, Martin Chalfie—now at Columbia University—used the C. elegans wiring diagram to explain one of the worm's behaviors: He identified the specific neural circuits responsible for the worm's tendency to wriggle backward when poked on the head and to squirm forward when touched on the tail. "The connectome was absolutely critical," Chalfie says. "Without it, we simply would not have known which cells were connected to which." By combining the wiring diagram with evidence from previous research, Chalfie predicted that a particular set of interneurons mediated forward movement and that another was involved in backward movement. Annihilating those neurons with lasers confirmed his predictions.
In the following 25 years researchers have continued to use the C. elegans connectome to study the worm's nervous system and behavior. In combination with genetic analysis and tools that eavesdrop on electrical activity within the worm's neurons, the connectome has helped researchers understand how C. elegans responds to temperature, chemicals and mechanical stimulation as well as how the worms mate and lay eggs. Scientists have also used the connectome to discover talents no one knew the nematode possessed: X. Z. Shawn Xu of the University of Michigan identified four neurons in the worm's body that respond to light—a surprising ability for a creature that lives between grains of soil in complete darkness. "Nearly every C. elegans neuroscience study (as long as it involves behavior) benefited from this connectome," Xu wrote in an e-mail message.
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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.