So even a nanoscale SEM pass over the whole brain wouldn’t be enough unless we could find some way to visually read off the sign of a synapse, perhaps with a stain, perhaps by learning what different types of neurons look like, perhaps by something not yet discovered?
Hayworth:
That is right, but those tell tale signs are well known for certain systems (like the retina) already, and will become more clear for others once large scale em imaging combined with functional recording becomes routine.
I would challenge him to show a “well characterized and mapped out part of the mammalian brain” that has a fraction of the detail that is known in c. elegans already. Moreover, the prospect of building a simulation requires that you can constrain the inputs and the outputs to the simulation. While this is a hard problem in c. elegans, its orders of magnitude more difficult to do well in a mammalian system.
There is still no retina connectome to work with (c. elegans has it). There are debates about cell types in retina (c. elegans has unique names for all cells). The gene expression maps of retina are not registered into a common space (c. elegans has that). The ability to do calcium imaging in retina is expensive (orders of magnitude easier in c. elegans). Genetic manipulation in mouse retina is expensive and takes months to produce specific mutants (you can feed c. elegans RNAi and make a mutant immediately).
There are methods now, along the lines of GFP (http://en.wikipedia.org/wiki/Green_fluorescent_protein) to “read the signs of synapses”. There is just very little funding interest from Government funding agencies to apply them to c. elegans. David Hall is one of the few who is pushing this kind of mapping work in c. elegans forward.
What confuses this debate is that unless you study neuroscience deeply it is hard to tell the “known unknowns” apart from the “unknown unknowns”. Biology isn’t solved, so there are a lot of “unknown unknowns”. Even with that, there are plenty of funded efforts in biology and neuroscience to do simulations. However, in c. elegans there are likely to be many fewer “unknown unknowns” because we have a lot more comprehensive data about its biology than we do for any other species.
Building simulations of biological systems helps to assemble what you know, but can also allow you to rationally work with the “known unknowns”. The “signs of synapses” is an example of known unknowns—we can fit those into a simulation engine without precise answers today and fill them in tomorrow. The statement that no one should start simulating the worm based on the current data has no merit when you consider that there is a lot to be done just to get to a framework that has the capacity to organize the “known unknowns” so that we can actually do something useful with them once they have them. More importantly, it makes the gaps a lot more clear. Right now, in the absence of any c. elegans simulations, data are being generated without a focused purpose of feeding into a global computational framework of understanding c. elegans behavior. I would argue that the field would be much better off collecting data in the context of adding to the gaps of a simulation, rather than everyone working at cross purposes.
That’s why we are working on this challenge of building not just a c. elegans simulations, but a general framework for doing so, over at the Open Worm project (http://openworm.googlecode.com).
Further exchange:
Me:
Hayworth:
I would respectfully disagree with Dr. Hayworth.
I would challenge him to show a “well characterized and mapped out part of the mammalian brain” that has a fraction of the detail that is known in c. elegans already. Moreover, the prospect of building a simulation requires that you can constrain the inputs and the outputs to the simulation. While this is a hard problem in c. elegans, its orders of magnitude more difficult to do well in a mammalian system.
There is still no retina connectome to work with (c. elegans has it). There are debates about cell types in retina (c. elegans has unique names for all cells). The gene expression maps of retina are not registered into a common space (c. elegans has that). The ability to do calcium imaging in retina is expensive (orders of magnitude easier in c. elegans). Genetic manipulation in mouse retina is expensive and takes months to produce specific mutants (you can feed c. elegans RNAi and make a mutant immediately).
There are methods now, along the lines of GFP (http://en.wikipedia.org/wiki/Green_fluorescent_protein) to “read the signs of synapses”. There is just very little funding interest from Government funding agencies to apply them to c. elegans. David Hall is one of the few who is pushing this kind of mapping work in c. elegans forward.
What confuses this debate is that unless you study neuroscience deeply it is hard to tell the “known unknowns” apart from the “unknown unknowns”. Biology isn’t solved, so there are a lot of “unknown unknowns”. Even with that, there are plenty of funded efforts in biology and neuroscience to do simulations. However, in c. elegans there are likely to be many fewer “unknown unknowns” because we have a lot more comprehensive data about its biology than we do for any other species.
Building simulations of biological systems helps to assemble what you know, but can also allow you to rationally work with the “known unknowns”. The “signs of synapses” is an example of known unknowns—we can fit those into a simulation engine without precise answers today and fill them in tomorrow. The statement that no one should start simulating the worm based on the current data has no merit when you consider that there is a lot to be done just to get to a framework that has the capacity to organize the “known unknowns” so that we can actually do something useful with them once they have them. More importantly, it makes the gaps a lot more clear. Right now, in the absence of any c. elegans simulations, data are being generated without a focused purpose of feeding into a global computational framework of understanding c. elegans behavior. I would argue that the field would be much better off collecting data in the context of adding to the gaps of a simulation, rather than everyone working at cross purposes.
That’s why we are working on this challenge of building not just a c. elegans simulations, but a general framework for doing so, over at the Open Worm project (http://openworm.googlecode.com).