why davidad wanted to do optogenetics
More details: he was planning to engineer a nematode to make neurons give off light when activating and to be light-sensitive so you can activate individual neurons with light. This lets you see which neurons fire in response to others. He wrote:
In short form, my justification for working on such a project where many have failed before me is:
- The "connectome" of C. elegans is not actually very helpful information for emulating it. Contrary to popular belief, connectomes are not the biological equivalent of circuit schematics. Connectomes are the biological equivalent of what you'd get if you removed all the component symbols from a circuit schematic and left only the wires. Good luck trying to reproduce the original functionality from that data.
- What you actually need is to functionally characterize the system's dynamics by performing thousands of perturbations to individual neurons and recording the results on the network, in a fast feedback loop with a very very good statistical modeling framework which decides what perturbation to try next.
- With optogenetic techniques, we are just at the point where it's not an outrageous proposal to reach for the capability to read and write to anywhere in a living C. elegans nervous system, using a high-throughput automated system. It has some pretty handy properties, like being transparent, essentially clonal, and easily transformed. It also has less handy properties, like being a cylindrical lens, being three-dimensional at all, and having minimal symmetry in its nervous system. However, I am optimistic that all these problems can be overcome by suitably clever optical and computational tricks. I'm a disciple of Kurzweil, and as such I'm prone to putting ridiculously near-future dates on major breakthroughs. In particular, I expect to be finished with C. elegans in 2-3 years. I would be Extremely Surprised, for whatever that's worth, if this is still an open problem in 2020.
I believe he's no longer working on this, however, and the NemaLoad project is stalled. The last update is a year ago and there haven't been any updates to the project's github page since April 2014. It does look like davidad contributed to a 2013 paper surveying methods of neural recording, but this seems to mostly be a discussion of theoretical capability based mostly on others' work than anything learned from NemaLoad experiments.
He wrote, "If I'd had $1 million seed, I wouldn't have had to cancel the project when I did..." on this Quora answer.
Uploading an animal, even one as simple as c. elegans would be very impressive. Unfortunately, we're not there yet. What the people working on Open Worm have done instead is to build a working robot based on the c. elegans and show that it can do some things that the worm can do.
The c. elegans nematode has only 302 neurons, and each nematode has the same fixed pattern. We've known this pattern, or connectome, since 1986. [1] In a simple model, each neuron has a threshold and will fire if the weighted sum of its inputs is greater than that threshold. Which means knowing the connections isn't enough: we also need to know the weights and thresholds. Unfortunately, we haven't figured out a way to read these values off of real worms. Suzuki et. al. (2005) [2] ran a genetic algorithm to learn values for these parameters that would give a somewhat realistic worm and showed various wormlike behaviors in software. The recent stories about the Open Worm project have been for them doing something similar in hardware. [3]
To see why this isn't enough, consider that nematodes are capable of learning. Sasakura and Mori (2013) [5] provide a reasonable overview. For example, nematodes can learn that a certain temperature indicates food, and then seek out that temperature. They don't do this by growing new neurons or connections, they have to be updating their connection weights. All the existing worm simulations treat weights as fixed, which means they can't learn. They also don't read weights off of any individual worm, which means we can't talk about any specific worm as being uploaded.
If this doesn't count as uploading a worm, however, what would? Consider an experiment where someone trains one group of worms to respond to stimulus one way and another group to respond the other way. Both groups are then scanned and simulated on the computer. If the simulated worms responded to simulated stimulus the same way their physical versions had, that would be good progress. Additionally you would want to demonstrate that similar learning was possible in the simulated environment.
(In a 2011 post on what progress with nematodes might tell us about uploading humans I looked at some of this research before. Since then not much has changed with nematode simulation. Moore's law looks to be doing much worse in 2014 than it did in 2011, however, which makes the prospects for whole brain emulation substantially worse.)
I also posted this on my blog.
[1] The Structure of the Nervous System of the Nematode Caenorhabditis elegans, White et. al. (1986).
[2] A Model of Motor Control of the Nematode C. Elegans With Neuronal Circuits, Suzuki et. al. (2005).
[3] It looks like instead of learning weights Busbice just set them all to +1 (excitatory) and -1 (inhibitory). It's not clear to me how they knew which connections were which; my best guess is that they're using the "what happens to work" details from [2]. Their full writeup is [4].
[4] The Robotic Worm, Busbice (2014).
[5] Behavioral Plasticity, Learning, and Memory in C. Elegans, Sasakura and Mori (2013).