Thanks for making this dialogue! I’ve been interested in the science of uploading for awhile, and I was quite excited about the various C. elegans projects when they started.
I currently feel pretty skeptical, though, that we understand enough about the brain to know which details will end up being relevant to the high-level functions we ultimately care about. I.e., without a theory telling us things like “yeah, you can conflate NMDA receptors with AMPA, that doesn’t affect the train of thought” or whatever, I don’t know how one decides what details are and aren’t necessary to create an upload.
You mention that we can basically ignore everything that isn’t related to synapses or electricity (i.e., membrane dynamics), because chemical diffusion is too long to account for the speed of cognitive reaction times. But as Tsvi pointed out, many of the things we care about occur on longer timescales. Like, learning often occurs over hours, and is sometimes not stored in synapses or membranes—e.g., in C. elegans some of the learning dynamics unfold in the protein circuits within individual neurons (not in the connections between them).[1] Perhaps this is a strange artifact of C. elegans, but at the very least it seems like a warning flag to me; it’s possible to skip over low-level details which seem like they shouldn’t matter, but end up being pretty important for cognition.
That’s just one small example, but there are many possibly relevant details in a brain… Does the exact placement of synapses matter? Do receptor subtypes matter? Do receptor kinematics matter, e.g., does it matter that NMDA is a coincidence detector? Do oscillations matter? Dendritic computation? Does it matter that the Hodgkin-Huxley model assumes a uniform distribution of ion channels? I don’t know! There are probably loads of things that you can abstract away, or conflate, or not even measure. But how can you be sure which ones are safe to ignore in advance?
This doesn’t make me bearish on uploading in general, but it does make me skeptical of plans which don’t start by establishing a proof of concept. E.g., if it were me, I’d finish the C. elegans simulation first, before moving onto to larger brains. Both because it seems important to establish that the details that you’re uploading in fact map onto the high-level behaviors that we ultimately care about, and because I suspect that you’d sort out many of the kinks in this pipeline earlier on in the project.
“The temperature minimum is reset by adjustments to the neuron’s internal signaling; this requires protein synthesis and takes several hours” and “Again, reprogramming a signaling pathway within a neuron allows experience to change the balance between attraction and repulsion.” Principles of Neural Design, page 32, under the section “Associative learning and memory.” (As far as I understand, these internal protein circuits are separate from the transmembrane proteins).
Thanks for making this dialogue! I’ve been interested in the science of uploading for awhile, and I was quite excited about the various C. elegans projects when they started.
I currently feel pretty skeptical, though, that we understand enough about the brain to know which details will end up being relevant to the high-level functions we ultimately care about. I.e., without a theory telling us things like “yeah, you can conflate NMDA receptors with AMPA, that doesn’t affect the train of thought” or whatever, I don’t know how one decides what details are and aren’t necessary to create an upload.
You mention that we can basically ignore everything that isn’t related to synapses or electricity (i.e., membrane dynamics), because chemical diffusion is too long to account for the speed of cognitive reaction times. But as Tsvi pointed out, many of the things we care about occur on longer timescales. Like, learning often occurs over hours, and is sometimes not stored in synapses or membranes—e.g., in C. elegans some of the learning dynamics unfold in the protein circuits within individual neurons (not in the connections between them).[1] Perhaps this is a strange artifact of C. elegans, but at the very least it seems like a warning flag to me; it’s possible to skip over low-level details which seem like they shouldn’t matter, but end up being pretty important for cognition.
That’s just one small example, but there are many possibly relevant details in a brain… Does the exact placement of synapses matter? Do receptor subtypes matter? Do receptor kinematics matter, e.g., does it matter that NMDA is a coincidence detector? Do oscillations matter? Dendritic computation? Does it matter that the Hodgkin-Huxley model assumes a uniform distribution of ion channels? I don’t know! There are probably loads of things that you can abstract away, or conflate, or not even measure. But how can you be sure which ones are safe to ignore in advance?
This doesn’t make me bearish on uploading in general, but it does make me skeptical of plans which don’t start by establishing a proof of concept. E.g., if it were me, I’d finish the C. elegans simulation first, before moving onto to larger brains. Both because it seems important to establish that the details that you’re uploading in fact map onto the high-level behaviors that we ultimately care about, and because I suspect that you’d sort out many of the kinks in this pipeline earlier on in the project.
“The temperature minimum is reset by adjustments to the neuron’s internal signaling; this requires protein synthesis and takes several hours” and “Again, reprogramming a signaling pathway within a neuron allows experience to change the balance between attraction and repulsion.” Principles of Neural Design, page 32, under the section “Associative learning and memory.” (As far as I understand, these internal protein circuits are separate from the transmembrane proteins).