Eric Raimy sent me this link to another paper about DNA computing. I discussed the relevance of this sort of technology to the Gallistel-King Conjecture (G-KC) before (here). The fact that this sort of work seems to be heating up is intellectually significant. How so? Well, quite often, intellectual life follows the technological cutting edge. There is a reason why the ‘mind as computer’ analogy became really big in the mid 50s (I’ll let you guess why). It won’t seem at all absurd to think that brains use large biological molecules to compute with if it turns out that we can use DNA, RNA and proteins in this way. And as this paper indicates, it seems that our ability to do so is ever increasing.
G-KC is a very bold proposal. From where we sit right now there is every reason to think that it is wrong. However, though I know very little about neuroscience beyond what my very smart friends and colleagues tell me, it does not seem at all obvious that conventional ways of thinking have really given us what we need (see here). We don’t really know how spike trains carry information, we don’t know how to scale up neural nets so that they are feasible computational devices, we don’t know how to code the kinds of things that behavioral studies on both animals and humans indicate we need to explain mental capacities.
Moreover, behavioral studies provide overwhelming evidence for the claim that what we compute in a pretty conventional way; we can manipulate variables, bind them in various ways, provide a wide range of values for them, and do this very systematically. The Fodor-Pylyshyn and Marcus arguments stressing this seem to me basically correct. We have no trouble modeling these capacities as standard programs (e.g. Sandiway Fong’s thesis does a fair job of this for a pretty interesting version of GB). Things get a lot hairier when we model these in neural nets.
Given this, the G-KC gains interest, especially when coupled with the constant improvement in the technology of DNA computing and G-K’s arguments concerning the physical limitations of standard neural net proposals.
Are G-K right? Who knows? But right now their idea is looking lees and less whacky. Maybe in a few years it will seem obvious. Stay tuned.
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