On Thu, Apr 05, 2007 at 12:20:54PM +0200, Shane Legg wrote: > That two specific neurons are not wired together due to genetics does > not mean > that there is not wiring that is genetically determined. The brain > contains a huge > about of wiring information that comes from the genes.
If you look at how many bits are required to describe the brain structure (some 10^19, taken from those 10^17 sites, a la 100 bits each), and and those few gigabases of the genome (a mere 10^9), there's still this gap of 10^10 which is generated endogenously, including interaction with the environment. These 10^10 bits (don't get hung up on that number, you knew where it's been) did not come from the genome. > I forget the exact number, but I think something like 20% of the human > genome describes the brain. If somebody is interested in building a No, it codes for the brain tissue. That's something very different from describing the brain. See http://www.amazon.com/Birth-Mind-Creates-Complexities-Thought/dp/0465044069/ref=pd_bbs_sr_1/002-8825487-1287227?ie=UTF8&s=books&qid=1175769947&sr=8-1 for the difference. > > going for at least several decades. Simple it is not. > It is not only simple, it is completely trivial in comparison to > a hypothetical AI designed by people. The complexity in the > behaviour > comes from the neuroanatomy, not the code. > > What you said was, "the models are not complex". No, I didn't say that. What I said was "The models are not complex. The emulation part is a standard numerics package. The complexity comes directly from scans of neurons. The resulting behaviour is complex, but IMHO not hopelessly so. I'm interested in automatic optimization, which is based on feature and function abstraction, and co-evolution of machine/represenation. This is a much harder task than "mere" brute-force simulation -- however, much easier than classical AI.' You remember the thread: complexity in the code versus complexity in the data? The Blue Brain complexity is all in the data. This is very different from the classical AI, which tends to obsessionate about lots of clever algorithms, but typically does sweep the data (state) under the carpet. You can extract more complexity from fewer bits using more complex transformations, up to a point. The complex transformations take resources, and add up atomic delays so the entire evolution is slower. You can also generate complexity from completely braindead transformations on a lot of sites aligned on a regular lattice, a la crystalline computation http://people.csail.mit.edu/nhm/cc.pdf > What they are modeling IS the neuroanatomy and its behaviour. We're in violent agreement here. I wish we could get Markram or someone from his group to the Oxford seminar. -- Eugen* Leitl <a href="http://leitl.org";>leitl</a> http://leitl.org ______________________________________________________________ ICBM: 48.07100, 11.36820 http://www.ativel.com http://postbiota.org 8B29F6BE: 099D 78BA 2FD3 B014 B08A 7779 75B0 2443 8B29 F6BE ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
