On Tue, Oct 25, 2011 at 10:17:24AM -0700, BGB wrote: > I was not arguing about the limits of computing, rather, IBM's specific > design. > it doesn't really realistically emulate real neurons, rather it is a
Real neurons have many features, many of them unknown, and do not map well into solid state as is. However, you can probably find a simplified model which is powerful and generic enough and maps well to solid state by co-evolving substrate and representation. > from what I can gather a simplistic "accumulate and fire" model, with > the neurons hard-wired into a grid. In principle you can use a hybrid model by using a crossbar for local connectivity which is analog, and a packet-switched signalling mesh for long-range interactions, similiarly as real neurons do it. The mesh can emulate total connectivity fine, and you can probably even finagle something which scales better than a crossbar locally. > I suspect something more "generic" would be needed. I don't see how generic will do long-term any than for bootstrap (above co-evolution) reasons. > another question is what can be done in the near term and on present > hardware (future hardware may or may not exist, but any new hardware may > take years to make it into typical end-user systems). Boxes with large number of ARM SoCs with embedded memory and signalling mesh have been sighted, arguably this is the way to go for large-scale. GPGPU approaches are also quite good, if you map your neurons to a 3d array and stream through memory sequentially. Exchanging interface state with adjacent nodes (which can be even on GBit Ethernet) is cheap enough. > > the second part of the question is: > assuming one can transition to a purely biology-like model, is this a > good tradeoff?... > if one gets rid of a few of the limitations of computers but gains some > of the limitations of biology, this may not be an ideal solution. Biology had never had the issue to deal with high-performance numerics, I'm sure if it had it wouldn't do too shabbily. You can always go hybrid e.g. if you want to do proofs or cryptography. > better would be to try for a strategy where the merits of both can be > gained, and as many limitations as possible can be avoided. > > most likely, this would be via a hybrid model. Absolutely. Hybrid at many scales, down to analog computation for neurons. > > or such... > -- 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 _______________________________________________ fonc mailing list [email protected] http://vpri.org/mailman/listinfo/fonc
