An R-Tree is hierarchal. If an R-Tree was used to represent a non-hierarchal space (hyper-space) it becomes combinatorial and varied.
Jim Bromer On Tue, Jun 16, 2015 at 9:09 PM, J. Andrew Rogers <[email protected]> wrote: > > On Jun 16, 2015, at 3:26 PM, Dean Pomerleau <[email protected]> wrote: > In short, growing evidence supporting the importance of cortical > oscillations in neural processing suggests that this sort of analog/digital > feedback loop might be critical to how the brain works, and that such > interactions might be very hard (possible intractably hard) to model > accurately (i.e. emulation vs. merely crude simulation) on a digital > computer, in a similar way to how protein folding is intractable to model > on a digital computer. > > > > The tractability challenges of computational dynamics for brain-like > models is related to why we can’t analyze the dynamics of *any* non-trivial > physical world system. It is not coincidence that all “big data” > computation focuses solely on relationships in the electronic world and not > the physical world. > > Interestingly, computer scientists rarely notice that these software > systems do not exist until you point it out. And when you do point it out > they are at a loss to explain why. It is only “obvious” in hindsight. > > > Virtually all existing computer science is based on the manipulation of > graph-like data models and primitives. The problem is that some systems, > notably physical world systems, have relationships that are fundamentally > topological in nature. Graphs are a special, strict subset of more general > topological computing representations; it is not possible to construct a > scalable topological computational model on top of graph primitives. > > There is no computer science literature for computing on topological data > models. To the extent algorithms and data structures exist to handle basic > topological data models (e.g. R-trees), they exhibit pathological > scalability because they are shoehorned into traditional graph models. If > you want to compute on topological models at scale, you need to build a > completely new computer science stack, from the most elementary primitives > on up. And it needs to have an efficient implementation on conventional > silicon. > > > If you can directly manipulate topologies as computational constructs, > instead of graphs only, many types of computational dynamic suddenly become > *much* more tractable. In practice, the use of inappropriate algorithms and > data structures to represent topological relationships are responsible for > most intractability related to expressions of physical world system > dynamics on a computer. It just never crosses the mind of most computer > scientists working on such things and it is never discussed in computer > science curricula. > > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/24379807-653794b5> | > Modify > <https://www.listbox.com/member/?&;> > Your Subscription <http://www.listbox.com> > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
