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And as a result, one can conclude that any of the very wide range of computations that can be performed by practical computers can also be done by cellular automata. … For not only can it emulate any cellular automaton: it can also emulate any of a wide range of other systems, including practical computers.
The leaves range in size from under an inch to many feet.
Nearby cellular automaton rules In a range r cellular automaton the new color of a particular cell depends only on cells at most a distance r away. One can make an equivalent cellular automaton of larger range by having a rule in which cells at distance more than r have no effect. … With larger and larger ranges one can then construct closer approximations to continuous sequences of cellular automata.
Indeed, it seems that as soon as the spectrum covers any broad range of frequencies all but very large peaks tend to be completely masked, just as in everyday life a sound needs to be loud if it is to be heard over background noise. … If a sequence is basically random but contains some short-range correlations then these will lead to smooth variations in the spectrum. … To get a spectrum with a more elaborate structure requires long-range correlations—as exist in nested sequences.
Systems of Limited Size and Class 2 Behavior In the past two sections [ 2 , 3 ] we have seen two important features of class 2 systems: first, that their behavior is always eventually repetitive, and second, that they do not support any kind of long-range communication. … The answer is that the absence of long-range communication effectively forces each part of a class 2 system to behave as if it were a system of limited size.
Indeed, in the particular case of systems such as random walks, the Central Limit Theorem suggested over two centuries ago ensures that for a very wide range of underlying microscopic rules, the same continuous so-called Gaussian distribution will always be obtained. … A demonstration of the fact that for a wide range of underlying rules for each step in a random walk, the overall distribution obtained always has the same continuous form.
A characteristic feature of class 3 systems, on the other hand, is that they show long-range communication of information—so that any change made anywhere in the system will almost always eventually be communicated even to the most distant parts of the system. … Long-range communication of information is in principle possible, but it does not always occur—for any particular change is only communicated to other parts of the system if it happens to affect one of the localized structures that moves across the system.
experience with computer languages, there is already an indication that the range of systems that are universal might be somewhat broader. … And in the next chapter I will argue that for example it also occurs in a wide range of important systems that we see in nature.
Block cellular automata With a rule of the form {{1, 1}  {1, 1}, {1, 0}  {1, 0}, {0, 1}  {0, 0}, {0, 0}  {0, 1}} the evolution of a block cellular automaton with blocks of size n can be implemented using BCAEvolveList[{n_Integer, rule_}, init_, t_] := FoldList[BCAStep[{n, rule}, #1, #2]&, init, Range[t]] /; Mod[Length[init], n]  0 BCAStep[{n_, rule_}, a_, d_] := RotateRight[ Flatten[Partition[RotateLeft[a, d], n]/.rule], d] Starting with a single black cell, none of the k = 2 , n = 2 block cellular automata generate anything beyond simple nested patterns. … For k = 2 , the number of rules that conserve the total number of black cells can be computed from q = Binomial[n, Range[0, n]] as Apply[Times, q q ] . … Compressing each block into a single cell, and n steps into one, any block cellular automaton with k colors and block size n can be translated directly into an ordinary cellular automaton with k n colors and range r = n/2 .
In case (c), the following gives a list of the numbers of nodes generated up to step t : FoldList[Plus, 1, Join[{1, 4, 12, 10, -20, 6, 4}, Map[d, IntegerDigits[Range[4, t - 5], 2]]]] d[{___, 1}] = 1 d[{1, p : 0 .., 0}] := -Apply[Plus, 4 Range[Length[{p}]] - 1] + 6 d[{__, 1, p : 0 .., 0}] := d[{1, p, 0}] - 7 d[{___, p : 1 .., q : 0 ..., 1, 0}] := 4 Length[{p}] + 3 Length[{q}] + 2 d[{___, p : 1 .., 1, 0}] := 4 Length[{p}] + 2
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