Return maps for various networks.
For a binary network, each state (bitstring) B1,B2,B3,...Bn
is converted into a decimal number M (0-2) as follows,
B1*1 + B2*(1/2) + B3*(1/4) + ... + Bn*(1/(2^n)).
For a mutli-value network, each term is divided by v-1 for an equivalent result.
As the network is itterated, M_{t} the value of the state at
timestep t (x-axis)
is plotted against M_{t+1} the value of the state at timestep t+1 (y-axis).
Note the fractal structure of the resulting trajectories
and attractors.

Fig 1a. A multi-value chain rule, k=3 v=3 CA rule (hex) 140842644a2669, n=150

Fig 1. k=3 CA rule 30, n=100

Fig 2. k=3 CA rule 22, n=100

Fig 3. k=3 CA rule 90, n=100

Fig 4. k=5 CA n=100

Fig 5. k=7 CA n=100

Fig 6. k=5 random wiring, one rule 56d8902b, n=30.
The graph shows the "input-entropy" for a moving window of
10 time-steps.

Fig 7. k=5 random wiring, one rule, totalistic 011001, n=100.
The graph shows the density of 1s in a moving window of
10 time-steps. This is an interesting rule because of the
unpredictable bi-stability.

Fig 7. k=5 RBN n=100

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Last modified: Jan 2003