Model of Neuronal Network
Each of N boolean elements has K inputs and 0..N-1 outputs. Initial values and inputs are assigned randomly. At every time step "average input" is calculated. In non-linear case it transforms to New value of element calculates depending on lim1 and lim2 parameters: If > lim2 OR < lim1 then res = 0 else res = 1
Net with N elements has distinct states. So any state sooner or later will be repeated, forming a loop with length L. But if L ~ , timeline looks like set of random points - it is determenistic chaos; Also we can discover flip-flop loops with L ~ 1..5 and long-period patterns. Loop with L=1 is stable and in this case animation stops. Try to find out, how these kinds of behavior depend of parameters lim1, lim2 and K!