Copy of 3 state Neuronal Network
Each of N elements has K inputs and 0..N-1 outputs. Initial values and inputs are assigned randomly.
Element has 3 possible states: 1, 0 and -1(inhibited).
At every time step "average input" is calculated.
New value of element calculates depending on previous state and lim1,2 parameters:
__________|in| < lim1_____________lim1 <= |in|<= lim2__________________|in|>lim2
<prev>
-1.........................0...........................................0.............................................................0
0..........................0...........................................1...........................................................-1
1..........................1...........................................0.......................................................... -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!