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Introduction to the Modeling and Analysis of Complex Systems

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202 CHAPTER 11. CELLULAR AUTOMATA I: MODELINGAssume a two-dimensional space made <strong>of</strong> cells where each cell can take ei<strong>the</strong>r a passive(0) or active (1) state. A cell becomes activated if <strong>the</strong>re are a sufficient number <strong>of</strong>active cells within its local neighborhood. However, o<strong>the</strong>r active cells outside this neighborhoodtry <strong>to</strong> suppress <strong>the</strong> activation <strong>of</strong> <strong>the</strong> focal cell with relatively weaker influencesthan those from <strong>the</strong> active cells in its close vicinity. These dynamics are called shortrangeactivation <strong>and</strong> long-range inhibition. This model can be described ma<strong>the</strong>maticallyas follows:∣ }N a ={x ′ ∣∣ |x ′ | ≤ R a (11.4)∣ }N i ={x ′ ∣∣ |x ′ | ≤ R i (11.5)∑∑a t (x) = w a s t (x + x ′ ) − w i s t (x + x ′ ) (11.6)x ′ ∈N a x ′ ∈N i{ 1 if at (x) > 0,s t+1 (x) =(11.7)0 o<strong>the</strong>rwise.Here, R a <strong>and</strong> R i are <strong>the</strong> radii <strong>of</strong> neighborhoods for activation (N a ) <strong>and</strong> inhibition (N i ), respectively(R a < R i ), <strong>and</strong> w a <strong>and</strong> w i are <strong>the</strong> weights that represent <strong>the</strong>ir relative strengths.a t (x) is <strong>the</strong> result <strong>of</strong> two neighborhood countings, which tells you whe<strong>the</strong>r <strong>the</strong> short-rangeactivation wins (a t (x) > 0) or <strong>the</strong> long-range inhibition wins (a t (x) ≤ 0) at location x. Figure11.10 shows <strong>the</strong> schematic illustration <strong>of</strong> this state-transition function, as well as asample simulation result you can get if you implement this model successfully.Exercise 11.6 Implement a CA model <strong>of</strong> <strong>the</strong> Turing pattern formation in Python.Then try <strong>the</strong> following:• What happens if R a <strong>and</strong> R i are varied?• What happens if w a <strong>and</strong> w i are varied?Waves in excitable media Neural <strong>and</strong> muscle tissues made <strong>of</strong> animal cells can generate<strong>and</strong> propagate electrophysiological signals. These cells can get excited in response <strong>to</strong>external stimuli coming from nearby cells, <strong>and</strong> <strong>the</strong>y can generate action potential across<strong>the</strong>ir cell membranes that will be transmitted as a stimulus <strong>to</strong> o<strong>the</strong>r nearby cells. Onceexcited, <strong>the</strong> cell goes through a refrac<strong>to</strong>ry period during which it doesn’t respond <strong>to</strong> anyfur<strong>the</strong>r stimuli. This causes <strong>the</strong> directionality <strong>of</strong> signal propagation <strong>and</strong> <strong>the</strong> formation <strong>of</strong>“traveling waves” on tissues.

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