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CNN processors can be used as [[Reaction–diffusion system|Reaction-Diffusion]] (RD) processors. RD processors are spatially invariant, topologically invariant, analog, parallel processors characterized by reactions, where two agents can combine to create a third agent, and [[diffusion]], the spreading of agents. RD processors are typically implemented through chemicals in a [[Petri dish]] (processor), light (input), and a camera (output) however RD processors can also be implemented through a multi-layer CNN processor. D processors can be used to create [[Voronoi diagrams]] and perform [[Skeletonization|skeletonisation]]. The main difference between the chemical implementation and the CNN implementation is that CNN implementations are considerably faster than their chemical counterparts and chemical processors are spatially continuous whereas the CNN processors are spatially discrete. The most researched RD processor, Belousov-Zhabotinsky (BZ) processors, has already been simulated using a four-layer CNN processors and has been implemented in a semiconductor.
Like CA, computations can be performed through the generation and propagation of signals that either grow or change over time. [[Computation|Computations]] can occur within a signal or can occur through the interaction between signals. One type of processing, which uses signals and is gaining momentum is [[Signal processing|wave processing]], which involves the generation, expanding, and eventual collision of waves. Wave processing can be used to measure distances and find optimal paths. Computations can also occur through particles, gliders, solutions, and filterons localized structures that maintain their shape and velocity. Given how these structures interact/collide with each other and with static signals, they can be used to store information as states and implement different [[Boolean functions]]. Computations can also occur between complex, potentially growing or evolving localized behavior through worms, ladders, and pixel-snakes. In addition to storing states and performing [[Boolean function|Boolean functions]], these structures can interact, create, and destroy static structures.<ref>[[Andrew Adamatzky|A. Adamatzky]], B. Costello, T Asai "Reaction-Diffusion Computers", 2005.</ref><ref>F. Gollas and R. Tetzlaff, "Modeling Complex Systems by Reaction-Diffusion Cellular Nonlinear Networks with Polynomial Weight-Functions", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>A. Selikhov, "mL-CNN: A CNN Model for Reaction Diffusion Processes in m Component Systems", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref>
=== Automata and Turing machines ===
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*A. Gacsadi, C. Grava, V. Tiponut, and P. Szolgay, "A CNN Implementation of the Horn & Schunck Motion Estimation Method", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
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