of CNN processors is to generate and respond to signals of specific dynamical properties. For example, CNN processors have been used to generate multiscroll chaos (like the Chen attractor), synchronize with chaotic systems, and exhibit multi-level hysteresis. CNN processors are designed specifically to solve local, low-level, processor intensive problems expressed as a function of space and time. For example, CNN processors can be used to implement high-pass and low-pass filters and morphological operators. They can also be used to approximate a wide range of Partial differential equations (PDE) such as heat dissipation and wave propagation. Reaction-Diffusion CNN processors can be used as 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 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. Boolean functions Like CA, computations can be performed through the generation and propagation of signals that either grow or change over time. 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 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 functions, these structures can interact, create, and destroy static structures. The applications of CNNs to Boolean functions is discussed in the paper by Fangyue