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==Model of computation==
{{Section citations needed|date=December 2020}}
The dynamical behavior of CNN processors can be expressed using [[differential equations]], where each equation represents the state of an individual processing unit. The behavior of the entire CNN processor is defined by its initial conditions, the inputs, the cell interconnections (topology and weights), and the cells themselves. One possible use of CNN processors is to generate and respond to signals of specific dynamical properties. For example, CNN processors have been used to generate multi-scroll chaos, [[Synchronization|synchronize]] with chaotic systems, and exhibit multi-level [[hysteresis]].<ref>A. Slavova and M. Markovat, "Receptor Based CNN Model with Hysteresis for Pattern Generation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>K. Yokosawa, Y. Tanji and M. Tanaka, "CNN with Multi-Level Hysteresis Quantization Output" Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>T. Nakaguchi, K. Omiya and M. Tanaka, "Hysteresis Cellular Neural Networks for Solving Combinatorial Optimization Problems", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> 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 [[Mathematical morphology|morphological]] operators. They can also be used to approximate a wide range of [[Partial differential equations]] (PDE) such as heat dissipation and wave propagation.<ref>P. Sonkolyt, P. Kozmat, Z. Nagyt and P. Szolgay, "Acoustic Wave Propagation Modeling on CNN-UM Architecture", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref>
=== Reaction-Diffusion ===
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*L. Goras, L. Chua, and D. Leenearts, "Turing Patterns in CNNs – Part II: Equations and Behavior", IEEE Trans. on Circuits and Systems – I, 42(10):612-626, 1995.
*L. Goras, L. Chua, and D. Leenearts, "Turing Patterns in CNNs – Part III: Computer Simulation Results", IEEE Trans. on Circuits and Systems – I, 42(10):627-637, 1995.
*L. Komatowskit, K. Slot, P. Dqbiec, and H. Kim, "Generation of Patterns with Predefined Statistical Properties using Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*C. Lin and S. Chen, "Biological Visual Processing for Hybrid-Order Texture Boundary Detection with CNN-UM", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
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