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The idea of CNN processors was introduced by [[Leon Chua]] and Lin Yang in 1988.<ref>https://www.researchgate.net/publication/3183706_Cellular_neural_networks_Theory ("Cellular Neural Networks: Theory" and "Cellular Neural Networks: Applications" in IEEE Transactions on Circuits and Systems)</ref> In these articles, Chua and Yang outline the underlying mathematics behind CNN processors. They use this mathematical model to demonstrate, for a specific CNN implementation, that if the inputs are static, the processing units will converge, and can be used to perform useful calculations. They then suggest one of the first applications of CNN processors: image processing and pattern recognition (which is still the largest application to date). [[Leon O. Chua|Leon Chua]] is still active in CNN research and publishes many of his articles in the [[International Journal of Bifurcation and Chaos]], of which he is an editor. Both [[IEEE Circuits and Systems Society|IEEE Transactions on Circuits and Systems]] and the International Journal of Bifurcation also contain a variety of useful articles on CNN processors authored by other knowledgeable researchers. The former tends to focus on new CNN architectures and the latter more on the dynamical aspects of CNN processors.
In 1993, [[:nl:Tamás_Roska|Tamas Roska]] and Leon Chua introduced the first algorithmically programmable analog CNN processor
=== Literature ===
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=== Automata and Turing machines ===
Although CNN processors are primarily intended for analog calculations, certain types of CNN processors can implement any Boolean function, allowing simulating CA. Since some CA are [[Universal Turing machine]]s (UTM), capable of [[Simulation|simulating]] any algorithm can be performed on processors based on the [[von Neumann architecture]], that makes this type of CNN processors, universal CNN, a UTM. One CNN architecture consists of an additional layer. CNN processors have resulted in the simplest realization of [[Conway’s Game of Life]] and [[Rule 110|Wolfram’s Rule 110]], the simplest known universal [[Turing machine|Turing Machine]]. This unique, dynamical representation of an old systems, allows researchers to apply techniques and hardware developed for CNN to better understand important CA. Furthermore, the continuous state space of CNN processors, with slight modifications that have no equivalent in [[Cellular Automata]], creates [[Emergence|emergent]] behavior never seen before.<ref name=":0">{{Cite journal|last=Goras|first=L.|last2=Chua|first2=L.O.|last3=Leenaerts|first3=D.M.W.|date=1995|title=Turing patterns in CNNs. I. Once over lightly|url=http://dx.doi.org/10.1109/81.473567|journal=IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications|volume=42|issue=10|pages=602–611|doi=10.1109/81.473567|issn=1057-7122}}</ref>
Any information processing platform that allows the construction of arbitrary [[Boolean function|Boolean functions]] is called universal, and as result, this class CNN processors are commonly referred to as universal CNN processors. The original CNN processors can only perform linearly separable Boolean functions. By translating functions from digital logic or look-up table domains into the CNN ___domain, some functions can be considerably simplified. For example, the nine-bit, odd parity generation logic, which is typically implemented by eight nested exclusive-or gates, can also be represented by a sum function and four nested absolute value functions. Not only is there a reduction in the function complexity, but the CNN implementation parameters can be represented in the continuous, real-number ___domain.
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Due to their processing capabilities and flexibility, CNN processors have been used & [[Prototype|prototyped]] for novel field applications such as flame analysis for monitoring combustion at a waste [[Incineration|incinerator]], mine-detection using [[infrared]] imagery, [[calorimeter]] cluster peak for high energy physics, anomaly detection in potential field maps for geophysics, laser dot detection, metal inspection for detecting manufacturing defects, and [[Seismology|seismic]] horizon picking. They have also been used to perform [[Biometrics|biometric]] functions such as [[fingerprint recognition]], vein feature extraction, face tracking, and generating visual stimuli via emergent patterns to gauge perceptual [[Resonance|resonances]]. CNN processors have been used for medical and biological research in performing automated nucleated cell counting for detecting [[hyperplasia]], segment images into anatomically and [[Pathology|pathologically]] meaningful regions, measure and quantify cardiac function, measure the timing of neurons, and detect brain abnormalities that would lead to seizures. One potential future application of CNN microprocessors is to combine them with DNA microarrays to allow for a near-real time DNA analysis of hundreds of thousands of different DNA sequences. Currently, the major bottleneck of DNA microarray analysis is the amount of time needed to process data in the form of images, and using a CNN microprocessor, researchers have reduced the amount of time needed to perform this calculation to 7ms.
CNN processors have also been used to generate and analyze patterns and textures. One motivation was to use CNN processors to understand pattern generation in natural systems. They were used to generate [[Turing pattern]]s in order to understand the situations in which they form, the different types of patterns which can emerge, and the presence of defects or asymmetries.<ref name=":0" /> Also, CNN processors were used to approximate pattern generation systems that create stationary fronts, [[spatio-temporal pattern]]s [[Oscillation|oscillating]] in time, [[hysteresis]], memory, and heterogeneity. Furthermore, pattern generation was used to aid high-performance image generation and compression via real-time generation of [[stochastic]] and coarse-grained biological patterns, texture boundary detection, and pattern and [[texture recognition]] and classification.
==Control and Actuator Systems==
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CNN processors are [[Neuromorphic engineering|neuromorphic]] processors, meaning that they emulate certain aspects of [[biological neural network]]s. The original CNN processors were based on mammalian retinas, which consist of a layer of [[Photodetector|photodetectors]] connected to several layers of locally coupled neurons. This makes CNN processors part of an interdisciplinary research area whose goal is to design systems that leverage knowledge and ideas from neuroscience and contribute back via real-world validation of theories. CNN processors have implemented a real-time system that replicates mammalian retinas, validating that the original CNN architecture chosen modeled the correct aspects of the biological neural networks used to perform the task in mammalian life. However, CNN processors are not limited to verifying biological neural networks associated with vision processing; they have been used to simulate dynamic activity seen in mammalian neural networks found in the olfactory bulb and locust [[antennal lobe]], responsible for pre-processing sensory information to detect differences in repeating patterns.
CNN processors are being used to understand systems that can be modeled using simple, coupled units, such as living cells, biological networks, physiological systems, and ecosystems. The CNN architecture captures some of the dynamics often seen in nature and is simple enough to analyze and conduct experiments. They are also being used for [[stochastic]] simulation techniques, which allow scientists to explore spin problems, population dynamics, lattice-based gas models, [[percolation]], and other phenomena. Other simulation applications include heat transfer, mechanical vibrating systems, protein production, Josephson Transmission Line (JTL) problems, seismic wave propagation, and geothermal structures. Instances of 3D (Three Dimensional) CNN have been used to prove known complex shapes are emergent phenomena in complex systems, establishing a link between art, dynamical systems and VLSI technology. CNN processors have been used to research a variety of mathematical concepts, such as researching non-equilibrium systems, constructing non-linear systems of arbitrary complexity using a collection of simple, well-understood dynamic systems, studying emergent chaotic dynamics, generating chaotic signals, and in general discovering new dynamic behavior. They are often used in researching systemics, a trandisiplinary, scientific field that studies natural systems. The goal of systemics researchers is to develop a conceptual and mathematical framework necessary to analyze, model, and understand systems, including, but not limited to, atomic, mechanical, molecular, chemical, biological, ecological, social and economic systems. Topics explored are emergence, collective behavior, local activity and its impact on global behavior, and quantifying the complexity of an approximately spatial and topologically invariant system.{{Citation needed|date=December 2015}} Although another measure of complexity may not arouse enthusiasm ([[Seth Lloyd]], a professor from Massachusetts Institute of Technology (MIT), has identified 32 different definitions of complexity), it can potentially be mathematically advantageous when analyzing systems such as economic and social systems.
==Notes==
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