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. Texture recognition 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 patterns 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. Also, CNN processors were used to approximate pattern generation systems that create stationary fronts, spatio-temporal patterns 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 There is an ongoing effort to incorporate CNN processors into sensory-computing-actuating machines as part of the emerging field of Cellular Machines. The basic premise is to create an integrated system that uses CNN processors for the sensory signal-processing and potentially the decision-making and control. The reason is that CNN processors can provide a low power, small size, and eventually low-cost computing and actuating system suited for Cellular Machines. These Cellular Machines will eventually create a Sensor-Actuator Network (SAN), a type of Mobile Ad Hoc Networks (MANET) which can be used for military intelligence gathering, surveillance of inhospitable environments, maintenance of large areas, planetary exploration, etc. CNN processors have been proven versatile enough for some control functions. They have been used to optimize function via a genetic algorithm, to measure distances, to perform optimal path-finding in a complex, dynamic environment, and theoretically can be used to learn and associate complex stimuli. They have also been used to create antonymous gaits and low-level motors for robotic nematodes, spiders, and lamprey gaits using a Central Pattern Generator (CPG). They were able to function using only feedback from the environment, allowing for a robust, flexible, biologically inspired robot motor system. CNN-based systems were able to operate