example, neurons in one layer are fully connected to another layer in a feed-forward NN and all the neurons are fully interconnected in Hopfield networks. In ANNs, the weights of interconnections contain information on the processing system’s previous state or feedback. But in CNN processors, the weights are used to determine the dynamics of the system. Furthermore, due to the high inter-connectivity of ANNs, they tend not exploit locality in either the data set or the processing and as a result, they usually are highly redundant systems that allow for robust, fault-tolerant behavior without catastrophic errors. A cross between an ANN and a CNN processor is a Ratio Memory CNN (RMCNN). In RMCNN processors, the cell interconnect is local and topologically invariant, but the weights are used to store previous states and not to control dynamics. The weights of the cells are modified during some learning state creating long-term memory. Continuous Automata The topology and dynamics of CNN processors closely resembles that of CA. Like most CNN processors, CA consists of a fixed-number of identical processors that are spatially discrete and topologically uniform. The difference is that most CNN processors are continuous-valued whereas CA have discrete-values. Furthermore, the CNN processor's cell behavior is defined via some non-linear function whereas CA processor cells are defined by some state machine. However, there are some exceptions. Continuous Valued Cellular Automata are CA with continuous resolution. Depending on how a given Continuous Automata is specified, it can also be a CNN. There are also Continuous Spatial Automata, which consist of an infinite number of spatially continuous, continuous-valued automata. There is considerable work being performed in this field since continuous spaces are easier to mathematically model than discrete spaces, thus allowing a more quantitative approach as opposed to an empirical approach taken by some researchers of cellular automata. Continuous Spatial Automata processors can be physically realized though an unconventional information processing platform such as a chemical computer. Furthermore, it is conceivable that large CNN processors (in terms of the resolution of the input and output) can be modeled as a Continuous Spatial Automata. Model of computation 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, inputs, cell interconnections (topology and weights), and the cells themselves. One possible use