Abstract: The theory and application of morphological associative memories and morphological neural networks in general are emerging areas of research in computer science. The concept of a morphological associative memory differs from a more conventional associative memory by the nonlinear functionality of the synaptic connection. By taking the maximum of sums instead of the sum of products, morphological network computation is inherently nonlinear. Hence, the morphological associative memory does not require any ad hoc methodology to interject a nonlinear state. In this paper, we introduce a very large scale integration analog circuit design that describes the nonlinear functionality of the synaptic connection. We specifically describe the fundamental circuit needed to implement a basic additive maximum associative memory, and describe noise conditions under which this memory will perform flawlessly. As a potential application, we propose the use of the analog circuit to real-time operation on or near a focal plane array sensor.!6
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