Memristive learning cellular automata for edge detection

Memristors are novel non volatile devices that manage to combine storing and processing capabilities in the same physical place.Their nanoscale dimensions low power consumption enable further design of various nanoelectronic circuits corresponding computing architectures, like neuromorhpic, memory, unconventional, etc.One possible ways exploit memristor's advantages is by combining them with Cellular Automata (CA).CA constitute a well known von Neumann architecture based on local interconnection simple identical cells forming N-dimensional grids.These interconnections allow emergence global complex phenomena.In this paper, we propose hybridization CA original definition coupled memristor implementation, and, more specifically, focus Memristive Learning (MLCA), which have ability learning using also interconnected taking advantage inherent variability.The proposed MLCA circuit level implementation applied optimal detection edges image through series SPICE simulations, proving its robustness efficacy.