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