  Recent results in adaptive matter revived the interest in the implementation
of novel devices able to perform brain-like operations. Here we introduce a
training algorithm for a memristor network which is inspired in previous work
on biological learning. Robust results are obtained from computer simulations
of a network of voltage controlled memristive devices. Its implementation in
hardware is straightforward, being scalable and requiring very little
peripheral computation overhead.
