Brain-Like Networks in Random Memristor Array Based on FORCE Training

Biological neural networks are connected in a highly recurrent manner, which displays complex activity patterns spontaneously upon an awareness stimulus. Here, we emulate such a network based on a recurrently connected memristor array. To generate a target function, a first-order and controlled error (FORCE) learning rule that only needs to modify a small fraction of connections is introduced. This rule tries to minimize the amount of modification and thus is especially suitable for memristor-based synapses. After learning, the network could spontaneously generate the desired function after a random stimulus, just as the biological systems do.