Abstract: A low-power, controllable, and reconfigurable method to control weights in model neurons in an Artificial Neural Network is disclosed. Memristors are utilized as adjustable synapses, where the memristor resistance reflects the synapse weight. The injection of extremely small electric currents (a few nanoamperes) in each cell forces the resistance to drop abruptly by several orders of magnitudes due to the formation of a conductive path between the two electrodes. These conductive paths dissolve as soon as the current injection stops, and the cells return to their initial state. A repeated injection of currents into the same cell results in an almost identical effect in resistance drop. Different, stable resistance values in each cell can be controllably achieved by injecting different current values.

Abstract: An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information. The basic computational element (model neuron) is often called a node or unit. It receives input from some other units and/or from external sources. Each input has an associated weight (w), which can be modified so as to model synaptic learning. The present invention disclosures a low-power, controllable, and reconfigurable method to control weights in models neurons. The injection of extremely small electric currents (a few nanoamperes) in each cell forces the resistance to drop abruptly by several orders of magnitudes due to the formation of a conductive path between the two electrodes. These conductive paths dissolve as soon as the current injection stops, and the cells return to their initial state. A repeated injection of currents into the same cell results in an almost identical effect in resistance drop.