title: Non-Hebbian learning implementation in light-controlled resistive memory devices.
abstract: Non-Hebbian learning is often encountered in different bio-organisms. In these processes, the strength of a synapse connecting two neurons is controlled not only by the signals exchanged between the neurons, but also by an additional factor external to the synaptic structure. Here we show the implementation of non-Hebbian learning in a single solid-state resistive memory device. The output of our device is controlled not only by the applied voltages, but also by the illumination conditions under which it operates. We demonstrate that our metal/oxide/semiconductor device learns more efficiently at higher applied voltages but also when light, an external parameter, is present during the information writing steps. Conversely, memory erasing is more efficiently at higher applied voltages and in the dark. Translating neuronal activity into simple solid-state devices could provide a deeper understanding of complex brain processes and give insight into non-binary computing possibilities.
authors.affiliation: CIC nanoGUNE Consolider, Donostia - San Sebastian, Spain. m.ungureanu@nanogune.eu
authors.forename: Mariana
authors.initials: M
authors.lastname: Ungureanu
authors.forename: Pablo
authors.initials: P
authors.lastname: Stoliar
authors.forename: Roger
authors.initials: R
authors.lastname: Llopis
authors.forename: Fèlix
authors.initials: F
authors.lastname: Casanova
authors.forename: Luis E
authors.initials: LE
authors.lastname: Hueso
mesh_terms: D001921:Brain / Q000502:physiology*; D007858:Learning / Q000502:physiology*; D008027:Light; D008568:Memory / Q000502:physiology*; D008670:Metals / Q000737:chemistry; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D010087:Oxides / Q000737:chemistry; D012666:Semiconductors
chemical_list: D008670:Metals; D010087:Oxides