0000000000395243

AUTHOR

S. A. Gerasimova

showing 2 related works from this author

Field- and irradiation-induced phenomena in memristive nanomaterials

2016

The breakthrough in electronics and information technology is anticipated by the development of emerging memory and logic devices, artificial neural networks and brain-inspired systems on the basis of memristive nano-materials represented, in a particular case, by a simple 'metal-insulator-metal' (MIM) thin-film structure. The present article is focused on the comparative analysis of MIM devices based on oxides with dominating ionic (ZrOx, HfOx) and covalent (SiOx, GeOx) bonding of various composition and geometry deposited by magnetron sputtering. The studied memristive devices demonstrate reproducible change in their resistance (resistive switching - RS) originated from the formation and …

Resistive touchscreenSettore FIS/02 - Fisica Teorica Modelli E Metodi MatematiciOxideIonic bondingNanotechnology02 engineering and technologyMemristorSputter deposition021001 nanoscience & nanotechnologyCondensed Matter Physics01 natural sciences010305 fluids & plasmasNanomaterialslaw.inventionIonchemistry.chemical_compoundchemistrylaw0103 physical sciences0210 nano-technologyMemristor resistive switching metal-oxide-metal nanostructure kinetic Monte-Carlo simulation radiation tolerance synaptic behaviour nonlinear dynamics stochastic resonanceElectrical conductor
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Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network

2021

Abstract We investigate the constructive role of an external noise signal, in the form of a low-rate Poisson sequence of pulses supplied to all inputs of a spiking neural network, consisting in maintaining for a long time or even recovering a memory trace (engram) of the image without its direct renewal (or rewriting). In particular, this unique dynamic property is demonstrated in a single-layer spiking neural network consisting of simple integrate-and-fire neurons and memristive synaptic weights. This is carried out by preserving and even fine-tuning the conductance values of memristors in terms of dynamic plasticity, specifically spike-timing-dependent plasticity-type, driven by overlappi…

Spiking neural networkQuantitative Biology::Neurons and CognitionComputer scienceNoise (signal processing)General MathematicsApplied MathematicsGeneral Physics and AstronomyStatistical and Nonlinear PhysicsEngramMemristorStochastic processeSignalNeural networklaw.inventionNoise induced phenomenaNeuromorphic engineeringlawVoltage spikeMemristive devicesState (computer science)Biological system
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