Reservoir Computing with Neuromemristive Nanowire Networks
Kaiwei Fu, Ruomin Zhu, Alon Loeffler, Joel Hochstetter, Adrian Diaz‐Alvarez, Adam Z. Stieg, James K. Gimzewski, Tomonobu Nakayama, Zdenka Kuncic
Abstract
We present simulations based on a model of self- assembled nanowire networks with memristive junctions and neural network-like topology. We analyze the dynamical voltage distribution in response to an applied bias and explain the network conductance fluctuations observed in our previous experimental studies. We then demonstrate the potential of neuromorphic nanowire networks as a physical reservoir by performing benchmark reservoir computing tasks. The tasks include sine wave nonlinear transformation, sine wave auto- generation and forecasting the Mackey-Glass chaotic time series.
Topics & Concepts
Reservoir computingNeuromorphic engineeringNanowireChaoticBenchmark (surveying)Sine waveNonlinear systemComputer scienceSineAttractorTopology (electrical circuits)ConductanceArtificial neural networkTransformation (genetics)Network topologyVoltagePhysicsMaterials scienceArtificial intelligenceRecurrent neural networkNanotechnologyMathematicsEngineeringElectrical engineeringComputer networkGeologyGeneGeometryGeodesyBiochemistryCondensed matter physicsChemistryQuantum mechanicsMathematical analysisNeural Networks and Reservoir ComputingAdvanced Memory and Neural ComputingNeural dynamics and brain function