Physical neural network

[9] Alignment or self-assembly of the nanoconnections is determined by the history of the applied electric field performing a function analogous to neural synapses.7,039,619[12] entitled "Utilized nanotechnology apparatus using a neural network, a solution and a connection gap," which issued to Alex Nugent by the U.S. Patent & Trademark Office on May 2, 2006.7,412,428 entitled "Application of hebbian and anti-hebbian learning to nanotechnology-based physical neural networks," which issued on August 12, 2008.[16] [17] In 2002, Stanford Ovshinsky described an analog neural computing medium in which phase-change material has the ability to cumulatively respond to multiple input signals.[20] In 2022, researchers reported the development of nanoscale brain-inspired artificial synapses, using the ion proton (H+), for 'analog deep learning'.
Hardware for artificial intelligenceNeuromorphic engineeringPhysics-informed neural networksartificial neural networkneural synapseneuronsmemristorBernard WidrowTed HoffADALINEmemistorssolid-state electronicsCarver Meadanalog VLSItranslinear circuitsBarrie GilbertnanotechnologyU.S. Patent & Trademark OfficehebbianStanford Ovshinskyphase-change materialHP LabsmemristorsSyNAPSE projectnanoscalebrain-inspireddeep learningAI acceleratorBrain simulationOptical neural networkQuantum neural networkBibcodePatent