Physical neural network

See also: Hardware for artificial intelligence and Neuromorphic engineering

A physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model.[1] "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.[2][3]

  1. ^ Lawrence, Celestine P. (2022), "Compact Modeling of Nanocluster Functionality as a Higher-Order Neuron", IEEE Transactions on Electron Devices, 69 (9): 5373–5376, Bibcode:2022ITED...69.5373L, doi:10.1109/TED.2022.3191956, S2CID 251340897
  2. ^ "Cornell & NTT's Physical Neural Networks: A "Radical Alternative for Implementing Deep Neural Networks" That Enables Arbitrary Physical Systems Training | Synced". 27 May 2021.
  3. ^ "Nano-spaghetti to solve neural network power consumption".