Editing Machine learning (section)

==== physical neural networks ====
A [[physical neural network]] is a specific type of neuromorphic hardware that relies on electrically adjustable materials, such as memristors, to emulate the function of [[chemical synapse|neural synapses]]. The term "physical neural network" highlights the use of physical hardware for computation, as opposed to software-based implementations. It broadly refers to artificial neural networks that use materials with adjustable resistance to replicate neural synapses.<ref>{{Cite web |date=27 May 2021 |title=Cornell & NTT's Physical Neural Networks: A "Radical Alternative for Implementing Deep Neural Networks" That Enables Arbitrary Physical Systems Training |url=https://syncedreview.com/2021/05/27/deepmind-podracer-tpu-based-rl-frameworks-deliver-exceptional-performance-at-low-cost-28/ |url-status=live |archive-url=https://web.archive.org/web/20211027183428/https://syncedreview.com/2021/05/27/deepmind-podracer-tpu-based-rl-frameworks-deliver-exceptional-performance-at-low-cost-28/ |archive-date=27 October 2021 |access-date=12 October 2021 |website=Synced}}</ref><ref>{{Cite news |date=5 October 2021 |title=Nano-spaghetti to solve neural network power consumption |url=https://www.theregister.com/2021/10/05/analogue_neural_network_research/ |url-status=live |archive-url=https://web.archive.org/web/20211006150057/https://www.theregister.com/2021/10/05/analogue_neural_network_research/ |archive-date=6 October 2021 |access-date=12 October 2021 |work=The Register}}</ref>