Central to this research is something called an associative monadic learning element (AMLE). Researchers at the University of Oxford applied this concept to simplified neural circuitry with two key roles: 1) Converge and associate two inputs and 2) store the memories of these associations for later reference. Image used courtesy of Optica and Tan et al Ivan Pavlov, who discovered this concept in the early 1900s, observed that he could induce salivation in a dog when he rang a bell by teaching the dog to associate the sound of the bell with food. The associative learning process associates stimulus s 2 (i.e., bell sound) with a natural stimulus s 1 (i.e., sight or smell of food) to trigger an identical response (i.e., salivation) in the dog. When the sensory neurons receive sensory signals, the motor neurons generate sensory-intensive actions. This process includes sensory and motor neurons. Unlike conventional machine learning algorithms that run on electronic processors and traditional neural networks, Oxford's system runs on a backpropagation-free photonic network and leverages Pavlovian associative learning.Ĭlassical conditioning is the process of associating two sensory stimuli to achieve an identical response. Oxford claims its new system offers advanced dataset similarity detection.
Image used courtesy of Optica and Tan et al Supervised learning of the on-chip hardware. Inspired by Pavlov's classical conditioning experiments in the early 19th century, researchers at the University of Oxford recently created an on-chip optical processor that may open doors to unprecedented advancements in artificial intelligence (AI) and machine learning (ML).
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