A Brain-Inspired Algorithm For Memory

Get 20% off at https://shortform.com/artem In this video we will explore the concept of Hopfield networks – a foundational model of associative memory that underlies many important ideas in neuroscience and machine learning, such as Boltzmann machines and Dense associative memory. Socials: X/Twitter: https://x.com/ArtemKRSV Patreon: https://www.patreon.com/artemkirsanov OUTLINE: 00:00 Introduction 02:17 Protein folding paradox 04:23 Energy definition 08:25 Hopfield network architecture 14:03 Inference 18:40 Learning 22:48 Limitations & Perspective 24:43 Shortform 25:54 Outro References: 1) Downing, K.L., 2023. Gradient expectations: structure, origins, and synthesis of predictive neural networks. The MIT Press, Cambridge, Massachusetts. 2) https://towardsdatascience.com/hopfield-networks-neural-memory-machines-4c94be821073 3) https://ml-jku.github.io/hopfield-layers/ Special thanks to Crimson Ghoul for providing English subtitles! Credits: Protein folding: https://www.youtube.com/shorts/fvBO3TqJ6FE 🎵 Music licensed from Lickd. The biggest mainstream and stock music platform for content creators Viva La Vida by Coldplay, https://lickd.lnk.to/4aEPvoID License ID: RXj082JWjbA Try Lickd FREE for 14 days for unlimited stock music and get 50% off your first mainstream track: https://app.lickd.co/r/47462149f85b4b6e9660bbe6d9b0f944