Training Spiking Neural Networks in Pure Julia | Ben Arthur, Christopher Kim | JuliaCon 2022 скачать в хорошем качестве

Training Spiking Neural Networks in Pure Julia | Ben Arthur, Christopher Kim | JuliaCon 2022

Training artificial neural networks to recapitulate the dynamics of biological neuronal recordings has become a prominent tool to understand computations in the brain. We present an implementation of a recursive-least squares algorithm to train units in a recurrent spiking network. Our code can reproduce the activity of 50,000 neurons of a mouse performing a decision-making task in less than an hour of training time. It can scale to a million neurons on a GPU with 80 GB of memory. For more info on the Julia Programming Language, follow us on Twitter:   / julialanguage   and consider sponsoring us on GitHub: https://github.com/sponsors/JuliaLang 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/You... Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/You...