In this second part of my discussion with Wolfgang (check out the first part), we talk about spiking neural networks in general, principles of brain computation he finds promising for implementing better network models, and we quickly overview some of his recent work on using these principles to build models with biologically plausible learning mechanisms, a spiking network analog of the well-known LSTM recurrent network, and meta-learning using reservoir computing.
Image courtesy of Kendrick Kay: Brain art Show notes: Check out Kendrick’s lab website: CVN lab. Follow him on twitter: @cvnlab. The papers we...
Thomas and I discuss the role of recurrence in visual cognition: how brains somehow excel with so few “layers” compared to deep nets, how...
Support the show to get full episodes and join the Discord community. Joe Monaco and Grace Hwang co-organized a recent workshop I participated in,...
