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.
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[bctt tweet="Check out episode 6 of the Brain Inspired podcast: Deep learning, eyeballs, and brains" username="pgmid"] Mentioned in the show Ryan Poplin What is...
Support the Podcast Jess and I discuss construction using graph neural networks. She makes AI agents that build structures to solve tasks in a...
