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.
Support the Podcast Andrew and I discuss his work exploring how various facets of deep networks contribute to their function, i.e. deep network theory....
Show Notes: Federico’s website.Federico’s papers we discuss: Conflicting emergences. Weak vs. strong emergence for the modelling of brain functionFrom homeostasis to behavior: balanced activity...
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