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.

• Wolfgang's website.
• Advice To a Young Investigator (has the quote at the beginning of the episode) by Santiago Ramon y Cajal.
• Papers we discuss or mention:
  • Searching for principles of brain computation.
  • Brain Computation: A Computer Science Perspective.
  • Long short-term memory and learning-to-learn in networks of spiking neurons.
  • A solution to the learning dilemma for recurrent networks of spiking neurons.
  • Reservoirs learn to learn.
• Talks that cover some of these topics:
  • Computation in Networks of Neurons in the Brain I.
  • Computation in Networks of Neurons in the Brain II.