Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.
https://braininspired.co/series/brain-inspired/
BI 058 Wolfgang Maass: Computing Brains and Spiking Nets
In this first part of our conversation (here's the second part), Wolfgang and I discuss the state of theoretical and computational neuroscience, and how experimental results in neuroscience should guide theories and models to understand and explain how brains compute. We also discuss brain-machine interfaces, neuromorphics, and more. In the next part (here), we discuss principles of brain processing to inform and constrain theories of computations, and we briefly talk about some of his most recent work making spiking neural networks that incorporate some of these brain processing principles.
- Wolfgang's website.
- The book Wolfgang recommends:
- The Brain from Inside Out by György Buzsáki.
- 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.