Brain Inspired
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.
- Update frequency
- every 12 days
- Average duration
- 89 minutes
- Episodes
- 133
- Years Active
- 2021 - 2025
BI 141 Carina Curto: From Structure to Dynamics
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Carina Curto is a profe…
BI 140 Jeff Schall: Decisions and Eye Movements
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Jeff Schall is the dire…
BI 139 Marc Howard: Compressed Time and Memory
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Marc Howard runs his Theo…
BI 138 Matthew Larkum: The Dendrite Hypothesis
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Matthew Larkum runs his…
BI 137 Brian Butterworth: Can Fish Count?
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Brian Butterworth is …
BI 136 Michel Bitbol and Alex Gomez-Marin: Phenomenology
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Michel Bitbol is Dire…
BI 135 Elena Galea: The Stars of the Brain
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Brains are often concei…
BI 134 Mandyam Srinivasan: Bee Flight and Cognition
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Srini is Emeritus Profe…
BI 133 Ken Paller: Lucid Dreaming, Memory, and Sleep
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Ken discusses the recen…
BI 132 Ila Fiete: A Grid Scaffold for Memory
Announcement:
I'm releasing my Neuro-AI course April 10-13, after which it will be closed for some time. Learn more here.
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BI 131 Sri Ramaswamy and Jie Mei: Neuromodulation-aware DNNs
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Sri and Mei join me to discuss how including principles of neuromodulation in deep learning networks may …
BI 130 Eve Marder: Modulation of Networks
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Eve discusses many of the lessons she has learned studying a small nervous system, the crustacean stomato…
BI 129 Patryk Laurent: Learning from the Real World
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Patryk and I discuss his wide-ranging background working in both the neuroscience and AI worlds, and his …
BI 128 Hakwan Lau: In Consciousness We Trust
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Hakwan and I discuss many of the topics in his new book, In Consciousness we Trust: The Cognitive Neurosc…
BI 127 Tomás Ryan: Memory, Instinct, and Forgetting
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Tomás and I discuss his research and ideas on how memories are encoded (the engram), the role of forgetti…
BI 126 Randy Gallistel: Where Is the Engram?
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Randy and I discuss his long-standing interest in how the brain stores information to compute. That is, w…
BI 125 Doris Tsao, Tony Zador, Blake Richards: NAISys
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Doris, Tony, and Blake are the organizers for this year's NAISys conference, From Neuroscience to Artificia…
BI 124 Peter Robin Hiesinger: The Self-Assembling Brain
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Robin and I discuss many of the ideas in his book The Self-Assembling Brain: How Neural Networks Grow Sma…
BI 123 Irina Rish: Continual Learning
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Irina is a faculty member at MILA-Quebec AI Institute and a professor at Université de Montréal. She has …
BI 122 Kohitij Kar: Visual Intelligence
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Ko and I discuss a range of topics around his work to understand our visual intelligence. Ko was a postdoc in James Dica…