Brain Inspired
A podcast by Paul Middlebrooks
Categories:
127 Episodes
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BI 147 Noah Hutton: In Silico
Published: 9/13/2022 -
BI 146 Lauren Ross: Causal and Non-Causal Explanation
Published: 9/7/2022 -
BI 145 James Woodward: Causation with a Human Face
Published: 8/28/2022 -
BI 144 Emily M. Bender and Ev Fedorenko: Large Language Models
Published: 8/17/2022 -
BI 143 Rodolphe Sepulchre: Mixed Feedback Control
Published: 8/5/2022 -
BI 142 Cameron Buckner: The New DoGMA
Published: 7/26/2022 -
BI 141 Carina Curto: From Structure to Dynamics
Published: 7/12/2022 -
BI 140 Jeff Schall: Decisions and Eye Movements
Published: 6/30/2022 -
BI 139 Marc Howard: Compressed Time and Memory
Published: 6/20/2022 -
BI 138 Matthew Larkum: The Dendrite Hypothesis
Published: 6/6/2022 -
BI 137 Brian Butterworth: Can Fish Count?
Published: 5/27/2022 -
BI 136 Michel Bitbol and Alex Gomez-Marin: Phenomenology
Published: 5/17/2022 -
BI 135 Elena Galea: The Stars of the Brain
Published: 5/6/2022 -
BI 134 Mandyam Srinivasan: Bee Flight and Cognition
Published: 4/27/2022 -
BI 133 Ken Paller: Lucid Dreaming, Memory, and Sleep
Published: 4/15/2022 -
BI 132 Ila Fiete: A Grid Scaffold for Memory
Published: 4/3/2022 -
BI 131 Sri Ramaswamy and Jie Mei: Neuromodulation-aware DNNs
Published: 3/26/2022 -
BI 130 Eve Marder: Modulation of Networks
Published: 3/13/2022 -
BI 129 Patryk Laurent: Learning from the Real World
Published: 3/2/2022 -
BI 128 Hakwan Lau: In Consciousness We Trust
Published: 2/20/2022
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.