Brain Inspired
En podkast av Paul Middlebrooks
Kategorier:
127 Episoder
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BI 147 Noah Hutton: In Silico
Publisert: 13.9.2022 -
BI 146 Lauren Ross: Causal and Non-Causal Explanation
Publisert: 7.9.2022 -
BI 145 James Woodward: Causation with a Human Face
Publisert: 28.8.2022 -
BI 144 Emily M. Bender and Ev Fedorenko: Large Language Models
Publisert: 17.8.2022 -
BI 143 Rodolphe Sepulchre: Mixed Feedback Control
Publisert: 5.8.2022 -
BI 142 Cameron Buckner: The New DoGMA
Publisert: 26.7.2022 -
BI 141 Carina Curto: From Structure to Dynamics
Publisert: 12.7.2022 -
BI 140 Jeff Schall: Decisions and Eye Movements
Publisert: 30.6.2022 -
BI 139 Marc Howard: Compressed Time and Memory
Publisert: 20.6.2022 -
BI 138 Matthew Larkum: The Dendrite Hypothesis
Publisert: 6.6.2022 -
BI 137 Brian Butterworth: Can Fish Count?
Publisert: 27.5.2022 -
BI 136 Michel Bitbol and Alex Gomez-Marin: Phenomenology
Publisert: 17.5.2022 -
BI 135 Elena Galea: The Stars of the Brain
Publisert: 6.5.2022 -
BI 134 Mandyam Srinivasan: Bee Flight and Cognition
Publisert: 27.4.2022 -
BI 133 Ken Paller: Lucid Dreaming, Memory, and Sleep
Publisert: 15.4.2022 -
BI 132 Ila Fiete: A Grid Scaffold for Memory
Publisert: 3.4.2022 -
BI 131 Sri Ramaswamy and Jie Mei: Neuromodulation-aware DNNs
Publisert: 26.3.2022 -
BI 130 Eve Marder: Modulation of Networks
Publisert: 13.3.2022 -
BI 129 Patryk Laurent: Learning from the Real World
Publisert: 2.3.2022 -
BI 128 Hakwan Lau: In Consciousness We Trust
Publisert: 20.2.2022
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.
