Brain Inspired
En podkast av Paul Middlebrooks - Onsdager
164 Episoder
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BI 204 David Robbe: Your Brain Doesn’t Measure Time
Publisert: 29.1.2025 -
BI 203 David Krakauer: How To Think Like a Complexity Scientist
Publisert: 14.1.2025 -
BI 202 Eli Sennesh: Divide-and-Conquer to Predict
Publisert: 3.1.2025 -
BI 201 Rajesh Rao: From Predictive Coding to Brain Co-Processors
Publisert: 18.12.2024 -
BI 200 Grace Hwang and Joe Monaco: The Future of NeuroAI
Publisert: 4.12.2024 -
BI 199 Hessam Akhlaghpour: Natural Universal Computation
Publisert: 26.11.2024 -
BI 198 Tony Zador: Neuroscience Principles to Improve AI
Publisert: 11.11.2024 -
BI 197 Karen Adolph: How Babies Learn to Move and Think
Publisert: 25.10.2024 -
BI 196 Cristina Savin and Tim Vogels with Gaute Einevoll and Mikkel Lepperød
Publisert: 11.10.2024 -
BI 195 Ken Harris and Andreas Tolias with Gaute Einevoll and Mikkel Lepperød
Publisert: 8.10.2024 -
BI 194 Vijay Namboodiri & Ali Mohebi: Dopamine Keeps Getting More Interesting
Publisert: 27.9.2024 -
BI 193 Kim Stachenfeld: Enhancing Neuroscience and AI
Publisert: 11.9.2024 -
BI 192 Àlex Gómez-Marín: The Edges of Consciousness
Publisert: 28.8.2024 -
BI 191 Damian Kelty-Stephen: Fractal Turbulent Cascading Intelligence
Publisert: 15.8.2024 -
BI 190 Luis Favela: The Ecological Brain
Publisert: 31.7.2024 -
BI 189 Joshua Vogelstein: Connectomes and Prospective Learning
Publisert: 29.6.2024 -
BI 188 Jolande Fooken: Coordinating Action and Perception
Publisert: 27.5.2024 -
BI 187: COSYNE 2024 Neuro-AI Panel
Publisert: 20.4.2024 -
BI 186 Mazviita Chirimuuta: The Brain Abstracted
Publisert: 25.3.2024 -
BI 185 Eric Yttri: Orchestrating Behavior
Publisert: 6.3.2024
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.
