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BI 119 Henry Yin: The Crisis in Neuroscience

BI 119 Henry Yin: The Crisis in Neuroscience

FromBrain Inspired


BI 119 Henry Yin: The Crisis in Neuroscience

FromBrain Inspired

ratings:
Length:
67 minutes
Released:
Nov 11, 2021
Format:
Podcast episode

Description

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Henry and I discuss why he thinks neuroscience is in a crisis (in the Thomas Kuhn sense of scientific paradigms, crises, and revolutions). Henry thinks our current concept of the brain as an input-output device, with cognition in the middle, is mistaken. He points to the failure of neuroscience to successfully explain behavior despite decades of research. Instead, Henry proposes the brain is one big hierarchical set of control loops, trying to control their output with respect to internally generated reference signals. He was inspired by control theory, but points out that most control theory for biology is flawed by not recognizing that the reference signals are internally generated. Instead, most control theory approaches, and neuroscience research in general, assume the reference signals are what gets externally supplied... by the experimenter.



Yin lab at Duke.Twitter: @HenryYin19.Related papersThe Crisis in Neuroscience.Restoring Purpose in Behavior.Achieving natural behavior in a robot using neurally inspired hierarchical perceptual control.





0:00 - Intro
5:40 - Kuhnian crises
9:32 - Control theory and cybernetics
17:23 - How much of brain is control system?
20:33 - Higher order control representation
23:18 - Prediction and control theory
27:36 - The way forward
31:52 - Compatibility with mental representation
38:29 - Teleology
45:53 - The right number of subjects
51:30 - Continuous measurement
57:06 - Artificial intelligence and control theory
Released:
Nov 11, 2021
Format:
Podcast episode

Titles in the series (99)

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.