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.
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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.
BI 212 John Beggs: Why Brains Seek the Edge of Chaos
Brain Inspired
1 hour 33 minutes 34 seconds
6 months ago
BI 212 John Beggs: Why Brains Seek the Edge of Chaos
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You may have heard of the critical brain hypothesis. It goes something like this: brain activity operates near a dynamical regime called criticality, poised at the sweet spot between too much order and too much chaos, and this is a good thing because systems at criticality are optimized for computing, they maximize information transfer, they maximize the time range over which they operate, and a handful of other good properties. John Beggs has been studying criticality in brains for over 20 years now. His 2003 paper with Deitmar Plenz is one of of the first if not the first to show networks of neurons operating near criticality, and it gets cited in almost every criticality paper I read. John runs the Beggs Lab at Indiana University Bloomington, and a few years ago he literally wrote the book on criticality, called The Cortex and the Critical Point: Understanding the Power of Emergence, which I highly recommend as an excellent introduction to the topic, and he continues to work on criticality these days.
On this episode we discuss what criticality is, why and how brains might strive for it, the past and present of how to measure it and why there isn't a consensus on how to measure it, what it means that criticality appears in so many natural systems outside of brains yet we want to say it's a special property of brains. These days John spends plenty of effort defending the criticality hypothesis from critics, so we discuss that, and much more.
Beggs Lab.
Book:
The Cortex and the Critical Point: Understanding the Power of Emergence
Related papers
Addressing skepticism of the critical brain hypothesis
Papers John mentioned:
Tetzlaff et al 2010: Self-organized criticality in developing neuronal networks.
Haldeman and Beggs 2005: Critical Branching Captures Activity in Living Neural Networks and Maximizes the Number of Metastable States.
Bertschinger et al 2004: At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks.
Legenstein and Maass 2007: Edge of chaos and prediction of computational performance for neural circuit models.
Kinouchi and Copelli 2006: Optimal dynamical range of excitable networks at criticality.
Chialvo 2010: Emergent complex neural dynamics..
Mora and Bialek 2011: Are Biological Systems Poised at Criticality?
0:00 - Intro
4:28 - What is criticality?
10:19 - Why is criticality special in brains?
15:34 - Measuring criticality
24:28 - Dynamic range and criticality
28:28 - Criticisms of criticality
31:43 - Current state of critical brain hypothesis
33:34 - Causality and criticality
36:39 - Criticality as a homeostatic set point
38:49 - Is criticality necessary for life?
50:15 - Shooting for criticality far from thermodynamic equilibrium
52:45 - Quasi- and near-criticality
55:03 - Cortex vs. whole brain
58:50 - Structural criticality through development
1:01:09 - Criticality in AI
1:03:56 - Most pressing criticisms of criticality
1:10:08 - Gradients of criticality
1:22:30 - Homeostasis vs. criticality
1:29:57 - Minds and criticality
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.
