Exploring Criticality in Neural Networks

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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?

Read the transcript.

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