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Xaq Pitkow runs the Lab for the Algorithmic Brain at Carnegie Mellon University. The main theme of our discussion is how Xaq approaches his research into cognition by way of principles, from which his questions and models and methods spring forth. We discuss those principles, and In that light, we discuss some of his specific lines of work and ideas on the theoretical side of trying understand and explain a slew of cognitive processes. A few of the specifics we discuss are:

• How when we present tasks for organisms to solve, they use strategies that are suboptimal relative to the task, but nearly optimal relative to their beliefs about what they need to do - something Xaq calls inverse rational control.
• Probabilistic graph networks.
• How brains use probabilities to compute.
• A new ecological neuroscience project Xaq has started with multiple collaborators.

• LAB: Lab for the Algorithmic Brain.
• Related papers
  • How does the brain compute with probabilities?
  • Rational thoughts in neural codes.
  • Control when confidence is costly
  • Generalization of graph network inferences in higher-order graphical models.
  • Attention when you need.

0:00 - Intro 3:57 - Xaq's approach 8:28 - Inverse rational control 19:19 - Space of input-output functions 24:48 - Cognition for cognition 27:35 - Theory vs. experiment 40:32 - How does the brain compute with probabilities? 1:03:57 - Normative vs kludge 1:07:44 - Ecological neuroscience 1:20:47 - Representations 1:29:34 - Current projects 1:36:04 - Need a synaptome 1:42:20 - Across scales

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We are in an exciting time in the cross-fertilization of the neurotech industry and the cognitive sciences. My guest today is Chris Rozell, who sits in that space that connects neurotech and brain research. Chris runs the Structured Information for Precision Neuroengineering Lab at Georgia Tech University, and he was just named the inaugural director of Georgia Tech’s Institute for Neuroscience, Neurotechnology, and Society. I think this is the first time on brain inspired we've discussed stimulating brains to treat mental disorders. I think. Today we talk about Chris's work establishing a biomarker from brain recordings of patients with treatment resistant depression, a specific form of depression. These are patients who have deep brain stimulation electrodes implanted in an effort to treat their depression. Chris and his team used that stimulation in conjunction with brain recordings and machine learning tools to predict how effective the treatment will be under what circumstances, and so on, to help psychiatrists better treat their patients. We'll get into the details and surrounding issues. Toward the end we also talk about Chris's unique background and path and approach, and why he thinks interdisciplinary research is so important. He's one of the most genuinely well intentioned people I've met, and I hope you're inspired by his research and his story.

• Structured Information for Precision Neuroengineering Lab.
• Twitter: @crozSciTech.
• Related papers
  • Cingulate dynamics track depression recovery with deep brain stimulation.
• Story Collider: Wired Lives

0:00 - Intro 3:20 - Overview of the study 17:11 - Closed and open loop stimulation 19:34 - Predicting recovery 28:45 - Control knob for treatment 39:04 - Historical and modern brain stimulation 49:07 - Treatment resistant depression 53:44 - Control nodes complex systems 1:01:06 - Explainable generative AI for a biomarker 1:16:40 - Where are we and what are the obstacles? 1:21:32 - Interface Neuro 1:24:55 - Why Chris cares

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The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released.

To explore more neuroscience news and perspectives, visit thetransmitter.org.

Do AI engineers need to emulate some processes and features found only in living organisms at the moment, like how brains are inextricably integrated with bodies? Is consciousness necessary for AI entities if we want them to play nice with us? Is quantum physics part of that story, or a key part, or the key part? Jennifer Prendki believes if we continue to scale AI, it will get us more of the same of what we have today, and that we should look to biology, life, and possibly consciousness to enhance AI. Jennifer is a former particle physicist turned entrepreneur and AI expert, focusing on curating the right kinds and forms of data to train AI, and in that vein she led those efforts at Deepmind on the foundation models ubiquitous in our lives now.

I was curious why someone with that background would come to the conclusion that AI needs inspiration from life, biology, and consciousness to move forward gracefully, and that it would be useful to better understand those processes in ourselves before trying to build what some people call AGI, whatever that is. Her perspective is a rarity among her cohorts, which we also discuss. And get this: she's interested in these topics because she cares about what happens to the planet and to us as a species. Perhaps also a rarity among those charging ahead to dominate profits and win the race

• Jennifer's website: Quantum of Data.
• The blog posts we discuss:
  • The Myth of Emergence
  • Embodiment & Sentience: Why the Body still Matters
  • The Architecture of Synthetic Consciousness
  • On Time and Consciousness
  • Superalignment and the Question of AI Personhood.

Read the transcript.

0:00 - Intro 3:25 - Jennifer's background 13:10 - Consciousness 16:38 - Life and consciousness 23:16 - Superalignment 40:11 - Quantum 1:04:45 - Wetware and biological mimicry 1:15:03 - Neural interfaces 1:16:48 - AI ethics 1:2:35 - AI models are not models 1:27:13 - What scaling will get us 1:39:53 - Current roadblocks 1:43:19 - Philosophy

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The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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A few episodes ago, episode 212, I conversed with John Beggs about how criticality might be an important dynamic regime of brain function to optimize our cognition and behavior. Today we continue and extend that exploration with a few other folks in the criticality world.

Woodrow Shew is a professor and runs the Shew Lab at the University of Arkansas. Keith Hengen is an associate professor and runs the Hengen Lab at Washington University in St. Louis Missouri. Together, they are Hengen and Shew on a recent review paper in Neuron, titled Is criticality a unified setpoint of brain function? In the review they argue that criticality is a kind of homeostatic goal of neural activity, describing multiple properties and signatures of criticality, they discuss multiple testable predictions of their thesis, and they address the historical and current controversies surrounding criticality in the brain, surveying what Woody thinks is all the past studies on criticality, which is over 300. And they offer a account of why many of these past studies did not find criticality, but looking through a modern lens they most likely would. We discuss some of the topics in their paper, but we also dance around their current thoughts about things like the nature and implications of being nearer and farther from critical dynamics, the relation between criticality and neural manifolds, and a lot more. You get to experience Woody and Keith thinking in real time about these things, which I hope you appreciate.

• Shew Lab. @ShewLab
• Hengen Lab.
• Is criticality a unified setpoint of brain function?

Read the transcript.

0:00 - Intro 3:41 - Collaborating 6:22 - Criticality community 14:47 - Tasks vs. Naturalistic 20:50 - Nature of criticality 25:47 - Deviating from criticality 33:45 - Sleep for criticality 38:41 - Neuromodulation for criticality 40:45 - Criticality Definition part 1: scale invariance 43:14 - Criticality Definition part 2: At a boundary 51:56 - New method to assess criticality 56:12 - Types of criticality 1:02:23 - Value of criticality versus other metrics 1:15:21 - Manifolds and criticality 1:26:06 - Current challenges

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The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

Read more about our partnership.

Check out this story:

What, if anything, makes mood fundamentally different from memory?

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Elusive Cures: Why Neuroscience Hasn’t Solved Brain Disorders―and How We Can Change That. Nicole Rust runs the Visual Memory laboratory at the University of Pennsylvania. Her interests have expanded now to include mood and feelings, as you'll hear. And she wrote this book, which contains a plethora of ideas about how we can pave a way forward in neuroscience to help treat mental and brain disorders. We talk about a small plethora of those ideas from her book. which also contains the story partially which will hear of her own journey in thinking about these things from working early on in visual neuroscience to where she is now.

• Nicole's website.
• Elusive Cures: Why Neuroscience Hasn’t Solved Brain Disorders―and How We Can Change That.

0:00 - Intro 6:12 - Nicole's path 19:25 - The grand plan 25:18 - Robustness and fragility 39:15 - Mood 49:25 - Model everything! 56:26 - Epistemic iteration 1:06:50 - Can we standardize mood? 1:10:36 - Perspective neuroscience 1:20:12 - William Wimsatt 1:25:40 - Consciousness

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The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

Read more about our partnership.

Check out this series of essays about representations:

What are we talking about? Clarifying the fuzzy concept of representation in neuroscience and beyond

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What do neuroscientists mean when they use the term representation? That's part of what Luis Favela and Edouard Machery set out to answer a couple years ago by surveying lots of folks in the cognitive sciences, and they concluded that as a field the term is used in a confused and unclear way. Confused and unclear are technical terms here, and Luis and Edouard explain what they mean in the episode. More recently Luis and Edouard wrote a follow-up piece arguing that maybe it's okay for everyone to use the term in slightly different ways, maybe it helps communication across disciplines, perhaps. My three other guests today, Frances Egan, Rosa Cao, and John Krakauer wrote responses to that argument, and on today's episode all those folks are here to further discuss that issue and why it matters. Luis is a part philosopher, part cognitive scientists at Indiana University Bloomington, Edouard is a philosopher and Director of the Center for Philosophy of Science at the University of Pittsburgh, Frances is a philosopher from Rutgers University, Rosa is a neuroscientist-turned philosopher at Stanford University, and John is a neuroscientist among other things, and co-runs the Brain, Learning, Animation, and Movement Lab at Johns Hopkins.

• Luis Favela.
  • Favela's book: The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment
• Edouard Machery.
  • Machery's book: Doing without Concepts
• Frances Egan.
  • Egan's book: Deflating Mental Representation.
• John Krakauer.
• Rosa Cao.
  • Paper mentioned: Putting representations to use.
• The exchange, in order, discussed on this episode:

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The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released.

To explore more neuroscience news and perspectives, visit thetransmitter.org.

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...