BI 215 Xiao-Jing Wang: Theoretical Neuroscience Comes of Age
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Xiao-Jing Wang is a Distinguished Global Professor of Neuroscience at NYU
Xiao-Jing was born and grew up in China, spent 8 years in Belgium studying theoretical physics like nonlinear dynamical systems and deterministic chaos. And as he says it, he arrived from Brussels to California as a postdoc, and in one day switched from French to English, from European to American culture, and physics to neuroscience. I know Xiao-Jing as a legend in non-human primate neurophysiology and modeling, paving the way for the rest of us to study brain activity related cognitive functions like working memory and decision-making.
He has just released his new textbook, Theoretical Neuroscience: Understanding Cognition, which covers the history and current research on modeling cognitive functions from the very simple to the very cognitive. The book is also somewhat philosophical, arguing that we need to update our approach to explaining how brains function, to go beyond Marr's levels and enter a cross-level mechanistic explanatory pursuit, which we discuss. I just learned he even cites my own PhD research, studying metacognition in nonhuman primates - so you know it's a great book. Learn more about Xiao-Jing and the book in the show notes. It was fun having one of my heroes on the podcast, and I hope you enjoy our discussion.
Computational Laboratory of Cortical Dynamics
Book: Theoretical Neuroscience: Understanding Cognition.
Related papers
Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory.
Macroscopic gradients of synaptic excitation and inhibition across the neocortex.
Theory of the multiregional neocortex: large-scale neural dynamics and distributed cognition.
0:00 - Intro
3:08 - Why the book now?
11:00 - Modularity in neuro vs AI
14:01 - Working memory and modularity
22:37 - Canonical cortical microcircuits
25:53 - Gradient of inhibitory neurons
27:47 - Comp neuro then and now
45:35 - Cross-level mechanistic understanding
1:13:38 - Bifurcation
1:24:51 - Bifurcation and degeneracy
1:34:02 - Control theory
1:35:41 - Psychiatric disorders
1:39:14 - Beyond dynamical systems
1:43:447 - Mouse as a model
1:48:11 - AI needs a PFC
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BI 214 Nicole Rust: How To Actually Fix Brains and Minds
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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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To explore more neuroscience news and perspectives, visit thetransmitter.org.
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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BI 213 Representations in Minds and Brains
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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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To explore more neuroscience news and perspectives, visit thetransmitter.org.
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:
Investigating the concept of representation in the neural and psychological sciences.
The concept of representation in the brain sciences: The current status and ways forward.
Commentaries:
Assessing the landscape of representational concepts: Commentary on Favela and Machery.
Comments on Favela and Machery's The concept of representation in the brain sciences: The current status and ways forward.
Where did real representations go? Commentary on: The concept of representation in the brain sciences: The current status and ways forward by Favela and Machery.
Reply to commentaries:
Contextualizing, eliminating, or glossing: What to do with unclear scientific concepts like representation.
0:00 - Intro
3:55 - What is a representation to a neuroscientist?
14:44 - How to deal with the dilemma
21:20 - Opposing views
31:00 - What's at stake?
51:10 - Neural-only representation
1:01:11 - When "representation" is playing a useful role
1:12:56 - The role of a neuroscientist
1:39:35 - The purpose of "representational talk"
1:53:03 - Non-representational mental phenomenon
1:55:53 - Final thoughts
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BI 212 John Beggs: Why Brains Seek the Edge of Chaos
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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.
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 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
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BI 211 COGITATE: Testing Theories of 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.
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.
Rony Hirschhorn, Alex Lepauvre, and Oscar Ferrante are three of many many scientists that comprise the COGITATE group. COGITATE is an adversarial collaboration project to test theories of consciousness in humans, in this case testing the integrated information theory of consciousness and the global neuronal workspace theory of consciousness. I said it's an adversarial collaboration, so what does that mean. It's adversarial in that two theories of consciousness are being pitted against each other. It's a collaboration in that the proponents of the two theories had to agree on what experiments could be performed that could possibly falsify the claims of either theory. The group has just published the results of the first round of experiments in a paper titled Adversarial testing of global neuronal workspace and integrated information theories of consciousness, and this is what Rony, Alex, and Oscar discuss with me today.
The short summary is that they used a simple task and measured brain activity with three different methods: EEG, MEG, and fMRI, and made predictions about where in the brain correlates of consciousness should be, how that activity should be maintained over time, and what kind of functional connectivity patterns should be present between brain regions. The take home is a mixed bag, with neither theory being fully falsified, but with a ton of data and results for the world to ponder and build on, to hopefully continue to refine and develop theoretical accounts of how brains and consciousness are related.
So we discuss the project itself, many of the challenges they faced, their experiences and reflections working on it and on coming together as a team, the nature of working on an adversarial collaboration, when so much is at stake for the proponents of each theory, and, as you heard last episode with Dean Buonomano, when one of the theories, IIT, is surrounded by a bit of controversy itself regarding whether it should even be considered a scientific theory.
COGITATE.
Oscar Ferrante. @ferrante_oscar
Rony Hirschhorn. @RonyHirsch
Alex Lepauvre. @LepauvreAlex
Paper: Adversarial testing of global neuronal workspace and integrated information theories of consciousness.
BI 210 Dean Buonomano: Consciousness, Time, and Organotypic Dynamics
Read the transcript.
0:00 - Intro
4:00 - COGITATE
17:42 - How the experiments were developed
32:37 - How data was collected and analyzed
41:24 - Prediction 1: Where is consciousness?
47:51 - The experimental task
1:00:14 - Prediction 2: Duration of consciousness-related activity
1:18:37 - Prediction 3: Inter-areal communication
1:28:28 - Big picture of the results
1:44:25 - Moving forward
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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