BI 222 Nikolay Kukushkin: Minds and Meaning from Nature’s Ideas
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Nikolay Kukushkin is an associate professor at New York University, and a senior scientist at Thomas Carew’s laboratory at the Center for Neural Science. He describes himself as a "molecular philosopher", owing to his day job as a molecular biologist and his broad perspective on how it "hangs together", in the words of Wilfrid Sellers, who in 1962 wrote, “The aim of philosophy, abstractly formulated, is to understand how things in the broadest possible sense of the term hang together in the broadest possible sense of the term”.
That is what Niko does in his book One Hand Clapping: Unraveling the Mystery of the Human Mind.
This book is about essences across spatial scales in nature. More precisely, it's about giving names to what is fundamental, or essential, to how things and processes function in nature. Niko argues those essences are where meaning resides. That's very abstract, and we'll spell it out more during the discussion. But as an example at the small scale, the essences of carbon and oxygen, respectively, are creation and destruction, which allows metabolism to occur in biological organisms. Moving way up the scale, following this essence perspective leads Niko to the conclusion that there is no separation between our minds and the world, and that instead we should embrace the relational aspect of mind and world as a unifying principle. On the way, via evolution, we discuss many more examples, plus some of his own work studying how memory works in individual cells, not just neurons or populations of neurons in brains.
Niko's website.
Twitter: @niko_kukushkin.
Book:
One Hand Clapping: Unraveling the Mystery of the Human Mind
Read the transcript.
0:00 - Intro
9:28 - Studying memory in cells
10:14 - Who the book is for
17:57 - Studying memory in cells
21:53 - What is memory?
29:49 - Book
29:52 - How the book came about
37:56 - Central message of the book
44:07 - Meaning in nature
49:09 - Meaning and essence
51:55 - Multicellularity and ant colonies
57:43 - Eukaryotes and complexification
1:03:38 - Why do we have brains?
1:06:17 - Emergence
1:10:58 - Language
1:12:41 - Human evolution
1:14:41 - Artificial intelligence, meaning and essences
1:25:49 - Consciousness
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1:28:26
BI 221 Ann Kennedy: Theory Beneath the Cortical Surface
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Ann Kennedy is Associate Professor at Scripps Research Institute and runs the Laboratory for Theoretical Neuroscience and Behavior.
Among other things, Ann has been studying how processes important in life, like survival, threat response, motivation, and pain, are mediated through subcortical brain areas like the hypothalamus. She also pays attention to the time course those life processes require, which has led her to consider how the expression of things like proteins help shape neural processes throughout the brain, so we can behave appropriately in those different contexts.
You'll hear us talk about how this is still a pretty open field in theoretical neuroscience, unlike the historically heavy use of theory in popular brain areas throughout the cortex, and the historically narrow focus on spikes or action potentials as the only game in town when it comes to neural computation. We discuss that and I link in the show notes to a commentary piece Ann wrote, in which she argues for both top-down and bottom-up theoretical approaches.
I also link to her papers about the early evolution of nervous systems, how heterogeneity or diversity of neurons is an advantage for neural computations, and we discuss a kaggle competition she developed to benchmark automated behavioral labels of behaving organisms, so that despite different researchers using different recording systems and setups, analyzing those data will produce consistent labels to better compare across labs and aggregated bigger and better data sets.
Laboratory for Theoretical Neuroscience and Behavior.
Social:
@antihebbiann.bsky.social
@Antihebbiann
The Kaggle competition Ann developed to generalize behavior categorization.
Related papersDynamics of neural activity in early nervous system evolution.Theoretical neuroscience has room to grow.
Neural heterogeneity controls computations in spiking neural networks.
A parabrachial hub for the prioritization of survival behavior.
An approximate line attractor in the hypothalamus encodes an aggressive state.
Read the transcript.
0:00 - Intro
3:36 - Why study subcortical areas?
13:30 - Evolution
15:06 - Dynamical systems and time scales
21:32 - NeuroAI
28:37 - Before there were brains
33:11 - Endogenous spontaneous activity
40:09 - Natural vs artificial
43:09 - Different is more - heterogeneity
45:32 - Neuromodulators and neuropeptide functions
55:47 - Heterogeneity: manifolds, subspaces, and gain
1:02:43 - Control knobs
1:09:45 - Theoretical neuroscience has room to grow
1:19:59 - Hypothalamus
1:20:57 - Subcortical vs "higher" cognition
1:24:53 - 4E cognition
1:26:56 - Behavior benchmarking
1:37:26 - Current challenges
1:39:46 - Advice to young researchers
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1:43:37
BI 220 Michael Breakspear and Mac Shine: Dynamic Systems from Neurons to Brains
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What changes and what stays the same as you scale from single neurons up to local populations of neurons up to whole brains? How tuning parameters like the gain in some neural populations affects the dynamical and computational properties of the rest of the system.
Those are the main questions my guests today discuss. Michael Breakspear is a professor of Systems Neuroscience and runs the Systems Neuroscience Group at the University of Newcastle in Australia. Mac Shine is back, he was here a few years ago. Mac runs the Shine Lab at the University of Sidney in Australia.
Michael and Mac have been collaborating on the questions I mentioned above, using a systems approach to studying brains and cognition. The short summary of what they discovered in their first collaboration is that turning up or down the gain across broad networks of neurons in the brain affects integration - working together - and segregation - working apart. They map this gain modulation on to the ascending arousal pathway, in which the locus coeruleus projects widely throughout the brain distributing noradrenaline. At a certain sweet spot of gain, integration and segregation are balanced near a bifurcation point, near criticality, which maximizes properties that are good for cognition.
In their recent collaboration, they used a coarse graining procedure inspired by physics to study the collective dynamics of various sizes of neural populations, going from single neurons to large populations of neurons. Here they found that despite different coding properties at different scales, there are also scale-free properties that suggest neural populations of all sizes, from single neurons to brains, can do cognitive stuff useful for the organism. And they found this is a conserved property across many different species, suggesting it's a universal principle of brain dynamics in general.
So we discuss all that, but to get there we talk about what a systems approach to neuroscience is, how systems neuroscience has changed over the years, and how it has inspired the questions Michael and Mac ask.
Breakspear: Systems Neuroscience Group.
@DrBreaky.
Shine: Shine Lab.
@jmacshine.
Related papers
Dynamic models of large-scale brain activity
Metastable brain waves
The modulation of neural gain facilitates a transition between functional segregation and integration in the brain
Multiscale Organization of Neuronal Activity Unifies Scale-Dependent Theories of Brain Function.
The brain that controls itself.
Metastability demystified — the foundational past, the pragmatic present and the promising future.
Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes.
Related episodes
BI 212 John Beggs: Why Brains Seek the Edge of Chaos
BI 216 Woodrow Shew and Keith Hengen: The Nature of Brain Criticality
BI 121 Mac Shine: Systems Neurobiology
Read the transcript.
0:00 - Intro
4:28 - Neuroscience vs neurobiology
8:01 - Systems approach
26:52 - Physics for neuroscience
33:15 - Gain and bifurcation: earliest collaboration
55:32 - Multiscale organization
1:17:54 - Roadblocks
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1:25:05
BI 219 Xaq Pitkow: Principles and Constraints of Cognition
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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.
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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.
Read the transcript.
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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1:47:11
BI 218 Chris Rozell: Brain Stimulation and AI for Mental Disorders
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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
Read the transcript.
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.
Netherlands