Afleveringen
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Check out this story: Beyond the algorithmic oracle: Rethinking machine learning in behavioral neuroscience
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Neuroscience studies in part the relation between brain activity and behaviors. But, what is a behavior? It's a simple question, but there's no simple answer. For example, you're behaving right now, whatever you're doing, even if you're not doing much. When you cross the street, how many behaviors do you use? When you sleep, what behaviors do you do? Hopefully these simple examples make you think about how difficult it can be call some single movement a behavior.
Nedah Nemati is a philosopher of neuroscience at Columbia University. I met Nedah at a workshop a few months ago, where we chatted about the growing trend in neuroscience toward what is sometimes being called "naturalistic neuroscience," which really means varying levels of allowing organisms to behave more freely, less constrained, than traditional neuroscience experiments that seek to minimize unrelated to the behavior or cognition you want to isolate to study and explain. In more extreme cases, researches will try in the lab to emulate as much as possible the ecological world a particular organism has evolved to exist in, or even perform the experiments out of the lab, in the wild, so to speak. So a good part of our discussion revolves around this trend, and what counts as a "naturalistic" behavior, and how the tools we use to perform experiments shape the experiments and the scientific questions themselves.
Nedah has a neuroscience background, but in her philosophical work she has embedded herself into various neuroscience labs to better understand how the experiences of the researchers themselves, called their lived experiences, shape the assumptions and questions in their science. As an example, we discuss her work looking into the neuroscience of sleep from over a 100 years ago to today. When a modern neuroscientist studies sleep, are they studying the same thing a scientist claimed to be studying 100 years ago, even though they claimed to be studying sleep back then as well?
Nedah's website.Transmitter piece:Beyond the algorithmic oracle: Rethinking machine learning in behavioral neuroscienceRelated papersRethinking Neuroscientific Methodology: Lived Experience in Behavioral StudiesWhat is âNaturalâ about Naturalistic Neuroscience?0:00 - Intro5:00 - Philosopher in a lab20:21 - Sleep as behavior22:22 - How the study of "sleep" has changed27:24 - How tools and methods shape definitions46:07 - Naturalistic neuroscience1:00:47 - Naturalistic vs experimental1:14:32 - How tools change theory1:16:57 - Lived experience1:26:28 - Lived experience vs. bias1:37:09 - AI and engineering in neuroscience1:45:29 - Should a lab hire a philosopher?
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James Harrison is a clinical hypnotist, and author of a new book, Mental Foraging and the Evolution of Memory: An Updated Model of Clinical Hypnosis. As you probably know, hypnosis carries some historical baggage, for example, in terms of how it could be used to manipulate people into having false memories that could be damaging to themselves and those around them. That baggage carries over into modern medical and clinical practice, with many people giving the side eye to hypnosis and disregarding it as a useful tool in the toolkit of treating patients with mental disorders or psychological distress. As a clinician, and as someone who has seen clinical hypnosis work for people, James set about exploring how it might be explained in modern neuroscience terms and concepts. What he ended up with is an account of hypnosis grounded in the neuroscience of state changes, interoception, exteroception, and predictive processing. His hope is that if we get the scientific explanation right of how it works, hypnosis might become more accepted as an effective tool among other psychological treatments.James's website.
Mental Foraging and the Evolution of Memory: An Updated Model of Clinical Hypnosis.@JamesMHarrison_0:00 - Intro4:23 - Why the book?15:21 - Hypnosis as mental foraging21:57 - Freud's unconscious23:51 - How it all works30:27 - Memory reconsolidation36:41 - Historical rejection of hypnosis48:44 - Old practice, new explanations51:55 - Clinician is a guide1:07:31 - Effectiveness1:22:22 - Aristotle's common sense1:30:47 - Allostasis and predictive processing
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Zijn er afleveringen die ontbreken?
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Ehud Ahissar runs the Ahissar Lab at the Weizmann Institute in Israel, where he studies the neuronal and behavioral mechanisms of perception. Ehud sees perception as a closed-loop process, in which organisms actively generate the sensory signals they interpret. Today, we discuss his development of an idea about how this kind of processing can account for our conscious experience. It's a type of dualism Ehud calls "perceptual dualism," different than the dualisms you may already know. I'll use his own words to summarize it hereâŠ
"The idea is that humans inevitably experience the world through two fundamentally different modes: digital brainâbrain (BB) communication and analog brainâworld (BW) interaction. In this view, the mind, and consciousness, emerge as social-like phenomena (in the philosophical sense), grounded in BB communication while constrained by BW interaction."
Take note of the term brain-brain, shortened as BB, and the term brain-world, shortened as BW, because throughout our discussion you'll often hear just BB and BW to refer to those two distinct domains.
So we discuss the ins and outs of his ideas, how came to them via studying active sensing in rodent whisker neurophysiology, how the brain implements this dualism via nested loops of neural circuitry that oppose and interlace with each other at multiple levels, and the idea that attractors, in the dynamical systems sense of attractor, may be the corresponding brain signatures of the digital phenomena that belong to the brain-brain mode of cognition.
Ahissar Lab@ehudahissar; @ehudahissar.bsky.socialRelated papersDigitalâAnalog Perceptual DualityClosed-loop perception: gaps between artificial intelligence and biologyRead the transcript.
0:00 - Intro5:09 - A new kind of dualism7:19 - Ehud's whiskers background14:10 - Digital-analog perceptual dualism26:08 - Digital communication between humans32:26 - Attractors as the digital-analog interface39:50 - Consciousness50:11 - Dynamics and perceptual bottleneck51:47 - Language, AI, and digital symbols1:00:54 - Computation and brains (digital and analog)1:06:43 - Improving AI with event based activation1:11:10 - Dualism1:17:26 - The hard problem of consciousness1:21:26 - BB and BW interaction1:24:55 - Tension between BB and BW1:34:28 - Looking forward1:37:37 - Srange loops
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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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Check out this story:
From genes to dynamics: Examining brain cell types in action may reveal the logic of brain function
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Liset de la Prida is director of the Centro de Neurociencias Cajal in Madrid, Spain, where she runs the Laboratory of Neural Circuits. Today we discuss two main topics.
What drew me to invite Liset was her work on neural manifolds, which we've talked about a lot recently on this podcast. She studies how specific subtypes of neurons affect and control neural manifolds. More on that it in a second, because what drew her to study manifolds was her work on what are known as sharp wave ripples in the hippocampus. Sharp wave ripples are generally quick bursts of oscillatory activity as found in local field potential recordings that accompany little bursty sequences of action potentials fired off by sets of neurons. Those ripples have been associated with a quick replaying of some experience an organism has had, with the thinking that by replaying those sequences of neural activity associated with an event, it's helping to consolidate the memory for that event in the cortex. Like everything else, the story isn't so simple, and we talk about some of the findings that have added to the complexity of understanding what sharp wave ripples are doing, and the varieties of sharp wave ripples.
That varieties part is related to the second main thing we discuss, which is the varieties of neuron subtypes and their roles in shaping the manifolds we've discussed a lot recently. As a reminder, manifolds are dynamic structures along which populations of neural activity unfold over time, and they have proved to be one effective way of making sense of how large populations of neurons coordinate their activity to do useful things for our cognition. Liset is interested in the relation between sharp wave ripples and manifolds, and in how specific subtypes of neurons affect manifolds and cognition in general.
Neural Circuits [email protected]; @LMPrida Book:Brain, space and time: The neuroscience of how we navigate reality, memory, or the futureRelatedFrom genes to dynamics: Examining brain cell types in action may reveal the logic of brain functionCell-type-specific manifold analysis discloses independent geometric transformations in the hippocampal spatial codeFrom cell types to population dynamics: Making hippocampal manifolds physiologically interpretable0:00 - Intro5:29 - Hippocampus9:31 - Sharp wave ripples27:30 - Oscillations and epiphenomena33:37 - Sharp wave ripples to manifolds43:54 - Manifolds and single neuron types49:45 - Hippocampus and granularity of cell types59:23 - Explanation across levels1:19:38 - Manifolds and higher cognition1:29:46 - Brain Space and Time
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Brains encode information in representations that perform computations to make predictions, right? No, no, no, and no. That's Romain Brette's response to those ill-conceived notions that neuroscience relies on to try to explain how cognition works. He uses more words to do that in his new book, The Brain, in Theory, which we discuss today. In the book Romain breaks down how many of the common metaphors we use donât withstand scrutiny, and he offers alternative approaches more in line with what we know about how biological entities work. Along those lines, we discuss his ongoing work understanding the cognition of a single celled organism, the paramecium, and what his views might mean for artificial intelligence. This is a long episode, but there's a lot more to be explored in the book, so I recommend you read it. If you're a patreon supporter, I coaxed Romain back on for another 45 minutes to go deeper on his thoughts about how anticipation is the core of cognition, how predictive processing accounts like active inference miss the mark, and a few other topics.
Romain's website. The Brain, in Theory.0:00 - Intro4:01 - The Brain, In Theory7:10 - Influences13:11 - Process metaphysics18:39 - Observer vs system perspective21:24 - Information in the brain?22:56 - Why this book?29:52 - Computations in the brain52:14 - Behavior is not a computation1:07:20 - Paramecium cognition1:22:02 - How should neuroscientists proceed?1:29:09 - Cognition as collective behavior of autonomous cells1:36:47 - Constraints, causes, and laws1:52:36 - Hopes for the book to influence the field1:55:04 - Thoughts about AI2:02:13 - Computation and goals2:08:17 - Anticipation vs prediction
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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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Check out this story: Neural manifolds: Latest buzzword or pathway to understand the brain?
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Juan Gallego runs the Neocybernetics Lab at the Champalimaud Centre for the Unknown in Lisbon, Portugal, affiliated with the neuroscience of disease and neuroscience programs, and the centre for restorative neurotechnology.
Juan has worked a lot on neural manifolds - the mathematical objects neuroscience is using more and more to describe how big populations of neurons coordinate their activity to do useful things. In fact, he recently gave a short talk that he titled The Manifold Manifesto, because he was asked to be provocative. And he was provocative, suggesting that manifolds are real - as real as chairs and tables are, that they have causal power, and they might be a target of evolution. Of course he talked about his own and others work to support those claims. So today we discuss many of those themes, through the lens of his own and others work, and we talk about what keeps him up at night about the possible limits of using manifolds to connect brain activity with behavior and mental phenomena.
He's not just a manifold person, though. Juan is more broadly interested in motor control and how brains do it.
We also discuss his work in patients with spinal cord injuries, who don't have enough nerve connections to their muscles to actually move, but have enough nerve connections that some signal gets through. Juan and his colleagues can detect that little bit getting through, and use it to infer what behaviors the patients intend to do, and they can use that information to control actions in a computer simulation. The hope is that this will translate to controlling prosthetics to give spinal cord injury patients their mobility again.
Neocybernetics [email protected] papersA neural manifold view of the brain.A neural implementation model of feedback-based motor learning.Conjoint specification of action by neocortex and striatum.Integrating across behaviors and timescales to understand the neural control of movement.Evolutionarily conserved neural dynamics across mice, monkeys, and humans.Read the transcript.
0:00 - Intro4:37 - Manifolds14:30 - Strengths and weaknesses24:32 - Conserved manifolds across animals and species34:31 - Causality and manifolds47:29 - Constraints and causes51:05 - What to measure58:55 - Complexity and manifolds1:10:29 - Juan's background1:14:08 - Prosthetics for spinal cord injuries1:41:06 - Integrating across behaviors and timescales1:46:56 - Conjoint specification of action by neocortex and striatum.
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Tom Griffiths directs both the Computational Cognitive Science Lab and the Princeton Laboratory for Artificial Intelligence at Princeton University. He's been on brain inspired before to talk about his previous book Algorithms to Live By: The Computer Science of Human Decisions, which he co-wrote with Brian Christian. Today he's here to talk about his new book, The Laws of Thought: The Quest for a Mathematical Theory of the Mind. In this book, Tom explains how the three pillars of logic, neural networks, and probability theory complement each other to explain cognition, arguing we are on the doorstep to settling what mathematical principles - the so-called "laws of thought" - underly our cognition. So we discuss a little bit about a lot of things, including the concepts themselves, the people who have generated and worked on those concepts. I should also mentioned, Tom recorded a bunch of his interviews with people he writes about, and he's edited and polished those into a podcast called the Cognition Project, which I have enjoyed after reading the book, and I think you'd enjoy it either before or after you read the book.
Computational Cognitive Science LabPrinceton Laboratory for Artificial IntelligenceSocial: @cocosci_lab; @cocoscilab.bsky.socialBook:The Laws of Thought: The Quest for a Mathematical Theory of the Mind.Podcast: The Cognition ProjectRead the transcript.
0:00 - Intro3:20 - Tom's approach7:19 - 3 pillars of the laws of thought28:24 - Logic and formal systems strip away meaning39:04 - Nature of thought50:35 - Kahneman and Tversky1:015:12 - Enabling constraints and inductive bias1:12:51 - Hidden layers, probability, and hidden markov models1:20:47 - Conscious vs nonconscious1:23:43 - Feelings1:31:26 - Personal
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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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How does brain activity explain your perceptions and your actions? That's what neuroscientists ask. How does the interaction between brain, body, and environment explain your perceptions and actions? That's what ecological psychologists ask⊠sometimes leaving the brain out of the equation altogether. These different approaches to perception and action come with different terms, concepts, underlying assumptions, and targets of explanations.
So what happens when neuroscientists are inspired by ecological psychology but don't necessarily want take on, or are ignorant of, the fundamental principles underlying ecological psychology?
This happens all the time, like how AI was "inspired" by the most rudimentary understanding of how brains work, and took terms from neuroscience like neuron, neural network, and so on, as stand-ins for their models. This has in some sense re-defined what people mean by neuron, and neural network, and how they function and how we should think of them.
Modern neuroscience, with better data collecting tools, has taken a turn toward more naturalistic experimental paradigms to study how brains operate in more ecologically valid situations than what has mostly been used in the history of neuroscience - highly controlled tasks and experimental setups that arguably have very little to do with how organisms evolved to interact with the world to do cognitive things.
One problem with this turn is that we neuroscientists don't have ready-made theoretical tools to deal with the less constrained massive amounts of data the new approach affords. This has led some neuroscientists to seek those theoretical concepts elsewhere. One of those places that offers those theoretical tools is ecological psychology, developed by James and Eleanor Gibson in the mid-20th century, and continued since then by many adherents of the concepts introduced by ecological psychology. Those concepts are very specific with regard to how and what to explain regarding perception and action.
Matthieu de Wit is an associate professor at Muhlenberg College in Pennsylvania, who runst the ECON Lab, as in Ecological Neuroscience. Luis Favela is an associate professor at Indiana University. He's been on before to talk about his book The Ecological Brain. And Vicente Raja is a research fellow at University of Murcia in Spain, and he's been on before to talk about ecological psychology and neuroscience.
With their deep expertise in ecological psychology, they are keenly interested in how neuroscience write large adopts various facets of ecological psychology. Do neuroscientists have it right? Do they need to have it right? Is there something being lost in translation? How should neuroscientists adopt ecological psychology for an ecological neuroscience? That's what we're discussing today.
More broadly, this is also a story about what it's like doing research that isn't part of the current mainstream approach, in this doing ecological psychology under the long shadow cast by the computational mechanistic neuro-centric dominant paradigm in neuroscience currently.
Matthieu de Wit [email protected] Favela.The Ecological Brain: Unifying the Sciences of Brain, Body, and EnvironmentVicente [email protected] Lab.Ecological psychology Previous episodes:BI 223 Vicente Raja: Ecological Psychology Motifs in NeuroscienceBI 190 Luis Favela: The Ecological BrainBI 213 Representations in Minds and BrainsRead the transcript.
0:00 - Intro8:23 - How Louie, Vicente, and Matthieu know each other11:16 - Past present and future of relation between neuroscience and ecological psychology17:02 - Why resistance to integrating neuroscience into ecological psychology?28:26 - What counts as ecological psychology?33:32 - Affordances properly understood40:33 - Ecological information47:58 - Importance of dynamics48:59 - What's at stake?58:27 - Environment intervention1:16:21 - When ecological neuroscience publishes1:31:25 - Neuroscientists escape hatch1:38:04 - Is ecological psychology a theory of everything?
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Jaan Aru is a co-principal investigator of the Natural and Artificial Intelligence Lab at the University of Tartu in Estonia, where he is an associate professor. Jaan's name has kept popping up on papers I've read over the last few years, sometimes alongside other guests I've had on the podcast, like Matthew Larkum and Mac Shine. With those people and others, he has co-authored papers exploring how some of the pesky biological details of brains might be important for our subjective conscious experience, details like dendritic integration, and loops between the cortex and the thalamus. Turns out a recurring theme in his work is to connect lower-level nitty gritty biological details with higher level cognitive functioning. And he has some thoughts about what that might mean for the prospects of consciousness in artificial systems. And we also touch on his more recent interest in understanding the brain basis of insight and creativity, connecting some of the more mundane kinds of insights during problem solving, for example, with some of the more profound kinds of insights during mystical and psychedelic experiences, for example.
Natural & Artificial Intelligence LabSocial: @jaanaru.bsky.socialRelated papersThe feasibility of artificial consciousness through the lens of neuroscienceOn biological and artificial consciousness: A case for biological computationalismCellular mechanisms of conscious processing.Realization experiences: a convergent account of insight and mystical experiences.0:00 - Intro4:21 - Jaan's approach8:51 - Likelihood of machine consciousness18:58 - Across-levels understanding30:23 - Intelligence vs consciousness36:27 - Connecting low-level implementation to cognition45:42 - Organization and constraints52:28 - Thalamocortical loops1:04:18 - Artificial consciousness1:14:34 - Theories of consciousness1:23:16 - Creativity and insight1:37:26 - Science research in Estonia
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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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Michael Shadlen is a professor of neuroscience in the Department of Neuroscience at Columbia University, where he's the principle investigator of the Shadlen Lab. If you study the neural basis of decision making, you already know Shadlen's extensive research, because you are constantly referring to it if you're not already in his lab doing the work. The name Shadlen adorns many many papers relating the behavior and neural activity during decision-making to mathematical models in the drift diffusion family of models. That's not the only work he is known for,
As you may have gleaned from those little intro clips, Michael is with me today to discuss his account of what makes a thought conscious, in the hopes to inspire neuroscience research to eventually tackle the hard problem of consciousness - why and how we have subjective experience.
But Mike's account isn't an account of just consciousness. It's an account of nonconscious thought and conscious thought, and how thoughts go from non-conscious to conscious
His account is inspired by multiple sources and lines of reasoning.
Partly, Shadlen refers to philosophical accounts of cognition by people like Marleau-Ponty and James Gibson, appreciating the embodied and ecological aspects of cognition.
And much of his account derives from his own decades of research studying the neural basis of decision-making mostly using perceptual choice tasks where animals make eye movements to report their decisions.
So we discuss some of that, including what we continue to learn about neurobiological, neurophysiological, and anatomical details of brains, and the possibility of AI consciousness, given Shadlen's account.
Shadlen Lab.Twitter: @shadlen.Decision Making and Consciousness (Chapter in upcoming Principles of Neuroscience textbook).Talk: Decision Making as a Model of thoughtRead the transcript.
0:00 - Intro7:05 - Overview of Mike's account9:10 - Thought as interrogation21:03 - Neurons and thoughts27:05 - Why so many neurons?36:21 - Evolution of Mike's thinking39:48 - Marleau-Ponty, cognition, and meaning44:54 - Naturalistic tasks51:11 - Consciousness58:01 - Martin Buber and relational consciousness1:00:18 - Social and conscious phenomena correlated1:04:17 - Function vs. nature of consciousness1:06:05 - Did language evolve because of consciousness?1:11:11 - Weak phenomenology and long-range feedback1:22:02 - How does interrogation work in the brain?1:26:18 - AI consciousness1:35:49 - The hard problem of consciousness1:39:34 - Meditation and flow
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Tomaso Poggio is the Eugene McDermott professor in the Department of Brain and Cognitive Sciences, an investigator at the McGovern Institute for Brain Research, a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and director of both the Center for Biological and Computational Learning at MIT and the Center for Brains, Minds, and Machines.
Tomaso believes we are in-between building and understanding useful AI That is, we are in between engineering and theory. He likens this stage to the period after Volta invented the battery and Maxwell developed the equations of electromagnetism. Tomaso has worked for decades on the theory and principles behind intelligence and learning in brains and machines. I first learned of him via his work with David Marr, in which they developed "Marr's levels" of analysis that frame explanation in terms of computation/function, algorithms, and implementation. Since then Tomaso has added "learning" as a crucial fourth level. I will refer to you his autobiography to learn more about the many influential people and projects he has worked with and on, the theorems he and others have proved to discover principles of intelligence, and his broader thoughts and reflections.
Right now, he is focused on the principles of compositional sparsity and genericity to explain how deep learning networks can (computationally) efficiently learn useful representations to solve tasks.
Lab website.Tomaso's Autobiography Related papersPosition: A Theory of Deep Learning Must Include Compositional SparsityThe Levels of Understanding framework, revisedBlog post:Poggio lab blog.The Missing Foundations of IntelligenceRead the transcript.
0:00 - Intro9:04 - Learning as the fourth level of Marr's levels12:34 - Engineering then theory (Volta to Maxwell)19:23 - Does AI need theory?26:29 - Learning as the door to intelligence38:30 - Learning in the brain vs backpropagation40:45 - Compositional sparsity49:57 - Math vs computer science56:50 - Generalizability1:04:41 - Sparse compositionality in brains?1:07:33 - Theory vs experiment1:09:46 - Who needs deep learning theory?1:19:51 - Does theory really help? Patreon1:28:54 - Outlook
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Alex is an associate professor of psychology at Vanderbilt University where he heads the Maier Lab. His work in neuroscience spans vision, visual perception, and cognition, studying the neurophysiology of cortical columns, and other related topics. Today, he is here to discuss where his focus has shifted over the past few years, the neuroscience of consciousness. I should say shifted back, since that was his original love, which you'll hear about.
I've known Alex since my own time at Vanderbilt, where I was a postdoc and he was a new faculty member, and I remember being impressed with him then. I was at a talk he gave - job talk or early talk - where it was immediately obvious how passionate and articulate he is about what he does, and I remember he even showed off some of his telescope photography - good pictures of the moon, I remember. Anyway, we always had fun interactions, even if sometimes it was a quick hello as he ran up stairs and down hallways to get wherever he was going, always in a hurry.
Today we discuss why Alex sees integration information theory as the most viable current prospect for explaining consciousness. That is mainly because IIT has developed a formalized mathematical account that hopes to do for consciousness what other math has done for physics, that is, give us what we know as laws of nature. So basically our discussion revolves around everything related to that, like philosophy of science, distinguishing mathematics from "the mathematical", some of the tools he is finding valuable, like category theory, and some of his work measuring the level of consciousness IIT says a whole soccer team has, not just the individuals that comprise the team.
Maier LabAstonishing Hypothesis (Alex's youtube channel)Twitter: Sensation and Perception textbook (in-the-making)Related papersLinking the Structure of Neuronal Mechanisms to the Structure of QualiaInformation integration and the latent consciousness of human groupsNeural mechanisms of predictive processing: a collaborative community experiment through the OpenScope programVarious things Alex mentioned:âAn Antiphilosophy of Mathematics,â Peter J. Freyd youtube video about "the mathematical".David Kaiser's playlist on modern physics.Here's a link to the Integrated Information Theory Wiki.Read the transcript.
0:00 - Intro4:27 - Discovering consciousness science11:23 - Laws of perception15:48 - Integrated information theory and mathematical formalism23:54 - Theories of consciousness without math28:18 - Computation metaphor34:44 - Formalized mathematics is the way36:56 - Category theory41:42 - Structuralism51:09 - The mathematical54:33 - Metaphysics of the mathematical59:52 - Yoneda Lemma1:12:05 - What's real1:26:22 - Measuring consciousness of a soccer team1:35:03 - Assumptions and approximations of IIT1:43:13 - Open science
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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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Can you look at all the synaptic connections of a brain, and tell me one nontrivial memory from the organism that has that brain? If so, you shall win the $100,000 prize from the Aspirational Neuroscience group.
I was recently invited for the second time to chair a panel of experts to discuss that question and all the issues around that question - how to decode a non-trivial memory from a static map of synaptic connectivity.
Before I play that recording, let me set the stage a bit more.
Aspirational Neuroscience is a community of neuroscientists run by Kenneth Hayworth, with the goal, from their website, to "balance aspirational thinking with respect to the long-term implications of a successful neuroscience with practical realism about our current state of ignorance and knowledge." One of those aspirations is to decoding things - memories, learned behaviors, and so on - from static connectomes. They hold satellite events at the SfN conference, and invite experts in connectomics from academia and from industry to share their thoughts and progress that might advance that goal.
In this panel discussion, we touch on multiple relevant topics. One question is what is the right experimental design or designs that would answer whether we are decoding memory - what is a benchmark in various model organisms, and for various theoretical frameworks? We discuss some of the obstacles in the way, both technologically and conceptually. Like the fact that proofreading connectome connections - manually verifying and editing them - is a giant bottleneck, or like the very definition of memory, what counts as a memory, let alone a "nontrivial" memory, and so on. And they take lots of questions from the audience as well.
I apologize the audio is not crystal clear in this recording. I did my best to clean it up, and I take full blame for not setting up my audio recorder to capture the best sound. So, if you are a listener, I'd encourage you to check out the video version, which also has subtitles throughout for when the language isn't clear.
Anyway, this is a fun and smart group of people, and I look forward to another one next year I hope.
The last time I did this was episode 180, BI 180, which I link to in the show notes. Before that I had on Ken Hayworth, whom I mentioned runs Aspirational Neuroscience, and Randal Koene, who is on the panel this time. They were on to talk about the future possibility of uploading minds to computers based on connectomes. That was episode 103.
Aspirational NeurosciencePanelMichaĆ [email protected] scientist (connectomics) with Google Research, automated neural tracing expertSven [email protected] fellow at the Allen Institute, first-author on first full Drosophila connectome paperHelene [email protected] leader at Ernst Strungmann Institute, hippocampus connectome & EM expertAndrew [email protected] of E11 Bio, expansion microscopy & viral tracing expert Randal KoeneFounder of the Carboncopies Foundation, computational neuroscientist dedicated to the problem of brain emulation.Related episodes:BI 103 Randal Koene and Ken Hayworth: The Road to Mind UploadingBI 180 Panel Discussion: Long-term Memory Encoding and Connectome Decoding -
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Tatiana Engel runs the Engel lab at Princeton University in the Princeton Neuroscience Institute. She's also part of the International Brain Laboratory, a massive across-lab, across-world, collaboration which you'll hear more about. My main impetus for inviting Tatiana was to talk about two projects she's been working on. One of those is connecting the functional dynamics of cognition with the connectivity of the underlying neural networks on which those dynamics unfold. We know the brain is high-dimensional - it has lots of interacting connections, we know the activity of those networks can often be described by lower-dimensional entities called manifolds, and Tatiana and her lab work to connect those two processes with something they call latent circuits. So you'll hear about that, you'll also hear about how the timescales of neurons across the brain are different but the same, why this is cool and surprising, and we discuss many topics around those main topics.
Engel [email protected] Brain Laboratory.Related papers:Latent circuit inference from heterogeneous neural responses during cognitive tasksThe dynamics and geometry of choice in the premotor cortex.A unifying perspective on neural manifolds and circuits for cognitionBrain-wide organization of intrinsic timescales at single-neuron resolutionSingle-unit activations confer inductive biases for emergent circuit solutions to cognitive tasks.0:00 - Intro3:03 - No central executive5:01 - International brain lab15:57 - Tatiana's background24:49 - Dynamical systems17:48 - Manifolds33:10 - Latent task circuits47:01 - Mixed selectivity1:00:21 - Internal and external dynamics1:03:47 - Modern vs classical modeling1:14:30 - Intrinsic timescales1:26:05 - Single trial dynamics1:29:59 - Future of manifolds
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Henk de Regt is a professor of Philosophy of Science and the director of the Institute for Science in Society at Radboud University. Henk wrote the book on Understanding. Literally, he wrote what has become a classic in philosophy of science, Understanding Scientific Understanding.
Henks' account of understanding goes roughly like this, but you can learn more in his book and other writings. To claim you understand something in science requires that you can produce a theory-based explanation of whatever you claim to understand, and it depends on you having the right scientific skills to be able to work productively with that theory - for example, making qualitative predictions about it without performing calculations. So understanding is contextual and depends on the skills of the understander.
There's more nuance to it, so like I said you should read the book, but this account of understanding distinguishes it from explanation itself, and distinguishes it from other accounts of understanding, which take understanding to be either a personal subjective sense - that feeling of something clicking in your mind - or simply the addition of more facts about something.
In this conversation, we revisit Henk's work on understanding, and how it touches on many other topics, like realism, the use of metaphors, how public understanding differs from expert understanding, idealization and abstraction in science, and so on.
And, because Henk's kind of understanding doesn't depend on subjective awareness or things being true, he and his cohorts have begun working on whether there could be a benchmark for degrees of understanding, to possibly asses whether AI demonstrates understanding, and to use as a common benchmark for humans and machines.
Google Scholar pageSocial: @henkderegt.bsky.social; Book:Understanding Scientific Understanding.Related papersTowards a benchmark for scientific understanding in humans and machinesMetaphors as tools for understanding in science communication among experts and to the publicTwo scientific perspectives on nerve signal propagation: how incompatible approaches jointly promote progress in explanatory understanding0:00 - Intro10:13 - Philosophy of explanation vs understanding14:32 - Different accounts of understanding20:29 - Henk's account of understanding26:47 - What counts as intelligible?34:09 - Hodgkin and Huxley alternative37:54 - Familiarity vs understanding44:42 - Measuring understanding1:02:53 - Machine understanding1:16:39 - Non-factive understanding1:23:34 - Abstraction vs understanding1:31:07 - Public understanding of science1:41:35 - Reflections on the book
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My guest today is Dan Nicholson, Assistant Professor of Philosophy at George Mason University, here to talk about his little book, What Is Life? Revisited. Erwin Schrödinger's What Is Life is a famous book that people point to as having predicted DNA and influenced and inspired many well-known biologists ushering in the molecular biology revolution. But Schrödinger was a physicist, not a biologist, and he spent very little time and effort toward understanding biology.
What was he up to, why did he write this "famous little book"? Schrödinger had an agenda, a physics agenda. He wanted to save the older deterministic version of quantum physics from the new indeterministic version. When Dan was on the podcast a few years ago, we talked about the machine view of biological systems, how everything has become a "mechanism", and how that view fails to capture what modern science is actually telling us, that organisms are unlike machines in important ways. That work of Dan's led him down this path to Schrödinger's What Is Life, which he argues was a major contributor to that machine metaphor so ubiquitous today in biology. One of the reasons I'm interested in this kind of work is because the cognitive sciences, including neuroscience and artificial intelligence, inherited this mechanistic perspective, and swallowed it so hard that if you don't include the word "mechanism" in your research paper, you're vastly decreasing your chances of getting your work published, when in fact the mechanistic perspective is one super useful perspective among many.
Danâs website. Google Scholar.Social: @NicholsonHPBio; @djnicholson.bsky.socialWhat Is Life? RevisitedPrevious episode:BI 150 Dan Nicholson: Machines, Organisms, ProcessesRead the transcript.
0:00 - Intro7:27 - Why Schrodinger wrote What is Life15:13 - Aperiodic crystal and the meaning of code21:39 - Order-from-order, order-from-disorder28:32 - Appeal to authority37:48 - Cell as machine39:33 - Relation between DNA and organism (development)44:44 - Negentropy53:54 - Original contributions58:54 - Mechanistic metaphor in neuroscience1:16:05 - What's the lesson?1:28:06 - Historical sleuthing1:39:49 - Modern philosophy of biology
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Vicente Raja is a research fellow at University of Murcia in Spain, where he is also part of the Minimal Intelligence Lab run by Paco Cavo, where they study plant behavior, and he is external affiliate faculty of the Rotman Institute of Philosophy at Western University. He is a philosopher, and he is a cognitive scientist, and he specializes in applying concepts from ecological psychology to understand how brains, and organisms, including plants, get about in the world.
We talk about many facets of his research, both philosophical and scientific, and maybe the best way to describe the conversation is a tour among many of the concepts in ecological psychology - like affordances, ecological information, direct perception, and resonance, and how those concepts do and don't, and should or shouldnât, contribute to our understanding of brains and minds.
We also discuss Vicente's use of the term motif to describe scientific concepts that allow different researches to study roughly the same things even though they have different definitions for those things, and toward the end we touch on his work studying plant behavior.
MINT Lab.Book: Ecological psychologySocial: @diovicen.bsky.socialRelated papersIn search for an alternative to the computer metaphor of the mind and brainEmbodiment and cognitive neuroscience: the forgotten tales.The motifs of radical embodied neuroscienceThe Dynamics of Plant NutationEcological Resonance Is Reflected in Human Brain ActivityAffordances are for life (and not just for maximizing reproductive fitness)Two species of realismLots of previous guests and topics mentioned:BI 152 Michael L. Anderson: After Phrenology: Neural ReuseBI 190 Luis Favela: The Ecological BrainBI 191 Damian Kelty-Stephen: Fractal Turbulent Cascading IntelligenceRead the transcript.
0:00 - Intro4:55 - Affordances and neuroscience13:46 - Motifs39:41- Reconciling neuroscience and ecological psychology1:07:55 - Predictive processing1:15:32 - Resonance1:23:00 - Biggest holes in ecological psychology1:29:50 - Plant cognition
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Nikolay Kukushkin is an associate professor at New York University, and a senior scientist at Thomas Carewâs 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, âThe 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 termâ.
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 MindRead the transcript.
0:00 - Intro9:28 - Studying memory in cells10:14 - Who the book is for17:57 - Studying memory in cells21:53 - What is memory?29:49 - Book29:52 - How the book came about37:56 - Central message of the book44:07 - Meaning in nature49:09 - Meaning and essence51:55 - Multicellularity and ant colonies57:43 - Eukaryotes and complexification1:03:38 - Why do we have brains?1:06:17 - Emergence1:10:58 - Language1:12:41 - Human evolution1:14:41 - Artificial intelligence, meaning and essences1:25:49 - Consciousness
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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 - Intro3:36 - Why study subcortical areas?13:30 - Evolution15:06 - Dynamical systems and time scales21:32 - NeuroAI28:37 - Before there were brains33:11 - Endogenous spontaneous activity40:09 - Natural vs artificial43:09 - Different is more - heterogeneity45:32 - Neuromodulators and neuropeptide functions55:47 - Heterogeneity: manifolds, subspaces, and gain1:02:43 - Control knobs1:09:45 - Theoretical neuroscience has room to grow1:19:59 - Hypothalamus1:20:57 - Subcortical vs "higher" cognition1:24:53 - 4E cognition1:26:56 - Behavior benchmarking1:37:26 - Current challenges1:39:46 - Advice to young researchers
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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 [email protected]: Shine [email protected] papersDynamic models of large-scale brain activityMetastable brain wavesThe modulation of neural gain facilitates a transition between functional segregation and integration in the brainMultiscale Organization of Neuronal Activity Unifies Scale-Dependent Theories of Brain Function.The brain that controls itself.Metastability demystified â 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 episodesBI 212 John Beggs: Why Brains Seek the Edge of ChaosBI 216 Woodrow Shew and Keith Hengen: The Nature of Brain CriticalityBI 121 Mac Shine: Systems NeurobiologyRead the transcript.
0:00 - Intro4:28 - Neuroscience vs neurobiology8:01 - Systems approach26:52 - Physics for neuroscience33:15 - Gain and bifurcation: earliest collaboration55:32 - Multiscale organization1:17:54 - Roadblocks
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