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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. 

This is the second conversation I had while teamed up with Gaute Einevoll at a workshop on NeuroAI in Norway. In this episode, Gaute and I are joined by Cristina Savin and Tim Vogels. Cristina shares how her lab uses recurrent neural networks to study learning, while Tim talks about his long-standing research on synaptic plasticity and how AI tools are now helping to explore the vast space of possible plasticity rules.

We touch on how deep learning has changed the landscape, enhancing our research but also creating challenges with the "fashion-driven" nature of science today. We also reflect on how these new tools have changed the way we think about brain function without fundamentally altering the structure of our questions.

Be sure to check out Gaute's Theoretical Neuroscience podcast as well!

• Mikkel Lepperød
• Cristina Savin
• Tim Vogels
  • Twitter: @TPVogels
• Gaute Einevoll
  • Twitter: @GauteEinevoll
  • Gaute's Theoretical Neuroscience podcast.
• Validating models: How would success in NeuroAI look like?

Read the transcript, provided by The Transmitter.

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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.

This is the first of two less usual episodes. I was recently in Norway at a NeuroAI workshop called Validating models: How would success in NeuroAI look like? What follows are a few recordings I made with my friend Gaute Einevoll. Gaute has been on this podcast before, but more importantly he started his own podcast a while back called Theoretical Neuroscience, which you should check out.

Gaute and I introduce the episode, then briefly speak with Mikkel Lepperød, one of the organizers of the workshop. In this first episode, we're then joined by Ken Harris and Andreas Tolias to discuss how AI has influenced their research, thoughts about brains and minds, and progress and productivity.

• Validating models: How would success in NeuroAI look like?
• Mikkel Lepperød
• Andreas Tolias
  • Twitter: @AToliasLab
• Ken Harris
  • Twitter: @kennethd_harris
• Gaute Einevoll
  • Twitter: @GauteEinevoll
  • Gaute's Theoretical Neuroscience podcast.

Read the transcript, provided by The Transmitter.

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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. 

The Transmitter has provided a transcript for this episode.

Vijay Namoodiri runs the Nam Lab at the University of California San Francisco, and Ali Mojebi is an assistant professor at the University of Wisconsin-Madison. Ali as been on the podcast before a few times, and he's interested in how neuromodulators like dopamine affect our cognition. And it was Ali who pointed me to Vijay, because of some recent work Vijay has done reassessing how dopamine might function differently than what has become the classic story of dopamine's function as it pertains to learning. The classic story is that dopamine is related to reward prediction errors. That is, dopamine is modulated when you expect reward and don't get it, and/or when you don't expect reward but do get it. Vijay calls this a "prospective" account of dopamine function, since it requires an animal to look into the future to expect a reward. Vijay has shown, however, that a retrospective account of dopamine might better explain lots of know behavioral data. This retrospective account links dopamine to how we understand causes and effects in our ongoing behavior. So in this episode, Vijay gives us a history lesson about dopamine, his newer story and why it has caused a bit of controversy, and how all of this came to be.

I happened to be looking at the Transmitter the other day, after I recorded this episode, and low and behold, there was an article titles Reconstructing dopamine’s link to reward. Vijay is featured in the article among a handful of other thoughtful researchers who share their work and ideas about this very topic. Vijay wrote his own piece as well: Dopamine and the need for alternative theories. So check out those articles for more views on how the field is reconsidering how dopamine works.

• Nam Lab.
• Mohebi & Associates (Ali's Lab).
• Twitter:
  • @vijay_mkn
  • @mohebial
• Transmitter
  • Dopamine and the need for alternative theories.
  • Reconstructing dopamine’s link to reward.
• Related papers
  • Mesolimbic dopamine release conveys causal associations.
  • Mesostriatal dopamine is sensitive to changes in specific cue-reward contingencies.
  • What is the state space of the world for real animals?
  • The learning of prospective and retrospective cognitive maps within neural circuits
• Further reading
  • (Ali's paper): Dopamine transients follow a striatal gradient of reward time horizons.
  • Ali listed a bunch of work on local modulation of DA release:
    • Local control of striatal dopamine release.
    • Synaptic-like axo-axonal transmission from striatal cholinergic interneurons onto dopaminergic fibers.
    • Spatial and temporal scales of dopamine transmission.
    • Striatal dopamine neurotransmission: Regulation of release and uptake.
    • Striatal Dopamine Release Is Triggered by Synchronized Activity in Cholinergic Interneurons.
    • An action potential initiation mechanism in distal axons for the control of dopamine release.

Read the transcript, produced by The Transmitter.

0:00 - Intro 3:42 - Dopamine: the history of theories 32:54 - Importance of learning and behavior studies 39:12 - Dopamine and causality 1:06:45 - Controversy over Vijay's findings

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

Kim Stachenfeld embodies the original core focus of this podcast, the exploration of the intersection between neuroscience and AI, now commonly known as Neuro-AI. That's because she walks both lines. Kim is a Senior Research Scientist at Google DeepMind, the AI company that sprang from neuroscience principles, and also does research at the Center for Theoretical Neuroscience at Columbia University. She's been using her expertise in modeling, and reinforcement learning, and cognitive maps, for example, to help understand brains and to help improve AI. I've been wanting to have her on for a long time to get her broad perspective on AI and neuroscience.

We discuss the relative roles of industry and academia in pursuing various objectives related to understanding and building cognitive entities

She's studied the hippocampus in her research on reinforcement learning and cognitive maps, so we discuss what the heck the hippocampus does since it seems to implicated in so many functions, and how she thinks of reinforcement learning these days.

Most recently Kim at Deepmind has focused on more practical engineering questions, using deep learning models to predict things like chaotic turbulent flows, and even to help design things like bridges and airplanes. And we don't get into the specifics of that work, but, given that I just spoke with Damian Kelty-Stephen, who thinks of brains partially as turbulent cascades, Kim and I discuss how her work on modeling turbulence has shaped her thoughts about brains.

• Kim's website.
• Twitter: @neuro_kim.
• Related papers
  • Scaling Laws for Neural Language Models.
  • Emergent Abilities of Large Language Models.
  • Learned simulators:
    • Learned coarse models for efficient turbulence simulation.
    • Physical design using differentiable learned simulators.

Check out the transcript, provided by The Transmitter.

0:00 - Intro 4:31 - Deepmind's original and current vision 9:53 - AI as tools and models 12:53 - Has AI hindered neuroscience? 17:05 - Deepmind vs academic work balance 20:47 - Is industry better suited to understand brains? 24?42 - Trajectory of Deepmind 27:41 - Kim's trajectory 33:35 - Is the brain a ML entity? 36:12 - Hippocampus 44:12 - Reinforcement learning 51:32 - What does neuroscience need more and less of? 1:02:53 - Neuroscience in a weird place? 1:06:41 - How Kim's questions have changed 1:16:31 - Intelligence and LLMs 1:25:34 - Challenges

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Àlex Gómez-Marín heads The Behavior of Organisms Laboratory at the Institute of Neuroscience in Alicante, Spain. He's one of those theoretical physicist turned neuroscientist, and he has studied a wide range of topics over his career. Most recently, he has become interested in what he calls the "edges of consciousness", which encompasses the many trying to explain what may be happening when we have experiences outside our normal everyday experiences. For example, when we are under the influence of hallucinogens, when have near-death experiences (as Alex has), paranormal experiences, and so on.

So we discuss what led up to his interests in these edges of consciousness, how he now thinks about consciousness and doing science in general, how important it is to make room for all possible explanations of phenomena, and to leave our metaphysics open all the while.

• Alex's website: The Behavior of Organisms Laboratory.
• Twitter: @behaviOrganisms.
• Previous episodes:
  • BI 168 Frauke Sandig and Eric Black w Alex Gomez-Marin: AWARE: Glimpses of Consciousness.
  • BI 136 Michel Bitbol and Alex Gomez-Marin: Phenomenology.
• Related:
  • The Consciousness of Neuroscience.
  • Seeing the consciousness forest for the trees.
  • The stairway to transhumanist heaven.

0:00 - Intro 4:13 - Evolving viewpoints 10:05 - Near-death experience 18:30 - Mechanistic neuroscience vs. the rest 22:46 - Are you doing science? 33:46 - Where is my. mind? 44:55 - Productive vs. permissive brain 59:30 - Panpsychism 1:07:58 - Materialism 1:10:38 - How to choose what to do 1:16:54 - Fruit flies 1:19:52 - AI and the Singularity

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Damian Kelty-Stephen is an experimental psychologist at State University of New York at New Paltz. Last episode with Luis Favela, we discussed many of the ideas from ecological psychology, and how Louie is trying to reconcile those principles with those of neuroscience. In this episode, Damian and I in some ways continue that discussion, because Damian is also interested in unifying principles of ecological psychology and neuroscience. However, he is approaching it from a different perspective that Louie. What drew me originally to Damian was a paper he put together with a bunch of authors offering their own alternatives to the computer metaphor of the brain, which has come to dominate neuroscience. And we discuss that some, and I'll link to the paper in the show notes. But mostly we discuss Damian's work studying the fractal structure of our behaviors, connecting that structure across scales, and linking it to how our brains and bodies interact to produce our behaviors. Along the way, we talk about his interests in cascades dynamics and turbulence to also explain our intelligence and behaviors. So, I hope you enjoy this alternative slice into thinking about how we think and move in our bodies and in the world.

• Damian's website.
• Related papers
  • In search for an alternative to the computer metaphor of the mind and brain.
  • Multifractal emergent processes: Multiplicative interactions override nonlinear component properties.

0:00 - Intro 2:34 - Damian's background 9:02 - Brains 12:56 - Do neuroscientists have it all wrong? 16:56 - Fractals everywhere 28:01 - Fractality, causality, and cascades 32:01 - Cascade instability as a metaphor for the brain 40:43 - Damian's worldview 46:09 - What is AI missing? 54:26 - Turbulence 1:01:02 - Intelligence without fractals? Multifractality 1:10:28 - Ergodicity 1:19:16 - Fractality, intelligence, life 1:23:24 - What's exciting, changing viewpoints

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Luis Favela is an Associate Professor at Indiana University Bloomington. He is part philosopher, part cognitive scientist, part many things, and on this episode we discuss his new book, The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment.

In the book, Louie presents his NeuroEcological Nexus Theory, or NExT, which, as the subtitle says, proposes a way forward to tie together our brains, our bodies, and the environment; namely it has a lot to do with the complexity sciences and manifolds, which we discuss. But the book doesn't just present his theory. Among other things, it presents a rich historical look into why ecological psychology and neuroscience haven't been exactly friendly over the years, in terms of how to explain our behaviors, the role of brains in those explanations, how to think about what minds are, and so on. And it suggests how the two fields can get over their differences and be friends moving forward. And I'll just say, it's written in a very accessible manner, gently guiding the reader through many of the core concepts and science that have shaped ecological psychology and neuroscience, and for that reason alone I highly it.

Ok, so we discuss a bunch of topics in the book, how Louie thinks, and Louie gives us some great background and historical lessons along the way.

• Luis' website.
• Book:
  • The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment

0:00 - Intro 7:05 - Louie's target with NEXT 20:37 - Ecological psychology and grid cells 22:06 - Why irreconcilable? 28:59 - Why hasn't ecological psychology evolved more? 47:13 - NExT 49:10 - Hypothesis 1 55:45 - Hypothesis 2 1:02:55 - Artificial intelligence and ecological psychology 1:16:33 - Manifolds 1:31:20 - Hypothesis 4: Body, low-D, Synergies 1:35:53 - Hypothesis 5: Mind emerges 1:36:23 - Hypothesis 6:

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Jovo, as you'll learn, is theoretically oriented, and enjoys the formalism of mathematics to approach questions that begin with a sense of wonder. So after I learn more about his overall approach, the first topic we discuss is the world's currently largest map of an entire brain... the connectome of an insect, the fruit fly. We talk about his role in this collaborative effort, what the heck a connectome is, why it's useful and what to do with it, and so on.

The second main topic we discuss is his theoretical work on what his team has called prospective learning. Prospective learning differs in a fundamental way from the vast majority of AI these days, which they call retrospective learning. So we discuss what prospective learning is, and how it may improve AI moving forward.

At some point there's a little audio/video sync issues crop up, so we switched to another recording method and fixed it... so just hang tight if you're viewing the podcast... it'll get better soon.

0:00 - Intro 05:25 - Jovo's approach 13:10 - Connectome of a fruit fly 26:39 - What to do with a connectome 37:04 - How important is a connectome? 51:48 - Prospective learning 1:15:20 - Efficiency 1:17:38 - AI doomerism

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Jolande Fooken is a post-postdoctoral researcher interested in how we move our eyes and move our hands together to accomplish naturalistic tasks. Hand-eye coordination is one of those things that sounds simple and we do it all the time to make meals for our children day in, and day out, and day in, and day out. But it becomes way less seemingly simple as soon as you learn how we make various kinds of eye movements, and how we make various kinds of hand movements, and use various strategies to do repeated tasks. And like everything in the brain sciences, it's something we don't have a perfect story for yet. So, Jolande and I discuss her work, and thoughts, and ideas around those and related topics.

• Jolande's website.
• Twitter: @ookenfooken.
• Related papers
  • I am a parent. I am a scientist.
  • Eye movement accuracy determines natural interception strategies.
  • Perceptual-cognitive integration for goal-directed action in naturalistic environments.

0:00 - Intro 3:27 - Eye movements 8:53 - Hand-eye coordination 9:30 - Hand-eye coordination and naturalistic tasks 26:45 - Levels of expertise 34:02 - Yarbus and eye movements 42:13 - Varieties of experimental paradigms, varieties of viewing the brain 52:46 - Career vision 1:04:07 - Evolving view about the brain 1:10:49 - Coordination, robots, and AI

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Recently I was invited to moderate a panel at the annual Computational and Systems Neuroscience, or COSYNE, conference. This year was the 20th anniversary of COSYNE, and we were in Lisbon Porturgal. The panel goal was to discuss the relationship between neuroscience and AI. The panelists were Tony Zador, Alex Pouget, Blaise Aguera y Arcas, Kim Stachenfeld, Jonathan Pillow, and Eva Dyer. And I'll let them introduce themselves soon. Two of the panelists, Tony and Alex, co-founded COSYNE those 20 years ago, and they continue to have different views about the neuro-AI relationship. Tony has been on the podcast before and will return soon, and I'll also have Kim Stachenfeld on in a couple episodes. I think this was a fun discussion, and I hope you enjoy it. There's plenty of back and forth, a wide range of opinions, and some criticism from one of the audience questioners. This is an edited audio version, to remove long dead space and such. There's about 30 minutes of just panel, then the panel starts fielding questions from the audience.

• COSYNE.

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Mazviita Chirimuuta is a philosopher at the University of Edinburgh. Today we discuss topics from her new book, The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience.

She largely argues that when we try to understand something complex, like the brain, using models, and math, and analogies, for example - we should keep in mind these are all ways of simplifying and abstracting away details to give us something we actually can understand. And, when we do science, every tool we use and perspective we bring, every way we try to attack a problem, these are all both necessary to do the science and limit the interpretation we can claim from our results. She does all this and more by exploring many topics in neuroscience and philosophy throughout the book, many of which we discuss today.

• Mazviita's University of Edinburgh page.
• The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience.
• Previous Brain Inspired episodes:
  • BI 072 Mazviita Chirimuuta: Understanding, Prediction, and Reality
  • BI 114 Mark Sprevak and Mazviita Chirimuuta: Computation and the Mind

0:00 - Intro 5:28 - Neuroscience to philosophy 13:39 - Big themes of the book 27:44 - Simplifying by mathematics 32:19 - Simplifying by reduction 42:55 - Simplification by analogy 46:33 - Technology precedes science 55:04 - Theory, technology, and understanding 58:04 - Cross-disciplinary progress 58:45 - Complex vs. simple(r) systems 1:08:07 - Is science bound to study stability? 1:13:20 - 4E for philosophy but not neuroscience? 1:28:50 - ANNs as models 1:38:38 - Study of mind