Brain Inspired

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BI 236 Liset de la Prida: Neurons, Ripples, and Manifolds Apr 22, 2026

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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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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.

0:00 - Intro 5:29 - Hippocampus 9:31 - Sharp wave ripples 27:30 - Oscillations and epiphenomena 33:37 - Sharp wave ripples to manifolds 43:54 - Manifolds and single neuron types 49:45 - Hippocampus and granularity of cell types 59:23 - Explanation across levels 1:19:38 - Manifolds and higher cognition 1:29:46 - Brain Space and Time

BI 235 Romain Brette: The Brain, in Theory Apr 08, 2026

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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 don’t 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.

0:00 - Intro 4:01 - The Brain, In Theory 7:10 - Influences 13:11 - Process metaphysics 18:39 - Observer vs system perspective 21:24 - Information in the brain? 22:56 - Why this book? 29:52 - Computations in the brain 52:14 - Behavior is not a computation 1:07:20 - Paramecium cognition 1:22:02 - How should neuroscientists proceed? 1:29:09 - Cognition as collective behavior of autonomous cells 1:36:47 - Constraints, causes, and laws 1:52:36 - Hopes for the book to influence the field 1:55:04 - Thoughts about AI 2:02:13 - Computation and goals 2:08:17 - Anticipation vs prediction

BI 234 Juan Gallego: The Neural Manifold Manifesto Mar 25, 2026

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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.

Read the transcript.

0:00 - Intro 4:37 - Manifolds 14:30 - Strengths and weaknesses 24:32 - Conserved manifolds across animals and species 34:31 - Causality and manifolds 47:29 - Constraints and causes 51:05 - What to measure 58:55 - Complexity and manifolds 1:10:29 - Juan's background 1:14:08 - Prosthetics for spinal cord injuries 1:41:06 - Integrating across behaviors and timescales 1:46:56 - Conjoint specification of action by neocortex and striatum.

BI 233 Tom Griffiths: The Laws of Thought Mar 11, 2026

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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.

Read the transcript.

0:00 - Intro 3:20 - Tom's approach 7:19 - 3 pillars of the laws of thought 28:24 - Logic and formal systems strip away meaning 39:04 - Nature of thought 50:35 - Kahneman and Tversky 1:015:12 - Enabling constraints and inductive bias 1:12:51 - Hidden layers, probability, and hidden markov models 1:20:47 - Conscious vs nonconscious 1:23:43 - Feelings 1:31:26 - Personal

BI 232 How Should Neuroscience Integrate with Ecological Psychology? Feb 25, 2026

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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 lab.
- @dewitmm.bsky.social
Luis Favela.
- The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment
Vicente Raja
Previous episodes:
- BI 223 Vicente Raja: Ecological Psychology Motifs in Neuroscience
- BI 190 Luis Favela: The Ecological Brain
- BI 213 Representations in Minds and Brains

Read the transcript.

0:00 - Intro 8:23 - How Louie, Vicente, and Matthieu know each other 11:16 - Past present and future of relation between neuroscience and ecological psychology 17:02 - Why resistance to integrating neuroscience into ecological psychology? 28:26 - What counts as ecological psychology? 33:32 - Affordances properly understood 40:33 - Ecological information 47:58 - Importance of dynamics 48:59 - What's at stake? 58:27 - Environment intervention 1:16:21 - When ecological neuroscience publishes 1:31:25 - Neuroscientists escape hatch 1:38:04 - Is ecological psychology a theory of everything?

BI 231 Jaan Aru: Conscious AI? Not Even Close! Feb 11, 2026

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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.

0:00 - Intro 4:21 - Jaan's approach 8:51 - Likelihood of machine consciousness 18:58 - Across-levels understanding 30:23 - Intelligence vs consciousness 36:27 - Connecting low-level implementation to cognition 45:42 - Organization and constraints 52:28 - Thalamocortical loops 1:04:18 - Artificial consciousness 1:14:34 - Theories of consciousness 1:23:16 - Creativity and insight 1:37:26 - Science research in Estonia

BI 230 Michael Shadlen: How Thoughts Become Conscious Jan 28, 2026

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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.
Conscious and nonconscious thought: Insights from the neuroscience of decision-making.
Decision Making and Consciousness (Chapter in upcoming Principles of Neuroscience textbook).
Talk: Decision Making as a Model of thought

Read the transcript.

0:00 - Intro 7:05 - Overview of Mike's account 9:10 - Thought as interrogation 21:03 - Neurons and thoughts 27:05 - Why so many neurons? 36:21 - Evolution of Mike's thinking 39:48 - Marleau-Ponty, cognition, and meaning 44:54 - Naturalistic tasks 51:11 - Consciousness 58:01 - Martin Buber and relational consciousness 1:00:18 - Social and conscious phenomena correlated 1:04:17 - Function vs. nature of consciousness 1:06:05 - Did language evolve because of consciousness? 1:11:11 - Weak phenomenology and long-range feedback 1:22:02 - How does interrogation work in the brain? 1:26:18 - AI consciousness 1:35:49 - The hard problem of consciousness 1:39:34 - Meditation and flow

BI 229 Tomaso Poggio: Principles of Intelligence and Learning Jan 14, 2026

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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 papers
- Position: A Theory of Deep Learning Must Include Compositional Sparsity
- The Levels of Understanding framework, revised
Blog post:
- Poggio lab blog.
- The Missing Foundations of Intelligence

Read the transcript.

0:00 - Intro 9:04 - Learning as the fourth level of Marr's levels 12:34 - Engineering then theory (Volta to Maxwell) 19:23 - Does AI need theory? 26:29 - Learning as the door to intelligence 38:30 - Learning in the brain vs backpropagation 40:45 - Compositional sparsity 49:57 - Math vs computer science 56:50 - Generalizability 1:04:41 - Sparse compositionality in brains? 1:07:33 - Theory vs experiment 1:09:46 - Who needs deep learning theory? 1:19:51 - Does theory really help? Patreon 1:28:54 - Outlook

BI 228 Alex Maier: Laws of Consciousness Dec 31, 2025

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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 Lab
Astonishing Hypothesis (Alex's youtube channel)
Twitter:
Sensation and Perception textbook (in-the-making)
Related papers

Various things Alex mentioned:
- “An Antiphilosophy of Mathematics,” 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 - Intro 4:27 - Discovering consciousness science 11:23 - Laws of perception 15:48 - Integrated information theory and mathematical formalism 23:54 - Theories of consciousness without math 28:18 - Computation metaphor 34:44 - Formalized mathematics is the way 36:56 - Category theory 41:42 - Structuralism 51:09 - The mathematical 54:33 - Metaphysics of the mathematical 59:52 - Yoneda Lemma 1:12:05 - What's real 1:26:22 - Measuring consciousness of a soccer team 1:35:03 - Assumptions and approximations of IIT 1:43:13 - Open science

BI 227 Decoding Memories: Aspirational Neuroscience 2025 Dec 17, 2025

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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.