All Episodes
BI 201 Rajesh Rao: From Predictive Coding to Brain Co-Processors
Rajesh Rao shares his updated theory on how the cortex could implement active predictive coding. Predictive coding shares theoretical roots with predictive processing, the bayesian brain, active inference, and the free energy principle, all of which are general theories of brain function.
BI 200 Grace Hwang and Joe Monaco: The Future of NeuroAI
Joe Monaco and Grace Hwang co-organized a recent workshop I participated in, the 2024 BRAIN NeuroAI Workshop. You may have heard of the BRAIN Initiative, but in case not, BRAIN is is huge funding effort across many agencies, one of which is the National Institutes of Health, where this recent workshop was held. The BRAIN Initiative began in 2013 under the Obama administration, with the goal to support developing technologies to help understand the human brain, so we can cure brain based diseases.
BI 199 Hessam Akhlaghpour: Natural Universal Computation
Hessam Akhlaghpour is a postdoctoral researcher at Rockefeller University in the Maimon lab. His experimental work is in fly neuroscience mostly studying spatial memories in fruit flies. However, we are going to be talking about a different (although somewhat related) side of his postdoctoral research. This aspect of his work involves theoretical explorations of molecular computation, which are deeply inspired by Randy Gallistel and Adam King’s book Memory and the Computational Brain.
BI 198 Tony Zador: Neuroscience Principles to Improve AI
We’re in a huge AI hype cycle right now, for good reason, and there’s a lot of talk in the neuroscience world about whether neuroscience has anything of value to provide AI engineers – and how much value, if any, neuroscience has provided in the past.
BI 197 Karen Adolph: How Babies Learn to Move and Think
Karen Adolph runs the Infant Action Lab at NYU, where she studies how our motor behaviors develop from infancy onward. We discuss how observing babies at different stages of development illuminates how movement and cognition develop in humans, how variability and embodiment are key to that development, and the importance of studying behavior in real-world settings as opposed to restricted laboratory settings.
BI 196 Cristina Savin and Tim Vogels with Gaute Einevoll and Mikkel Lepperød
/
BI 195 Ken Harris and Andreas Tolias with Gaute Einevoll and Mikkel Lepperød
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.
BI 194 Vijay Namboodiri & Ali Mohebi: Dopamine Keeps Getting More Interesting
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.
BI 193 Kim Stachenfeld: Enhancing Neuroscience and AI
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.
BI 192 Àlex Gómez-Marín: The Edges of Consciousness
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.
BI 191 Damian Kelty-Stephen: Fractal Turbulent Cascading Intelligence
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.
BI 190 Luis Favela: The Ecological Brain
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.
BI 189 Joshua Vogelstein: Connectomes and Prospective Learning
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.
BI 188 Jolande Fooken: Coordinating Action and Perception
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.
BI 187: COSYNE 2024 Neuro-AI Panel
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.
BI 186 Mazviita Chirimuuta: The Brain Abstracted
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.
BI 185 Eric Yttri: Orchestrating Behavior
Eric’s lab studies the relationship between various kinds of behaviors and the neural activity in a few areas known to be involved in enacting and shaping those behaviors, namely the motor cortex and basal ganglia. And study that, he uses tools like optogentics, neuronal recordings, and stimulations, while mice perform certain tasks, or, in my case, while they freely behave wandering around an enclosed space.
BI 184 Peter Stratton: Synthesize Neural Principles
What Pete argues for is what he calls a sideways-in approach. So a bottom-up approach is to build things like we find them in the brain, put them together, and voila, we’ll get cognition. A top-down approach, the current approach in AI, is to train a system to perform a task, give it some algorithms to run, and fiddle with the architecture and lower level details until you pass your favorite benchmark test.