All Episodes
BI 210 Dean Buonomano: Consciousness, Time, and Organotypic Dynamics
Dean Buonomano on time in neuroscience vs. physics, integrated information theory, testing timing dynamics in organotypic brain slices, and how AI doesn’t need neuroscience to continue to progress.
BI 209 Aran Nayebi: The NeuroAI Turing Test
Aran Nayebi on NeuroAgents, reverse-engineering brains to build autonomous agents, and an update to the Turing test for NeuroAI.
BI 208 Gabriele Scheler: From Verbal Thought to Neuron Computation
Gabriele Scheler co-founded the Carl Correns Foundation for Mathematical Biology. In fact, Carl Correns was her great grandfather, one of the early pioneers in genetics. Gabriele is a computational neuroscientist, whose goal is to build models of cellular computation, and much of her focus is on neurons.
BI 207 Alison Preston: Schemas in our Brains and Minds
Ali Preston on how the neuroscience of schemas, which help us form memories, integrate and differentiate information, and make predictions.
Quick Announcement: Complexity Group
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BI 206 Ciara Greene: Memories Are Useful, Not Accurate
Ciara Greene discusses how human episodic memory works, and why our common assumptions about the role of memory
BI 205 Dmitri Chklovskii: Neurons Are Smarter Than You Think
Dmitri Chklovskii believes all your neurons are smart controller agents, and understanding this will transform neuroscience and how we build artificial intelligence.
BI 204 David Robbe: Your Brain Doesn’t Measure Time
My guest today, neuroscientist David Robbe, believes we don’t rely on clocks in our brains, or measure time internally, or really that we measure time at all. Rather, our estimation of time emerges through our interactions with the world around us and/or the world within us as we behave.
BI 203 David Krakauer: How To Think Like a Complexity Scientist
David Krakauer is the president of the Santa Fe Institute, where their mission is officially “Searching for Order in the Complexity of Evolving Worlds.” When I think of the Santa Fe institute, I think of complexity science, because that is the common thread across the many subjects people study at SFI, like societies, economies, brains, machines, and evolution. David has been on before, and I invited him back to discuss some of the topics in his new book The Complex World: An Introduction to the Fundamentals of Complexity Science.
BI 202 Eli Sennesh: Divide-and-Conquer to Predict
Eli Sennesh on bridging predictive coding and NeuroAI
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
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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.