From First Principles

Hosted by Lester Nare and Krishna Choudhary, this episode is a deep dive into one of the hardest questions in neuroscience: what breaks in the brain during a coma, and can we figure out how to turn consciousness back on? We unpack a new paper from Daniel Toker et al. that uses an interpretable AI framework — not a generic black box chatbot model — to reverse engineer the biological mechanisms of prolonged unconsciousness, recover known features of coma, predict new ones, and propose a possible new target for deep brain stimulation.

Summary

Why diagnosis is so hard — disorders of consciousness are not just about whether a patient is awake, but whether awareness is still present even when motor output is gone.

The mesocircuit hypothesis — the episode explains how the cortex, thalamus, and basal ganglia may work together like an electrical grid to support consciousness.

Interpretable AI, not black-box hype — Daniel Toker’s team built a biophysically grounded model that rediscovered known coma features and predicted two new biological mechanisms.

A possible stimulation target — the subthalamic nucleus emerged as a standout candidate for deep brain stimulation, suggesting a new path toward restoring wakefulness.

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Show Notes
Daniel Toker et al. — Adversarial AI reveals mechanisms and treatments for disorders of consciousness

Nicholas Schiff et al. — deep brain stimulation in a minimally conscious patient

Adrian Owen et al. — fMRI evidence of covert awareness in a patient diagnosed as vegetative

Hosted by Lester Nare and Krishna Choudhary, this episode is a fast-moving science rundown covering four remarkable stories from across AI, genetics, neuroscience, and paleontology. We dig into the story of a machine learning engineer who used AI tools to help design a personalized cancer vaccine for his dog, explore how an all-female fish species has survived far longer than evolutionary theory would predict, unpack new brain-scan evidence for how ketamine may rapidly relieve severe depression, and look at new research suggesting life rebounded shockingly fast after the asteroid that killed the dinosaurs.

Summary

AI and personalized medicine — a striking case study in how AI tools may help accelerate highly customized treatments, starting with a rescue dog named Rosie.

Evolution gets weird — the Amazon molly fish appears to challenge the usual assumptions about why asexual reproduction should fail over long time scales.

Why ketamine works so fast — new PET imaging research points to brain-region-specific changes in AMPA receptors in treatment-resistant depression.

Life after catastrophe — microscopic plankton may have evolved into new species within just a few thousand years after the Chicxulub impact.

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Show Notes
AI-designed dog cancer vaccine story
https://finance.yahoo.com/news/mans-dog-riddled-tumors-dying-210500037.html?guccounter=1

Amazon molly / gene conversion paper
https://www.nature.com/articles/s41586-026-10180-9

Ketamine / AMPA receptor PET imaging paper
https://www.nature.com/articles/s41380-026-03510-w

Post-asteroid plankton recovery paper
https://www.yokohama-cu.ac.jp/english/news/20260306takahashi.html

Hosted by Lester Nare and Krishna Choudhary, this episode is a deep dive into one of the strangest science stories of the year: a dish of human neurons allegedly learning to play Doom. We go back to the original 2022 DishBrain paper out of Cortical Labs, unpack how biological neurons can be read and written with multi-electrode arrays, and then compare the peer-reviewed Pong result to the much newer Doom claim. The result is a story that is both genuinely impressive and, in places, probably overhyped.

Summary

Wetware engineering — replacing artificial neurons with real biological neurons plus electronics, and why some people think this could become a new computing paradigm.

How DishBrain worked — human stem-cell-derived cortical neurons grown on a multi-electrode array, trained through sensory encoding and a “minimize surprise” feedback loop.

Where the Doom story gets messy — the newer system appears to include a reinforcement-learning layer in the loop, raising the key question: are the neurons actually doing the learning?

The big idea underneath the hype — even if Doom is overstated, the broader platform is still a remarkable step toward programmable biocomputing.

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Hosted by Lester Nare and Krishna Choudhary, this is our first standalone rundown episode — a faster, looser format where we hit several stories we didn’t have room to turn into full deep dives. This week: bacteria revived from a Romanian ice cave after 5,000 years, a speculative but fascinating theory linking solar storms to earthquakes, new evidence that dogs and humans share genetic roots for personality traits, and the increasingly dramatic fight over the future of AI after Yann LeCun leaves Meta to build a new billion-dollar company focused on world models.

Summary
Ancient bacteria, modern resistance — a microbe revived from a 5,000-year-old Romanian ice cave resists modern antibiotics and may even contain compounds useful against present-day superbugs.
Solar storms and earthquakes? — a Kyoto University theoretical paper suggests space weather could perturb electric fields in Earth’s crust enough to influence faults already near critical stress.
Dogs and humans, genetically — a Cambridge / Morris Animal Foundation study finds shared gene pathways that map to personality-like traits in both golden retrievers and humans.
The Meta AI split — Yann LeCun leaves Meta to pursue AI systems that model the physical world, arguing that simple scaling of LLMs may never reach real general intelligence.

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Show Notes
Story 1 — Ancient bacteria in Romanian ice cave (Frontiers in Microbiology)
Story 2 — Solar storms and earthquakes (Kyoto University / International Journal of Plasma Environmental Science and Technology)
Story 4 — Dog and human personality genes (PNAS)
Story 5 — Yann LeCun leaves Meta / world-model AI (Wired)

Hosted by Lester Nare and Krishna Choudhary, this episode is a deep dive into a new synthetic-biology breakthrough out of EPFL: OptoEvolution. The big idea is simple but powerful — traditional directed evolution is great at making proteins that are always “on,” but biology is full of proteins that need to switch states, respond to stimuli, and behave more like logic gates than static tools. This paper takes directed evolution and couples it to light and the cell cycle, creating a new way to evolve dynamic proteins that can toggle, compute, and respond with far more control.
Summary
Why directed evolution needed an upgrade — classic methods select for proteins with continuous function, not proteins that toggle between active and inactive states.
OptoEvolution — using light as a control signal and the cell cycle as a built-in oscillator to evolve proteins that must turn on and off to survive.
Color-multiplexed biology — engineering proteins to respond to different wavelengths of light, opening the door to finer control of gene expression.
Single-protein logic gates — proof-of-concept AND-gate behavior inside a single protein, hinting at a future where biology can be programmed with much more software-like precision.
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Show Notes
OptoEvolution / dynamic protein control (Cell)