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Happy Birthday, Claude Shannon. One of Shannon's conceptual feats was to connect information content to uncertainty via entropy. Uncertainty reduction has become one of the most powerful concepts in biology showing up as a foundational idea in cognitive science + evolutionary

Our latest study - psilocybin evokes structural neural plasticity, and we wanted to know how this maps onto pyramidal cell type-specific circuits to produce behavioral effects. 🍄🔬🧠 Link to bioRxiv: biorxiv.org/content/10.110… Led by Ling-Xiao Shao and Clara Liao.

Marc Andreessen and Ben Horowitz say that AI models are hitting a ceiling of capabilities: "we've really slowed down in terms of the amount of improvement... we're increasing GPUs, but we're not getting the intelligence improvements, at all"

“Deep learning is hitting a wall”, translated into VC, by Marc Andreessen 🇺🇸 himself. Sweet vindication, esp. after everyone from Sam Altman to Yann LeCun to Elon Musk took shots at me for daring to say this in 2022. Sometimes you just have to say the truth and wait for the world to catch up.

François Chollet Neural networks are flat structures & cannot represent depth and complexity required to model intelligence. Similarly, tensors also are linear & rigid in nature to represent any sort of complex relations. We have to go away from linear, rigid, scratch the surface kind of

Nature research paper: Cellular ATP demand creates metabolically distinct subpopulations of mitochondria nature.com/articles/s4158…

The $3B Human Genome Project was started under a Republican administration and later acknowledged by the Obama admin to have returned $141 to the economy for every $1 spent. Science has a higher ROI than any other category of government spending.

Understanding the difference between Standard Deviation (SD) and Standard Error (SE) is crucial for accurate data interpretation. SD measures the variability within your data, indicating how spread out the individual data points are from the mean. In contrast, SE measures the

Understanding the molecular diversity of synapses — a Review by Marc van Oostrum & Erin M. Schuman go.nature.com/3ZKmRVx Marc van Oostrum Erin schuman

Feeling like this prediction has already come true two hours later 🤔

Human cortical pyramidal neurons are larger, exhibit more complex branching patterns, and feature distinctly nonlinear biophysical properties compared to rat cortical pyramidal neurons. Do these unique characteristics make human neurons more functionally complex? Would that

A breathtaking convergence of science and imagination: the simulated black hole’s meets the awe-inspiring reality of the first-ever captured black hole image.

Developmental axon diameter growth of central nervous system axons does not depend on ensheathment or myelination by oligodendrocytes biorxiv.org/cgi/content/sh… #biorxiv_neursci

Happy birthday to Python creator Guido van Rossum. The open source language was named after comedy troupe Monty Python: bit.ly/42oOFjN AI image v/Grok

70% of an E. coli cell is water. The other 30% of mass is *mostly* proteins. But what's the most abundant protein in the cell? To my surprise, it's Lpp, a protein that anchors the outer membrane to the peptidoglycan layer. There are ~700,000 of them in each cell. New blog🔻

The flow and the transformation of energy through your mitochondria

I think this was part of the implicit premise of the first incarnation of Hammer Lab at Mount Sinai, about a dozen of us with math/CS backgrounds ditched tech for biomedicine. And we got humbled hard: most of what we did flopped & techies don't understand experimental design.

If you are reading a seemingly insightful piece about AI, you should ask yourself: how much should I trust the author? Does he/she fall for the "underwear gnome fallacy"? Or make one of 3 more argumentative mistakes? New blog post: pacificklaus.com/is-your-ai-exp…

Devastating loss for neuroscience and ultimately for AI safety. Hearing that Lichtman's NIH grant to scale up EM connectomics by 10x beyond MICrONS was just canceled. Essential work for scaling EM to whole brains and for training a large workforce to support connectomics.

AI is powerful but it cannot invent data. AI for science is falling into the simulation trap of rendering a dream within a dream. The truth is that the ground truth costs more but actually works. Don’t fall for fake data.