Davy Deng

Graduate student @mit_hst @harvardmed; Working towards whole brain emulation. Humanist and Transhumanist. 🧠🧬🧫

New neuroscience substack: @davyneuro

[Davy Deng @davyneuro]

I FINALLY started a substack (also @davyneuro)!!! 🤓 🎉🧠

Please consider subscribing and following if you'd like to be informed on topics such as neuroscience 🧪 , neuro-engineering 🦾 , whole brain emulation ⁉️ , neuroAI 🤖 , consciousness ⚗️ etc..

[Davy Deng @davyneuro]

@neuroecology @KordingLab I work on this problem in c elegans and I would say that we are definitely still bottlenecked by better imaging technology and molecular tools rather than compute. The existing literature is vast but still underwhelming for completely reverse engineering it.

[Davy Deng @davyneuro]

so stoked!

[Daniel Yamins @dyamins]

Stanford Digital Brain Project has launched! We're hoping to make a seriously useful model of the human brain. Please come join us! https://t.co/Gnzm6wJS8d

[Davy Deng @davyneuro]

@kaime @clairebookworm Yes a hierarchical model or a foundation model should do that (and should be made a requirement since WBE is not just about general intelligence but also about particular intelligence)

[Davy Deng @davyneuro]

@sentimentGod @clairebookworm because we do not have sufficiently detailed structure-to-function neural compiler. I discussed this extensively in my substack if you're interested ;)

[Davy Deng @davyneuro]

@Milton_Sneedman @clairebookworm hence the importance of structure-to-function model (and that it needs to be mechanistic) because obtaining dynamical structure data is gonna be brutal

[Davy Deng @davyneuro]

What started as an informal discussion with @clairebookworm turned into a formalized answer to what I consider as the most prerequisite to answer for the field of emulation, i.e. how do you know if/when you have emulated a brain?

[claire night skies🪼 @clairebookworm]

what would establish that a simulated brain is a ‘real’ emulation? the recent viral ‘fly playing beat saber’ videos has made this question surprisingly prescient. @davyneuro & I argue that whole-brain emulation needs a shared benchmark & propose what it should test! >>

[Davy Deng @davyneuro]

cool!

[Antonio Regalado @antonioregalado]

My new listicle is mental 🤖

"Six Wild Ideas for Perpetuating Your Mind" 🧠

Head Transplant w/Sergio Canavero
Mindclones w/@skybiome
Brain-in-Jar w/Bexorg
Brain Upload w/@NectomeResearch
Gradual Replacement w/@Replacing_Aging
Synthetic Neortex w/@danburonline

👇for link

[Adam Cohen @AdamEzraCohen]

We posted a preprint on all-to-all functional mapping of a cortical microcircuit in vivo. https://t.co/enxvQaV5wW
Yitong Qi developed molecular tools, optics, and protocols for massively parallel optogenetic stimulation and voltage imaging during animal behavior.

[Davy Deng @davyneuro]

wow i have no idea @foresightinst has such a long history, happy 40th anniversary!

[Allison Duettmann @allisondman]

Congratulations to @lifeext @KEricDrexler for having co-founded an organization that was unusually early, often right, and always prescient when it comes to envisioning and building flourishing futures defined by the interplay of strong technology - from molecular nanotechnology, longevity escape velocity, sovereign AI, to neurotechnology. You were the reason I packed up my life a decade ago to move to the Bay and I've been grateful for that every day since.

[Davy Deng @davyneuro]

Of course it should not be forgotten that my PhD advisor @eboyden3 had a tremendous amount of direct contributions to this technology. I think ExM also has the potential for a Nobel, onwards for a whole human brain map! #NobelPrize #Optogenetics #ExpansionMicroscopy #Neuroscience

[The Nobel Prize @NobelPrize]

BREAKING NEWS
The 2026 #NobelPrize in Physiology or Medicine has been awarded to Karl Deisseroth, Peter Hegemann and Georg Nagel “for their discoveries concerning light-gated ion channels and optogenetics.”

[Yann LeCun @ylecun]

I feel like the progress of AI is actually making systems neuroscience more exciting.

It's not like current AI has a particularly good angle on physical intuition, common sense, world models, planning/reasoning, motivations/drives, emotions, etc.

We may now have the tools to implement and test cognitive architectures.

[Davy Deng @davyneuro]

Amazing! After a long wait, I thought i would never see optogenetic win a Nobel!

[The Nobel Prize @NobelPrize]

BREAKING NEWS
The 2026 #NobelPrize in Physiology or Medicine has been awarded to Karl Deisseroth, Peter Hegemann and Georg Nagel “for their discoveries concerning light-gated ion channels and optogenetics.”

[Davy Deng @davyneuro]

i wonder if this works for hodgkin-huxley?

[Earl K. Miller @MillerLabMIT]

It's a struggle to figure out the math behind chaotic systems, especially when the data is limited and noisy. PEM-UDE can recover interpretable equations from messy, chaotic data, and it yields testable predictions about neural networks.

https://t.co/e7L2C6IyqH
#neuroscience

[nature @Nature]

Lab-grown brain organoids: time for international oversight

https://t.co/eLDxOkeuob

[Davy Deng @davyneuro]

The issue is: if you define consciousness as subjective experience, it’s epistemically unverifiable from the outside. Define it functionally, then you hit a loop: we don’t know which causal dynamics produce consciousness because we can’t know which is conscious. So.. why?

[Anil Seth @anilkseth]

I'm happy to have had the chance to contribute to this mammoth paper. Many congrats @shamilch, Arvo Munoz, and all other co-authors. I'm still *extremely* sceptical that (silicon-based digital) AI can be conscious, but I think the perspective of 'structured agnosticism' that can accommodate different views is an important contribution to the debate. 👇🏽

[Davy Deng @davyneuro]

The article proposes evolution tunes the stiff parameters. This resonates with precisely what neural compiler from my substack addresses - identify mechanisms using shared biological rules across evolution instead of naive AI model which is bad at handling stiffness anyway (2/2)

[Davy Deng @davyneuro]

This is brilliant insight! In biology many parameters are sloppy (Gutenkunst et. al., 2007), which is why throwing an AI on a bunch of data don't give you understanding, but there may only be few stiff parameters that matter, the question is how to obtain those. (1/2)

[Asimov Press @AsimovPress]

Theory, in biology, has never gotten the same "respect" or "institutional standing" as theoretical physics.

This is a shame. Theory building in biology has been at least as fertile and consequential in the process of discovery as in physics. And yet, it has generally been hard to get funding for purely theoretical work in biology.

Biology also has difficulty compressing its empirical data into theories. In physics, there’s only one kind of electron, and what applies to one, applies to all. But biological cells and organisms are products of individual developmental and evolutionary histories. Biological variation can itself be irreducible and causally meaningful. This means that, often, biological models have many many parameters!

In our latest essay, @ulkar_aghayeva considers whether biology can be made as compressible and comprehensible as physics, and thus restore its institutional standing. She explains how a broad range of phenomena—including biochemical reaction networks, insect flight, and the eukaryotic cell cycle—are well described by so-called sloppy models. In such models, only a few parameter combinations are important to the observable behavior.

This discussion is particularly important in light of AI, and in answering this question of whether or not AI systems will ever be able to make great "theoretical leaps" for biology.

Read the piece and subscribe: https://t.co/G4oyE7R4HT

[Davy Deng @davyneuro]

I am not quite sure how preprint helps here, but I do agree with the general statement. Time will tell but only if scientific community's collective attention and memory lasts as long as that time.

[Sophien Kamoun @KamounLab]

I just published: How do we recognize world-class science? Not by the journal it appeared in

What I told early-career researchers about prediction, rigour, failure, priors — and the tyranny of bar graphs.

Science predicts. Pseudoscience doesn’t.
Twenty-nine years
How to science, and how not to
What’s a failed experiment?
Death by statistics
Ditch the bar graphs
Get credit for all of it

https://t.co/1NzCnyC2mn

[Davy Deng @davyneuro]

So intriguing. The cortical layers are still disorganized and E/I balance is wrong but it is quite integrated with the rest of the mouse brain, with thalamic input, that's creepy/scary/exciting (the three words i love to use when it comes to neural organoid).

[Prof. Brian Keating @Briankeating]

Stanford scientists put millions of living human neurons into mice whose own cortex had been removed. The odd part? The mice still behave like mice. So where, exactly, does “human” cognition begin? We are getting disturbingly good at building experiments before we agree on what their results would mean.

Read the article here: https://t.co/r9GtRFuzU6