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Michael Abbott

@m-abski.bsky.social
16 followers 46 following 16 posts

Physics & life, at Yale Qbio

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Michael Abbott @m-abski.bsky.social · 12/10/2026
So we made a learning network out of transistors instead! For instance, here's a piece which does softmax. The only problem is that pulling the output no longer perturbs the whole thing... which we solve by using a clever "dyadic" construction which won't fit in a tweet!
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Michael Abbott @m-abski.bsky.social · 12/10/2026
These resistor networks do fine at classification. But they struggle to learn the things transformers are good at -- "in-context learning" tasks, where the network has to learn to copy information from elsewhere in the input
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Michael Abbott @m-abski.bsky.social · 12/10/2026
"Physical learning" is a school of ML in which some analog device does both forward and reverse, inference and training. In a "neural network" made of resistors, pulling the output to where it should be changes all the voltages, and this lets you train them...
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Michael Abbott @m-abski.bsky.social · 12/10/2026
Out today on arxiv is a Rube Goldberg machine I made in my basement. It's an analog transformer made of a few thousand transistors, and it can learn to learn things: arxiv.org/abs/2610.12571
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Michael Abbott @m-abski.bsky.social · 21/07/2026
See also Matt's longer 🧵 about this: bsky.app/profile/mlei...
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Michael Abbott @m-abski.bsky.social · 21/07/2026
Out today on @arxiv.bsky.social, "On the Information Required for Feedback Control", the article has other nice examples (and counter-examples!): arxiv.org/abs/2607.16639
arxiv.org
On the Information Required for Feedback Control
Biological systems across scales, along with many engineering problems, must control noisy systems with limited information. Here we study information-limited feedback control of stochastic systems to...
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Michael Abbott @m-abski.bsky.social · 21/07/2026
This bound can often be saturated, which suggests a way to derive the optimal control protocol. Applied to our paper from February about how bacteria (and guys in boats) ought to navigate, it provides a proof that the steering strategies we had there are truly optimal.
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Michael Abbott @m-abski.bsky.social · 21/07/2026
Our bound looks at the entropy of the controlled state, and asks how quickly it would start going pear-shaped sans control. The rate of change dSₚₐₛₛᵢᵥₑ/dt at that moment is precisely the min transfer entropy rate 𝒯̇̇(x → dxᶜᵒⁿᵗʳᵒˡ) needed for steady-state control.
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Michael Abbott @m-abski.bsky.social · 21/07/2026
The classic result here is the "data rate theorem", which gives a loose lower bound on the information required for stability -- each unstable direction contributes its eigenvalue. The DRT is about stopping exponential growth, but says nothing about tighter constraints, like ±5°.
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Michael Abbott @m-abski.bsky.social · 21/07/2026
To balance this giant pencil within 5° of vertical, how much information has to flow from @mleighton.bsky.social 's eyes to his hand? His new article out today (2607.16639, with friends in Yale QBio -- @jbetancourt015.bsky.social, @emonetlab.bsky.social and Ben Machta), has an answer...
Matt is performing a feedback control task, using less than 10 bits per second. The number arXiv:2607.16639 is written on the wall behind him, because Twitter doesn't like links
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Reposted by Michael Abbott
Michael Abbott @m-abski.bsky.social · 02/04/2026
For these animals, published rates of entropy production are at most a few bits per second. That's very small compared to W, which we can estimate from their diet. And the same is true for our 𝐴. 𝑠𝑢𝑏𝑎𝑟𝑢... the bound kʙ T Σ ≤ W is quite far from being tight:
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Michael Abbott @m-abski.bsky.social · 03/04/2026
Preprint link: arxiv.org/abs/2604.00453 With thanks to my partners in crime Yu, Emmy & Ben. And to @mleighton.bsky.social who answered many questions, and @emonetlab.bsky.social who helped with some of the data collection.
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Reposted by Michael Abbott
arXiv physics.bio-ph Biological Physics @physicsbioph-bot.bsky.social · 02/04/2026
Yu Fu, Emmy Dobson, Benjamin B. Machta, Michael C. Abbott: In-vivo entropy production of A. subaru arxiv.org/abs/2604.00453 arxiv.org/pdf/2604.00453 arxiv.org/html/2604.00453
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Michael Abbott @m-abski.bsky.social · 02/04/2026
Disappointingly, 𝐴. 𝑠𝑢𝑏𝑎𝑟𝑢 doesn't win the prize for the weakest thermodynamic bound so far. That goes to a super-organism, the public transit system of Turin... shown to produce (at least) Σ = 2.6 × 10⁻⁴ bits/s
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Michael Abbott @m-abski.bsky.social · 02/04/2026
For these animals, published rates of entropy production are at most a few bits per second. That's very small compared to W, which we can estimate from their diet. And the same is true for our 𝐴. 𝑠𝑢𝑏𝑎𝑟𝑢... the bound kʙ T Σ ≤ W is quite far from being tight:
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Michael Abbott @m-abski.bsky.social · 02/04/2026
The entropy production rate, or irreversibility Σ, is a statistical concept intimately connected to energy: kʙ T Σ ≤ W, where W is energy per time. Nonzero Σ is impossible without nonzero W. And this Σ has been estimated in all kinds of macroscopic systems: birds, humans, cows...
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Michael Abbott @m-abski.bsky.social · 02/04/2026
Life needs energy from food, or the sun, to survive. It's a non-equilibrium process, and this fact leaves statistical clues in things we can observe. Looking for these clues in biological data is a small industry, which started by looking for closed cycles in microscopic systems:
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Michael Abbott @m-abski.bsky.social · 02/04/2026
Maybe I should tweet here too. Out yesterday is my first ever experimental paper, on a novel model organism... arxiv.org/abs/2604.00453
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