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mleighton.bsky.social

@mleighton.bsky.social
29 followers 34 following 45 posts
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mleighton.bsky.social @mleighton.bsky.social · 21/07/2026
Thanks Trevor!
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mleighton.bsky.social @mleighton.bsky.social · 21/07/2026
Finally, thanks to Yale's Department of Physics and Quantitative Biology Institute, and the Natural Sciences and Engineering Research Council of Canada (NSERC) for supporting our work!
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mleighton.bsky.social @mleighton.bsky.social · 21/07/2026
It’s worth noting that the optimal control protocols we derive are probabilistic, operating analogously to the diffusion models popular in generative AI applications. Implementing these protocols may thus be a promising use case for new tech like thermodynamic computers!
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mleighton.bsky.social @mleighton.bsky.social · 21/07/2026
In summary we have a general bound for information-limited feedback control, and performance-information Pareto frontiers for wide-ranging control problems. We've also found an information-optimal control protocol: probabilistically time-reversing the system’s passive dynamics.
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mleighton.bsky.social @mleighton.bsky.social · 21/07/2026
Example 2: information engines, nanoscale devices using feedback control to extract energy from heat baths without doing work. We bound the information cost of extracting energy from a heat bath, with a diverging information cost to maximize performance. (tighter than 2nd law!)
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mleighton.bsky.social @mleighton.bsky.social · 21/07/2026
We apply our results to microbial navigation up a gradient, deriving a velocity-information Pareto frontier (saturated by time-reversal). We prove that memoryless Run-Reverse, a strategy used by microbes like P. Aeruginosa, is information-optimal given biophysical constraints.
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mleighton.bsky.social @mleighton.bsky.social · 21/07/2026
What conditions are required to achieve the minimum information cost of control quantified by our bound? For a broad class of control problems, the explicit optimal control protocol is to probabilistically time-reverse the passive (uncontrolled) dynamics of the system.
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mleighton.bsky.social @mleighton.bsky.social · 21/07/2026
To understand how control performance is limited by information, we derived a bound quantifying the minimum information rate required for a feedback controller to fix a target steady state. This leads to performance-information Pareto frontiers for any feedback control problem.
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mleighton.bsky.social @mleighton.bsky.social · 21/07/2026
arxiv.org/abs/2607.16639 Living organisms across scales, and many engineering problems, face the task of controlling stochastic systems with a limited information flow from system to controller. These limitations can arise from noise in sensing, control algorithm, or actuation.
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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mleighton.bsky.social @mleighton.bsky.social · 21/07/2026
How well can you control a stochastic system with limited information? Excited to share a new preprint answering this question! “On the Information Required for Feedback Control”, with @jbetancourt015.bsky.social, @emonetlab.bsky.social, Ben Machta, and @m-abski.bsky.social.
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APS DSNP @aps-dsnp.bsky.social · 29/05/2026
📆 Join us next Tuesday, June 9 at 12 PM EST for the next klogW seminar featuring Jonas Veenstra (ENS Lyon) and Matthew Leighton (Yale University). Don’t miss this chance to hear from two outstanding early-career researchers! Please sign up and find more details here: engage.aps.org/dsnp/resourc...
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Miro Astore @miroastore.bsky.social · 24/04/2026
We just preprinted one of my favorite studies @FlatironInst . I was lucky to be part of an amazing team studying the effects of rapid cooling to preserve samples in cryoEM. Read on to learn about the limits of cryoEM for biophysics and how to overcome them www.biorxiv.org/content/10.6...
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mleighton.bsky.social @mleighton.bsky.social · 04/03/2026
P.S. This work was inspired in large part by one of my favorite mathematical biology papers of the 20th century, "Will a Large Complex System be Stable", published by Robert May in 1972. www.nature.com/articles/238... I highly recommend checking it out if you haven't read it!
nature.com
Will a Large Complex System be Stable? - Nature
Nature - Will a Large Complex System be Stable?
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mleighton.bsky.social @mleighton.bsky.social · 04/03/2026
Thanks to Yale's Department of Physics and Quantitative Biology Institute, and the Natural Sciences and Engineering Research Council of Canada (NSERC) for supporting my research!
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mleighton.bsky.social @mleighton.bsky.social · 04/03/2026
These results indicate that Maxwell demons cannot arise by chance in systems with many degrees of freedom, so we should be surprised when we find demons in the wild! This ultimately suggests that Maxwell demons can only arise through some process of selection.
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mleighton.bsky.social @mleighton.bsky.social · 04/03/2026
For a large class of models, both analytic and numerical results show that the probability of finding a Maxwell demon by chance decreases at least exponentially, and in some cases even double-exponentially, with the number N of degrees of freedom -- becoming vanishingly unlikely.
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mleighton.bsky.social @mleighton.bsky.social · 04/03/2026
So how likely is it that a random complex system with N degrees of freedom will operate as a Maxwell demon? To address this I formulate null models for random dynamics with both continuous and discrete degrees of freedom, and calculate the probability p(demon|N).
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mleighton.bsky.social @mleighton.bsky.social · 04/03/2026
A Maxwell demon is a multi-component thermodynamic system where at least one subsystem takes in heat from its environment, thus appearing in isolation to locally violate the 2nd law of thermodynamics. The 2nd law is recovered by accounting for information flow between subsystems.
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mleighton.bsky.social @mleighton.bsky.social · 04/03/2026
I tackle this question in a new preprint: Will a Large Complex System be a Maxwell Demon? arxiv.org/abs/2603.03248
arxiv.org
Will a Large Complex System be a Maxwell Demon?
Emerging evidence suggests that physical systems operating as Maxwell demons, in which some subsystem of a larger system extracts heat energy from its environment in an apparent local violation of the...
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mleighton.bsky.social @mleighton.bsky.social · 04/03/2026
The spectre of Maxwell’s demon looms all around us, with sightings reported across diverse fields from economics, to evolutionary biology, to cellular sensing, to molecular biophysics. But how surprised should we be to find demons haunting the physical world?
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mleighton.bsky.social @mleighton.bsky.social · 10/02/2026
Thanks again to NSERC, the Canada Research Chairs program, and @sfuphysics.bsky.social for supporting our research, and thanks to @mitacscanada.bsky.social for funding Julián’s time as a visiting researcher in the Sivak Group.
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mleighton.bsky.social @mleighton.bsky.social · 10/02/2026
Check it out here: journals.aps.org/prresearch/a..., or see my previous thread for more details: bsky.app/profile/mlei...
journals.aps.org
Information thermodynamics of cellular ion pumps
The framework of bipartite stochastic thermodynamics is a powerful tool to analyze a composite system's internal thermodynamics. It has been used to study the components of different molecular machine...
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mleighton.bsky.social @mleighton.bsky.social · 10/02/2026
Out today in @physrevresearch.bsky.social: "Information Thermodynamics of Cellular Ion Pumps", led by Julián Jiménez-Paz (@cornelluniversity.bsky.social), with @davidasivak.bsky.social (@sfuphysics.bsky.social)!
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David Hathcock @davidhathcock.bsky.social · 09/01/2026
Last call for postdoctoral applications to join my group at University of Toronto. Feel free to reach out if you want to learn more about the position.
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David Hathcock @davidhathcock.bsky.social · 22/12/2025
Postdoctoral Job Opportunity! I am hiring a postdoc to join my group next fall. Many possible research directions in theoretical biophysics and nonequilibrium statistical mechanics. See the posting linked below for details — apply by Jan 15th for full consideration.
physics.utoronto.ca
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
Finally, thanks to Yale's Department of Physics and Quantitative Biology Institute, and the Natural Sciences and Engineering Research Council of Canada (NSERC) for supporting our work!
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
The non-Markovian dynamics of biological systems arise from coarse-graining the underlying fundamental physics to produce simplified descriptions. Going forward, our work paves the way to study how non-Markovian dynamics emerge through coarse-graining across scales. Stay tuned!
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
This is roughly the timescale biologists have identified as the length of the fly’s working memory for both odor sensing and navigation, which we’ve detected using only recorded behavior data! This hints that memory is the main driving factor for the fly’s non-Markovian behavior.
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
We then turn to biological data, 400,000 minutes of recorded fruit fly behavior with 10ms time resolution. Our most intriguing result: we discover a unique timescale that maximizes how much information the fly’s past behavior holds about its future behavior, about 7 seconds.
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
We first explore analytically-tractable minimal models for non-Markovian dynamics. This lets us build intuition for the highly counterintuitive behavior of non-Markovian dynamics with long-range history dependence. E.g.: autocorrelations can fail to reflect true dependencies!
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
We develop new information-theoretic tools to 
 1. Quantify how strongly the dynamics of biological systems depend on their past. 2. Decompose this dependence into contributions from different parts of the past, quantifying the information you gain by learning each past state.
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
arxiv.org/abs/2512.13933 arxiv.org/abs/2512.13936 The fundamental laws of physics are Markovian: the next state of a physical system depends only on its current state. Biology, however, is often non-Markovian: the next state can depend on states arbitrarily far back into the past.
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
Excited to share two final preprints for the year: “Decomposing Non-Markovian History Dependence”, and “Tractable Model for Tunable Non-Markovian Dynamics”. Both with chriswlynn.bsky.social.
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
This is roughly the timescale biologists have identified as the length of the fly’s working memory for both odor sensing and navigation, which we’ve detected using only recorded behavior data! This hints that memory is the main driving factor for the fly’s non-Markovian behavior.
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
We then turn to biological data, 400,000 minutes of recorded fruit fly behavior with 10ms time resolution. Our most intriguing result: we discover a unique timescale that maximizes how much information the fly’s past behavior holds about its future behavior, about 7 seconds.
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
We first explore analytically-tractable minimal models for non-Markovian dynamics. This lets us build intuition for the highly counterintuitive behavior of non-Markovian dynamics with long-range history dependence. E.g.: autocorrelations can fail to reflect true dependencies!
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
We develop new information-theoretic tools to 
1. Quantify how strongly the dynamics of biological systems depend on their past. 2. Decompose this dependence into contributions from different parts of the past, quantifying the information you gain by learning each past state.
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mleighton.bsky.social @mleighton.bsky.social · 17/12/2025
arxiv.org/abs/2512.13933 arxiv.org/abs/2512.13936 The fundamental laws of physics are Markovian: the next state of a physical system depends only on its current state. Biology, however, is often non-Markovian: the next state can depend on states arbitrarily far back into the past.
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FQxI @fqxi.org · 27/09/2025
Johann du Buisson, Jannik Ehrich, mleighton.bsky.social, davidasivak.bsky.social, and John Bechhoefer introduce a one-coordinate test that infers heat flow to flag “demonic” operation. In kinesin simulations tuned to experiments, the motor grows more demon-like as active fluctuations rise.
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Qiwei Yu @qiweiyu.bsky.social · 07/07/2025
Our recent work (elifesciences.org/articles/104...) combines theory and experiments (by Alex Papagiannakis and Christine Jacobs-Wagner) to understand how chromosome segregation is coupled to growth in E coli. We demonstrate that the nonequilibrium dynamics of polysomes may play a key role.
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mleighton.bsky.social @mleighton.bsky.social · 17/06/2025
Thanks to NSERC, the Canada Research Chairs program, and @sfuphysics.bsky.social for supporting our research! Special thanks to @mitacscanada.bsky.social for funding Julian’s time as a visiting researcher in the Sivak Group last Summer.
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mleighton.bsky.social @mleighton.bsky.social · 17/06/2025
Over the voltage range typical of a neuronal action potential, at low voltages the pump exhibits Maxwell-demon behavior and high efficiency, while at high voltages the pump instead operates as a conventional engine and achieves higher turnover at the cost of lower efficiency.
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mleighton.bsky.social @mleighton.bsky.social · 17/06/2025
Remarkably, we find that sodium-potassium pumps can exhibit Maxwell-demon behavior, supporting internal information flow that enables the ion-transporting subsystem to leverage thermal fluctuations to produce useful electrochemical work.
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mleighton.bsky.social @mleighton.bsky.social · 17/06/2025
We study sodium potassium pumps through the lens of bipartite stochastic thermodynamics, identifying and computing energy and information flows between the ATP-consuming and ion-transporting parts of these machines.
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mleighton.bsky.social @mleighton.bsky.social · 17/06/2025
These pumps are nanoscale molecular machines that consume chemical energy to transport ions across membranes into, out of, and within cells. They are essential both for maintaining cellular homeostasis, and for propagating electrical signals in neurons.
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mleighton.bsky.social @mleighton.bsky.social · 17/06/2025
New preprint out today: “Information Thermodynamics of Cellular Ion Pumps”. Led by talented undergraduate student Julián Jiménez-Paz, and working with @davidasivak.bsky.social, we explore the thermodynamics of cellular ion pumps like the sodium-potassium pump. arxiv.org/abs/2506.11248
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davidasivak.bsky.social @davidasivak.bsky.social · 09/06/2025
Canada Excellence Research Chairs offer $4-8M over eight years to build a world-class research focus.
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mleighton.bsky.social @mleighton.bsky.social · 05/06/2025
Interested in coarse-graining, irreversibility, or neural activity in the hippocampus? If so, check out our new preprint exploring how maximizing the irreversibility preserved from microscopic dynamics leads to interpretable coarse-grained descriptions of biological systems!
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davidasivak.bsky.social @davidasivak.bsky.social · 02/06/2025
Postdoc opportunity! Join us in heavenly Vancouver (Canada) to develop fundamental nonequilibrium stat mech, thermo, and info theory applied to biomolecular machines and in close collaboration with experiment. Details: www.sfu.ca/physics/siva...
sfu.ca
Postdoc Ad
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