Christopher W. Lynn

Republicans Want States to Cut Food Aid Errors. Their Bill Could Do the Opposite.

Republicans want states to cut food aid errors. Their bill could do the opposite: www.nytimes.com/2025/07/02/u...

Discovering cognitive strategies with tiny recurrent neural networks - Nature Modelling biological decision-making with tiny recurrent neural networks enables more accurate predictions of animal choices than classical cognitive models and offers insights into the underlying cog...

Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately while staying fully interpretable. This approach can transform how we model cognitive processes in both healthy and disordered decisions. doi.org/10.1038/s415...

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!

Living systems operate nonequilibrium processes across many scales in space and time. Is there a model-free way to bridge the descriptions at different levels of coarse-graining? Here we find that preserving the evidence of time-reversal symmetry breaking works remarkably well!

Check out the preprint for much more: "Coarse-graining dynamics to maximize irreversibility"

And a massive shout out to the leaders of the project: Qiwei Yu (@qiweiyu.bsky.social‬) and Matt Leighton (@mleighton.bsky.social‬)

In neural dynamics in the hippocampus, the maximum irreversibility coarse-graining uncovers a large-scale loop of flux in neural space that is directly driven by the animal's movement in physical space.

In chemical oscillators, the maximum irreversibility coarse-graining picks out macroscopic loops of flux that dominate the dynamics.

Across a range of living systems, this maximum irreversibility coarse-graining uncovers key biological functions.

For example, in models of kinesin (a motor protein that ships cargo inside your cells), we can derive simplified dynamics without losing any irreversibility.

When living systems burn energy, they drive irreversible dynamics and produce entropy.

Under coarse-graining, the apparent irreversibility can only decrease.

This means that -- at every level of description -- there's a unique coarse-graining with maximum irreversibility.

Biology consumes energy at the microscale to power functions across all scales: From proteins and cells to entire populations of animals.

Led by @qiweiyu.bsky.social‬ and @mleighton.bsky.social‬, we study how coarse-graining can help to bridge this gap 👇🧵

arxiv.org/abs/2506.01909

Coarse-graining dynamics to maximize irreversibility In many far-from-equilibrium biological systems, energy injected by irreversible processes at microscopic scales propagates to larger scales to fulfill important biological functions. But given dissip...

Check out the preprint for much more: "Coarse-graining dynamics to maximize irreversibility"

And a massive shout out to the leaders of the project: Qiwei Yu (@qiweiyu.bsky.social) and Matt Leighton

arxiv.org/abs/2506.01909

In neural dynamics in the hippocampus, the maximum irreversibility coarse-graining uncovers a large-scale loop of flux in neural space that is directly driven by the animal's movement in physical space.

In chemical oscillators, the maximum irreversibility coarse-graining picks out macroscopic loops of flux that dominate the dynamics.

Across a range of living systems, this maximum irreversibility coarse-graining uncovers key biological functions.

For example, in models of kinesin (a motor protein that ships cargo inside your cells), we can derive simplified dynamics without losing any irreversibility.

When living systems burn energy, they drive irreversible dynamics and produce entropy.

Under coarse-graining, the apparent irreversibility can only decrease.

This means that -- at every level of description -- there's a unique coarse-graining with maximum irreversibility.

Science is built on public trust, federal funding, and academic freedom. All of these are currently being eroded, but what can we do?

Join us for an online panel on "Being a Voice for Science" (June 11, 2-3pm ET).

Info and registration: engage.aps.org/dbio/resourc...

Apply or nominate for DBIO awards by June 2! Awards include:

APS Fellowship

Max Delbrück Prize in Biological Physics

Award for Outstanding Doctoral Thesis Research in Biological Physics

Huge shoutout to my student David Carcamo for leading this paper, and Nick Weaver and Purushottam Dixit for invaluable contributions! ❤️

...but these applications are limited by our ability to solve difficult statistical physics problems! We thus need new creative methods in order to construct optimal compressions of complex systems.

We review emerging applications, which range from neuroscience and biology to machine learning and engineering...

Starting only with MDL, we show that the optimal details provide as much information about the data as possible while remaining maximally random with regard to all unobserved details

This "minimax entropy" principle was proposed 25 years ago but remains largely unexplored

Information theory makes these intuitions concrete: Each model gives us an encoding of the data, and the shortest code provides the best compression of the data. This is the minimum description length (MDL) principle

When constructing models of the world, we aim for good compressions: models that are as accurate as possible with as few details as possible. But which details should we include in a model?

An answer lies in the "minimax entropy" principle 👇
arxiv.org/abs/2505.01607

What can physics tell us about the brain? - Part 2 Podcast Episode · Simplifying Complexity · 05/12/2025 · 31m

What is the connection between information theory and statistical physics? And how can this connection help us understand the brain?

Final installment with the Simplifying Complexity podcast (
@bhcomplexity.bsky.social)

podcasts.apple.com/us/podcast/w...

What can physics tell us about the brain? - Part 1 Podcast Episode · Simplifying Complexity · 04/28/2025 · 35m

Statistical physics and information theory may seem daunting, but with a little insight they can become intuitive and are actually deeply related.

A fun chat on the Simplifying Complexity podcast (@bhcomplexity.bsky.social)

podcasts.apple.com/us/podcast/w...

Hear Christopher Lynn from Yale on the latest episode of #SimplifyingComplexity.

How can we use the ideas of complexity and network science to better understand the brain?

Apple: buff.ly/3VTjG9I
Spotify: buff.ly/3WaR9MA

🧪⚛️ #complex #complexity #ComplexSystems #ComplexityScience

Simple low-dimensional computations explain variability in neuronal activity Our understanding of neural computation is founded on the assumption that neurons fire in response to a linear summation of inputs. Yet experiments demonstrate that some neurons are capable of complex...

For much more, see the preprint: "Simple low-dimensional computations explain variability in neuronal activity"

Moreover, the inferred connection weights are 1. sparse, 2. heavy-tailed, 3. balanced, and 4. directed -- all key features observed in synaptic wiring between neurons

With only a small number of direct inputs (no interactions between inputs) we are able to predict complex higher-order dependencies on multiple inputs

This means that real neurons are closely approximated *quantitatively* by the first artificial neuron proposed by McCulloch and Pitts in 1943