Ricard Solé
@ricardsole.bsky.social
Scientist & skeptic. Dad. Book addict. Pathologically curious. Origins and Evolution of Complexity, Synthetic Transitions, Liquid Brains, and Earth Terraformation. ICREA + SFI professor. Author. Secular humanist.
Biology
28%
Physics
16%
Pinned
Back to @sfiscience.bsky.social joining the night shift (with some extra coffee) at the Cormac McCarthy's Library. Working on criticality + cancer, statistical physics of ant colonies, the Physarum Lagrangian, universal genetic codes, synthetic agriculture & hybrid agencies.
@jordiplam.bsky.social
@jordiplam.bsky.social
Big Brother is already here.
Reposted by Ricard V. Solé
When matter came alive: the physics of life’s emergence
Exploring the origins of life through the mathematical theory of transitions
manlius.substack.com
Phase transitions, bifurcations, thermal vents, exoplanets and their interactive maps, as well as pointers to a whole special issue just published in @royalsocietypublishing.org about origin of life.
With Spotify and Apple podcasts as usual.
What do you need more?
CC: @ricardsole.bsky.social
With Spotify and Apple podcasts as usual.
What do you need more?
CC: @ricardsole.bsky.social
I am really intrigued by your comment...
Thanks!
... which connects with the problem of individuality. Multicellular organisms (animals in particular) achieve a unique potential to develop nervous systems and learn through their lives. The multicellularity of biofilms and bacterial aggregates has a very different nature and limits.
Agreed.
I would say that the Physarum Lagrangian has the same variational structure as a free-energy functional: flows act like variational parameters, the dissipation term plays the role of expected energy, and the conservation constraints act like normalization constraints. First thoughts...
Indeed, I think these properties are too often used to make big claims about intelligence that are not supported by the observations. Bacteria in particular display remarkable collective patterns which can have adaptive meaning, but the range of behavioral responses is very limited.
There is a key point beyond the approach taken here, which is to articulate a message concerning the ongoing discussion about basal cognition and its origins. We try to make clear that the physics side of the process when dealing with a predefined graph is the right origin of the "smart" behavior.
Writing this as a Lagrangian is simply a compact variational device—it enforces “least dissipation under constraints”. The true Physarum dynamics occur in the slow evolution of the edge conductivities, while the instantaneous flow is always the solution of this constrained least-action problem.
I totally agree (and I think we made that clear) that in Physarum models, the flow at any instant is the minimizer of a dissipation functional under Kirchhoff. Our formulation does not differ from other models on networks rouring (Kelly, Phil Trans 1991) wjere a similar Lagrangian aproach is taken.
Thanls for your comments Alessandro and for pointing to your paper (great stuff). The goal here was to show that the Physarum dynamics, which has been studied using ode models onn graphs, can be mapped into a rather standard physics optmization problem onn a graph.
As you probably know, there is no universal answer (yet) for that question or for defining "cognition". We can define Intelligence is the capacity of a system to acquire information, learn from it, and use in changing environments.
Thanks for mentioning this. Is different in many ways although they share a path to solving a problem grounded in a physics minimization process.
Very good point. In fact we were considering to include in the discussion the potential extension to ant colonies and the double bridge problem, bt it turns out that, although a Lagrangian can be defined, several key things are different in a very interesting way. So... more research ahead.
Thanks Audrey! I hope you like it. Of course our great inspiration is the NKT paper, and ot has been great to study the problme from a physics perspective that I believe can help iluminate where we can locate Physarum (what an neverendingly interesting organism) within the space of cognitions.
We do not say that Physarum lacks cognition, but a big part of it is driven by a universal physical principle that is successfully epxloited by the giant cell (which also has learning and memory). We (complex animals) have cognitive complexities that belongg to a very different class.
sites.santafe.edu
Concerning those "for free" components of evolutionary complexity, we do think there is much to understand by looking at fundamental constraints: sites.santafe.edu/~lachmann/pu...
Yes, thanks. We don't try to disprove it, but make th epoint (with a formal approach) that, when dealing with predefined boundary conditions (mazes etc), we should not overinterpret the dynamics of the giant cell, which exploits least action under a rather physics-based mechanism.
But one of our goals is to map the cognition space to understand the potential classes of cognitive complexity that (I believe) partition the space and how each class emerges thorugh evolution. And of course of the first animals occupied that space are one of the most fascinating problems.
Physarum is in some respects an outlier within the space of cognitive complexity, given its unicellular nature. The way it changes shape and explores its environment has some common points with ants and plants, but still a unique one. That's why we could use least action here.
but of course there are other aspects (learning, memory) as well as features of the dynamics (such as oscillations) that need to be added to the whole picture. And yes, least action could reveal a precondition for cognitive complexity in different systems. Anyways, what a fascinating organism.
Thanks Mike. Here we consider those aspects of Physarum complexity that deal with specified (and thus external) graphs associated with problem solving. As it happens with non-living transport systems, we show that Physarum finds the solutions through the functional minimization...
Reposted by Olivier Morin
How “intelligent” is a slime mold? When it solves mazes, it might not be thinking:it’s obeying physics. Our new paper with
@jordiplam.bsky.social shows how it follows a least action principle,letting physics do the job arxiv.org/pdf/2511.08531
@drmichaellevin.bsky.social @docteur-drey.bsky.social
@jordiplam.bsky.social shows how it follows a least action principle,letting physics do the job arxiv.org/pdf/2511.08531
@drmichaellevin.bsky.social @docteur-drey.bsky.social
Reposted by Ricard V. Solé
Loeb’s 3I/ATLAS “Anomalies” Explained
Avi Loeb continues to claim that 3I/ATLAS has many anomalous behaviors that lead to the conclusion that it “might” be an alien spacecraft. He carefully hedges the probability that it is a spacecraft ...
sites.psu.edu
Great piece by astronomer and SETI leader Jason Wright on the endless, exhausting claims about 3I/Atlas by Avi Loeb.
"zero planetary scientists give Avi’s claims any credence... because he’s demonstrably wrong"
sites.psu.edu/astrowright/...
"zero planetary scientists give Avi’s claims any credence... because he’s demonstrably wrong"
sites.psu.edu/astrowright/...
