Giulio Ruffini

Annotated version: oup.silverchair-cdn.com/oup/backfile...

This is the paper where, after almost 20 years, I started taking my AIT obsessions a bit more seriously. I am happy I did... maybe you have some too... => Don't wast time! : )
academic.oup.com/nc/article/2... PS: the Supplementary Data part is more fun!

Philip, agree on 1... but I think the road for studying consciousness is assuming primordial "experience" and instead focus on "structured experience" - and this connects directly with mathematics. See my talk in the "Platonic Space Symposium" thoughtforms.life/wp-content/u...

Thanks, Johannes!

An Introduction to Galois Theory (with connections to AIT): zenodo.org/records/1845... ...This note aims to demystify Galois Theory by connecting its foundational definitions to a broader principle of computational and compositional tractability.

From The Sorcerer's Apprentice to Crystal Nights: Security Implications from Moltbot/Moltbook to Greg Egan's Crystal Nights! zenodo.org/records/1844...

Could life have begun with simpler molecules than we once thought? A new paper in @science.org by @edogia.bsky.social shows that a tiny RNA catalyst can self-replicate itself, suggesting that life may have been easier to emerge than expected. Getting closer. www.biorxiv.org/content/10.1...

Updated version with graphical materials now available here: zenodo.org/records/1864...

Thanks for spreading the word, Ricard!

What if we had a Rosetta Stone for brain oscillations—one framework to translate between models and scales?
In this paper lead by F Castaldo and @ruffini.bsky.social they build a simple, systematic ladder of neural mass models showing how diverse formalisms connect arxiv.org/pdf/2512.10982

My annotated slides for my talk in the wonderful thoughtforms.life/symposium-on... organized by @drmichaellevin are here: giulioruffini.github.io/assets/slide...

Thanks! That was straight from the paper! Uploaded it, clicked on slides…. That’s it!

Paper: mdpi.com/1099-4300/27...
Related: arxiv.org/abs/2510.10586
More on Algorithmic agents: giulioruffini.github.io

Impressed: #notebooklm created a nice presentation of the Algorithmic Agent and Symmetry paper! github.com/giulioruffin...

15/ ... plus, linearization of Wilson-Cowan; Connecting SL with Wilson-Cowan.

14/ Other goodies: discussion using L-operators (for synapses) and transfer functionals.

Definition: A dataset is said to represent an oscillation when it can be most succinctly Lie-generated from a representation of U1 (plus noise). #ait #kolmogorov

12/ Bonus material: plenty of good stuff in the Appendix for aficionados, including links with Groups, Topology, and Algorithmic Information Theory (What is an oscillation)? @ERC_Research @neurotwin @Neuroelectrics

11/ If you use neural mass models (or teach them), I’d love feedback: what translation step is hardest in your workflow?
PDF: arxiv.org/pdf/2512.10982
#computationalneuroscience #neuralmass #EEG #MEG

10/ Practical cheat-sheet:
• Phase locking/entrainment → phase models
• Spectra/covariances → damped linear resonators
• Limit cycles near Hopf → Stuart–Landau
• Firing-rate E–I loops → Wilson–Cowan
• PSP/synaptic kinetics → NMM1
• First-principles spiking link → NMM2

9/ NMM2 (next-generation masses): Exact mean-field reductions of QIF networks yield dynamic (r,v) equations—a *dynamic* transfer function replacing static sigmoids, linking spikes ↔ masses.

8/ NMM1 (second-order synapses):
PING-like motifs, Jansen–Rit, Wendling, and laminar neural masses become variants of one formalism—
highlighting which parameters control resonance, PSPs, and phase shifts.

7/ Wilson–Cowan then appears as the same backbone made explicit: an E–I push–pull loop + delayed, nonlinear transfer → Hopf/limit cycles,
with clean recipes for forcing & coupling.

6/ Interlude: synapses + transfer functionals.
Synaptic filters create delays/phase lags; nonlinear transfer function(al)s map summed input → firing-rate output. We keep forcing/coupling consistent across model “dialects” via an operator viewpoint.

5/ Each rung is treated in 3 regimes:
(i) isolated node,
(ii) forced node (external drive),
(iii) coupled network.
Because that’s how models meet data *and* interventions.

4/ The undamped harmonic oscillator (phase) and the damped HO (phase and amplitude) are natural starting points. We climb a ladder:
HO → damping/forcing/coupling → driven resonator → nonlinearity → Stuart–Landau (Hopf normal form) → connect to classic firing-rate & synapse-based models.

3/ Neural mass models power EEG/MEG/fMRI generators, whole-brain simulations, and perturbation/stimulation studies—but the zoo of formalisms makes model choice & interpretation messy.

2/ Our starting point is simple:
Oscillations can be seen as a push–pull interaction between two effective degrees of freedom (think E↔I, or quadrature components).
That core motif survives as we add biology.

1/ 🧠 New in Computational Neuroscience: "Rosetta Stone of Neural Mass Models." An unifying framework connecting harmonic oscillator with Stuart-Landau, Wilson-Cowan, NMM1 & NMM2 (next generation). W. @Castaldo_Fr, R de Palma Aristides, P Clusella & J Garcia-Ojalvo - arxiv.org/abs/2512.109...

Neural Encoding through Hierarchical Amplitude Modulation Hierarchical encoding is a structural element of the Free Energy Principle and related information-centric accounts of brain function, but a concrete circuit-level mechanism for it remains elusive. He...

Predictions: (i) intermodulation components in frequency‑tagging, (ii) superficial carriers vs deep envelope readouts, (iii) perturb slow rhythms → envelope variance & PSD slope shifts. www.biorxiv.org/content/10.1... #Neuroscience #EEG #Oscillations
@erc.europa.eu @neuroelectrics.bsky.social