Giulio Ruffini @ruffini.bsky.social · 10/09/2026Algorithmic emergence: the event where an observer/agent discovers a useful representation and reusable model that reveal regularity it could not use before. Compression may not exist—and even when it does, every reliable method that always halts has blind spots. 110
Giulio Ruffini @ruffini.bsky.social · 13/07/2026New preprint out! => The cortical column as a tuned receiver: a network mechanism for temporal-interference stimulation. We propose a mechanism for #TI leveraging oscillatory computation in the brain. How? Nonlinearity + resonant network amplification, like AM radio! 📻 doi.org/10.5281/zeno... 010
Giulio Ruffini @ruffini.bsky.social · 13/07/2026What survives the Filter of Time? Will we? What do a proton, a flame, a chemotactic cell, and an LLM agent have in common? ⚛️🧬🐕🤖 (fast intro: giulioruffini.com/blog/) @bcom-foundation.bsky.social 211
Giulio Ruffini @ruffini.bsky.social · 06/07/2026The things that stay. @bcom-foundation.bsky.social You cannot step into the same river twice—but the river remains. 🌊 From Aristotle to Alan Turing, how do we persist when our atoms are constantly replaced? #Philosophy #CognitiveScience #KT giulioruffini.github.io/assets/blogs... 141
Giulio Ruffini @ruffini.bsky.social · 28/03/2026The transformer has no memory. Some thoughts on LLMs, memory, RAG, and how to use Claude. bcomone.atlassian.net/wiki/spaces/... @bcom_foundation @bcom-foundation.bsky.social @francesca-castaldo.bsky.social 141
Giulio Ruffini @ruffini.bsky.social · 03/03/2026Do you need to understand the world to survive in it? A classic 1970 cybernetics theorem says yes: "Every good regulator of a system must be a model of that system." But proving this mathematically for complex, unpredictable real-world scenarios has always been notoriously difficult. 1/5 120
Giulio Ruffini @ruffini.bsky.social · 16/02/2026This 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! 110
Giulio Ruffini @ruffini.bsky.social · 15/02/2026An 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. 150
Giulio Ruffini @ruffini.bsky.social · 15/02/2026From The Sorcerer's Apprentice to Crystal Nights: Security Implications from Moltbot/Moltbook to Greg Egan's Crystal Nights! zenodo.org/records/1844... 000
Giulio Ruffini @ruffini.bsky.social · 31/12/2025My annotated slides for my talk in the wonderful thoughtforms.life/symposium-on... organized by @drmichaellevin are here: giulioruffini.github.io/assets/slide... 010
Giulio Ruffini @ruffini.bsky.social · 15/12/202514/ Other goodies: discussion using L-operators (for synapses) and transfer functionals. 100
Giulio Ruffini @ruffini.bsky.social · 15/12/2025Definition: 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 100
Giulio Ruffini @ruffini.bsky.social · 15/12/202512/ 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 100
Giulio Ruffini @ruffini.bsky.social · 15/12/20258/ 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. 100
Giulio Ruffini @ruffini.bsky.social · 15/12/20252/ 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. 110
Giulio Ruffini @ruffini.bsky.social · 15/12/20251/ 🧠 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... 130
Giulio Ruffini @ruffini.bsky.social · 05/11/20254/5 Proof‑of‑concept in a laminar neural mass model: a fast PING‑like circuit acts as the carrier; a slower Jansen‑Rit circuit modulates/extracts the envelope. We observe gamma sidebands and staged demodulation (Fig. 3.1, p. 14). 100
Giulio Ruffini @ruffini.bsky.social · 05/11/2025HAM also explains spectral “1/f” structure. In a cascade, the aperiodic slope obeys α = 2 ln(2/m)/ln r (m=modulation depth, r=spacing). A log‑uniform bank of oscillators gives a 1/f backbone (see Fig. 2.3, p. 10). 3/5 100
Giulio Ruffini @ruffini.bsky.social · 05/11/2025This multiplicative mixing creates intermodulation terms (fc ± Σ ai fi). To keep bands separable & demodulable, centers should be log‑spaced with ratio r ≳ 2–3 (constant‑Q)—matching canonical delta→gamma spacing (see Fig. 1.1, p. 5). 2/5 100
Giulio Ruffini @ruffini.bsky.social · 05/11/2025New preprint 🪢: Neural Encoding through Hierarchical Amplitude Modulation (HAM). Slower rhythms multiplicatively modulate the amplitude of faster ones, so information can live in signals, their envelopes, and envelopes‑of‑envelopes. 1/5 121
Giulio Ruffini @ruffini.bsky.social · 15/10/20258) Bonus: the regulator displays an improved compression with regulator ON ⇒ the regulator shares structure with the world. Bonus: the regulator displays as-if behavior with Objective Function + Policy (algorithmic agenthood!). 100
Giulio Ruffini @ruffini.bsky.social · 15/10/20257) Example (thermostat): a dead‑band bang‑bang thermostat (no integrator ⇒ fails IMP for constant refs) can still be a good algorithmic regulator if it keeps the error stream regular/compressible (e.g., a simple limit cycle). 100
Giulio Ruffini @ruffini.bsky.social · 15/10/20256) Relation to IMP: The Internal Model Principle gives structural necessity (embed a copy of the exosystem for perfect regulation). Our result gives information‑theoretic necessity from single episodes, with no linearity/probability/exactness assumptions. 120
Giulio Ruffini @ruffini.bsky.social · 15/10/20255) Main theorem: a statement about the posterior on the W, R pair. Meaning: a sustained compressibility advantage is evidence that the regulator contains a model of the world, formalized as positive mutual algorithmic information – M(W,R)>0. 100
Giulio Ruffini @ruffini.bsky.social · 15/10/20254) Following the AIT rabbit, we score regulation by the Kolmogorov complexity of the output K(x). This is actually intuitive (e.g., perfect regulation = output is all 0s!). 110
Giulio Ruffini @ruffini.bsky.social · 15/10/2025News🥁! Preprint on an AIT version of the Good Regulator Theorem. World (W) and Regulator (R) are Turing machines interacting via "interface" tapes. Regulation =: compression of W output (e.g., x = temperature series of a room). #Cybernetics #AlT #ActiveInference arxiv.org/abs/2510.10300 1/9 220
Giulio Ruffini @ruffini.bsky.social · 11/07/2025What does Kolmogorov Complexity have to do with brain stimulation? I say ... a lot! www.dropbox.com/scl/fi/7dekr... 120
Giulio Ruffini @ruffini.bsky.social · 11/07/2025My slides from The Science of Consciousness (Barcelona 2025) Plenary talk this week! "From Kolmogorov Theory to Computational Modeling and Brain Stimulation". #consciousness #neuromodulation 131
Giulio Ruffini @ruffini.bsky.social · 11/07/2025If you like the music, consider sending a paper to our special Issue, "The Mathematics of Structured Experience: Exploring Dynamics, Topology, and Complexity in the Brain". See www.mdpi.com/journal/entr... 011
Giulio Ruffini @ruffini.bsky.social · 11/07/2025Here's our poster – "Algorithmic Structure and Lie Groups" – at ICMNS 2025 (Barcelona): www.dropbox.com/scl/fi/qbn55... It is a summary of our recent paper, Structured Dynamics in the Algorithmic Agent (www.mdpi.com/1099-4300/27...). 110
Giulio Ruffini @ruffini.bsky.social · 10/04/202513. Conclusion LaNMM is not just another NMM — it’s a physically grounded, biophysically realistic platform that integrates structure, dynamics, and function. Expect to see more work where biology meets theory, with LaNMM at the core! @neurotwin @galvani_lab @neuroelectrics.bsky.social 020
Giulio Ruffini @ruffini.bsky.social · 10/04/2025🧵 Thread: 1. The Laminar Neural Mass Model (LaNMM) — A Unified Framework for Oscillatory Dynamics in Health and Disease (news!) The Laminar Neural Mass Model (LaNMM) links circuit-level mechanisms to M/EEG biomarkers across brain states — from healthy function to disease and altered consciousness. 120
Giulio Ruffini @ruffini.bsky.social · 23/12/2024Psychedelics increase EEG complexity and entropy—markers of a flexible, healthier brain network. This may counteract AD’s rigid neural patterns. #Entropy #NetworkFlexibility See prior work in mdpi.com/1099-4300/26... and journals.plos.org/ploscompbiol... 110
Giulio Ruffini @ruffini.bsky.social · 23/12/2024Key result: Psychedelics suppress alpha band power (linked to hyper-synchronization in AD) and enhance gamma oscillations (important for cognitive processes). This rebalances brain dynamics. #BrainOscillations 100
Giulio Ruffini @ruffini.bsky.social · 23/12/2024Based on our laminar model, we model serotonin receptor activation in 30 AD patients using multimodal neuroimaging (MRI, fMRI, PET). The models simulate EEG changes under psychedelics, revealing insights into altered neural dynamics. #AI #BrainModels 100
Giulio Ruffini @ruffini.bsky.social · 23/12/2024Current treatments for AD address symptoms but fail to tackle the underlying disruptions in brain dynamics. Psychedelics, like serotonin 2A receptor agonists, may offer a novel therapeutic pathway. #Alzheimers #Neuroscience 100
Giulio Ruffini @ruffini.bsky.social · 15/11/2024Hypothesis of Structured Experience in KT: Structured experience (𝒮) arises when an agent uses compressive, accurate models to interact with the world. In depression, one or more agent systems falter, leading to persistent low valence. 🌐 KT offers a roadmap to model-driven neuropsychiatry. 010
Giulio Ruffini @ruffini.bsky.social · 15/11/2024Why does an agent enter a state of low valence? In this framework, depression stems from disruptions in: •Modeling Engine (linked to posterior cingulate cortex, hippocampus) •Objective Function (amygdala, ventral striatum) •Planning Engine 3/n 000
Giulio Ruffini @ruffini.bsky.social · 16/12/2023New preprint for the KT framework released! After Neural Geometrodynamics (www.biorxiv.org/content/10.1...) comes Structured Dynamics in the Algorithmic Agent. sciencecast.org/casts/jq63ve... 030