About emergence... algorithmic style (compression rules!). Preprint updated (and submitted). Algorithmic 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. @bcom_foundation @Castaldo_Fr doi.org/10.5281/zenodo.21008…

Wonderful paper, wonderful collaboration, wonderful collaborators! And a very fun paper to write... @bcom_foundation

The things that stay, with @Castaldo_Fr @bcom_foundation. 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? Dive into Kolmogorov Theory and the computational nature of identity. #Philosophy #CognitiveScience #KT giulioruffini.github.io/asse…

Download our "Rosetta Stone of Neural Mass Models" review paper for a limited time (preprint also available at arXiv)! By transforming a repertoire of disparate formalisms into a navigable ladder, we hope to help select NMM choice for brain modeling and translate between scales, modalities, and interventions.authors.elsevier.com/a/1nH4W…

Do 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. 2/5 In a new paper, "The Algorithmic Regulator" (published in Entropy), I tackle this using Algorithmic Information Theory and Kolmogorov Complexity. 🧠💻 It takes the classic Good Regulator Theorem and the Internal Model Principle and complements and extends them for non-linear, deterministic systems. 3/5 The paper looks at regulation as data compression. The proof shows that if a regulator successfully keeps a system (embedded in the world) stable (reducing the algorithmic complexity of its output), it mathematically must share "mutual algorithmic information" with the world with high probability. The key result: K(W∣R)mdpi.com/1099-4300/28/3/257

🧵 Thread: The Laminar Neural Mass Model (LaNMM) — A Unified Framework for Oscillatory Dynamics in Health and Disease 1. Intro Let me introduce a modeling framework that my team has developed for computational neuroscience: the Laminar Neural Mass Model (LaNMM). This approach links circuit-level mechanisms to EEG/MEG biomarkers across brain states — from healthy function to disease and altered consciousness. 2. The Laminar Neural Mass Model (2022) sciencedirect.com/science/ar… biorxiv.org/content/10.1101/… Our original LaNMM papers (2019, 2022) introduced a physics-grounded neural mass model capable of generating both alpha/beta and gamma oscillations across cortical layers. It embeds synaptic sources into a spatially layered medium, enabling simulation of laminar LFPs and their spectral features. 3. Key Insight Alpha/beta (slow/fast) rhythms are generated in deep layers, gamma in superficial ones. The model’s structure allows us to simulate realistic depth-resolved LFP, bipolar LFP, and CSD — crucial for aligning with experimental macaque data and uncovering oscillatory origins. Now comes the trilogy of applied studies using LaNMM: 4. AD Study (2025) biorxiv.org/content/10.1101/… In this AD-focused work, the model simulates fast interneuron (PV+) dysfunction and later-stage pyramidal cell loss. This reproduces AD’s biphasic M/EEG trajectory: early hyperexcitability (↑gamma, ↑alpha), followed by slowing and hypoactivity. 5. Mechanistic Findings Aβ oligomers impair PV+ interneurons → disinhibition → hyperactivity. Later, tau pathology hits pyramidal neurons → hypoactivity. The LaNMM shows that PV+ dysfunction alone explains the early phase EEG biomarkers, but tau-induced hypoactivity is required to match reduced firing/metabolism in advanced stages. 6. Clinical Relevance This model suggests PV+ cells are a therapeutic target — and that EEG spectral changes can serve as early-stage biomarkers. It bridges molecular pathology and mesoscopic dynamics. 7. Psychedelics + AD (2024) biorxiv.org/content/10.1101/… In this follow-up study, the LaNMM is embedded in whole-brain models personalized to AD patients. Activation of 5-HT2A receptors (mimicking psychedelics) increases excitability in L5 pyramidal cells — counteracting AD-related oscillatory deficits. 8. Restoration of Dynamics Under 5-HT2A modulation: → ↓ Alpha power (reduces hypersynchrony) → ↑ Gamma power (restores PV-related processing) → ↑ Entropy/complexity (a proxy for cognitive flexibility) 9. Spatial Specificity Spectral changes correlate with PET-derived 5-HT2A receptor distributions — suggesting that psychedelics could restore oscillatory dynamics in AD via targeted circuit modulation. 10. Prediction Error & CFC (2025) biorxiv.org/content/10.1101/… Our latest paper goes deeper into theory. It proposes that LaNMM supports biologically plausible Comparator functions via cross-frequency coupling (CFC), enabling local prediction error evaluation in predictive coding. 11. Key Mechanisms • Signal-Envelope Coupling (SEC): Low-freq rhythms modulate the amplitude of fast oscillations (PAC-like). • Envelope-Envelope Coupling (EEC): Slow envelopes modulate fast envelopes — allowing gating and precision weighting (think Kalman gain). 12. Comparator Disruption Across Conditions → In AD: Interneuron loss disrupts CFC, leading to inflated early prediction errors and their later suppression. → In psychedelics: Increased gain weakens prediction precision → “relaxed beliefs” and increased error signaling. 13. Comparator Hypothesis CFC in LaNMM instantiates the analog version of the hierarchical XOR-like error computation central to predictive coding and KT/AIF. This formalization bridges algorithmic models with real electrophysiology. 14. Why This Matters Together, these papers show how LaNMM provides a unified, mechanistic scaffold for studying: • Neurodegeneration (AD) • Psychedelic states • Predictive inference • Oscillatory biomarkers • Interneuron dynamics 15. 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 @Neuroelectrics @KarlFristonNews @GustavoDeco @Castaldo_Fr @ne_sanchez_todo @galvani_lab @ERC_Research @alvaropleone @VohryzekJakub 🧵/end

We have released an updated version of our AIT/KT MDD paper, "The algorithmic agent perspective and computational neuropsychiatry: from etiology to advanced therapy in major depressive disorder." osf.io/preprints/psyarxiv/eq…. 1/n