Giulio Ruffini
@ruffini.bsky.social
Physicist working on computational neuroscience, brain stimulation and foundational aspects of (meta)physics, swimming and music during spare CPU cycles. Neuroelectrics.com, Starlab.es
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
@erc.europa.eu @neuroelectrics.bsky.social
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...
www.biorxiv.org
November 5, 2025 at 5:00 PM
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
@erc.europa.eu @neuroelectrics.bsky.social
4/5
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).
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).
November 5, 2025 at 5:00 PM
4/5
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).
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).
HAM 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
November 5, 2025 at 5:00 PM
HAM 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
This 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
November 5, 2025 at 5:00 PM
This 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
Congrats!!! Looking forward to collaborating ... BCOM BAM!
October 17, 2025 at 3:57 PM
Congrats!!! Looking forward to collaborating ... BCOM BAM!
9) arXiv post: "The Algorithmic Regulator" arxiv.org/abs/2510.10300
For related papers, see: giulioruffini.github.io/kt/#gsc.tab=0
For related papers, see: giulioruffini.github.io/kt/#gsc.tab=0
The Algorithmic Regulator
The regulator theorem states that, under certain conditions, any optimal controller must embody a model of the system it regulates, grounding the idea that controllers embed, explicitly or implicitly,...
arxiv.org
October 15, 2025 at 1:02 PM
9) arXiv post: "The Algorithmic Regulator" arxiv.org/abs/2510.10300
For related papers, see: giulioruffini.github.io/kt/#gsc.tab=0
For related papers, see: giulioruffini.github.io/kt/#gsc.tab=0
8) 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!).
Bonus: the regulator displays as-if behavior with Objective Function + Policy (algorithmic agenthood!).
October 15, 2025 at 1:02 PM
8) 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!).
Bonus: the regulator displays as-if behavior with Objective Function + Policy (algorithmic agenthood!).
7) 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).
October 15, 2025 at 1:02 PM
7) 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).
6) 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.
October 15, 2025 at 1:02 PM
6) 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.
5) 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.
October 15, 2025 at 1:02 PM
5) 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.
4) 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!).
October 15, 2025 at 1:02 PM
4) 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!).
3) Here, "R contains a model of the World" is defined through mutual algorithmic information, M(W,R)>0. This means one can compress W if given R, for example, (think of both of them as code/programs).
October 15, 2025 at 1:02 PM
3) Here, "R contains a model of the World" is defined through mutual algorithmic information, M(W,R)>0. This means one can compress W if given R, for example, (think of both of them as code/programs).
2) Basic idea: if switching the Regulator ON makes the readout more compressible than OFF, then the Regulator likely carries algorithmic info about the World (i.e., M(W : R)>0).
October 15, 2025 at 1:02 PM
2) Basic idea: if switching the Regulator ON makes the readout more compressible than OFF, then the Regulator likely carries algorithmic info about the World (i.e., M(W : R)>0).
July 11, 2025 at 10:24 PM
What does Kolmogorov Complexity have to do with brain stimulation? I say ... a lot!
www.dropbox.com/scl/fi/7dekr...
www.dropbox.com/scl/fi/7dekr...
July 11, 2025 at 10:24 PM
What does Kolmogorov Complexity have to do with brain stimulation? I say ... a lot!
www.dropbox.com/scl/fi/7dekr...
www.dropbox.com/scl/fi/7dekr...
If 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...
July 11, 2025 at 9:55 PM
If 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...
Great work, Rodrigo!
May 6, 2025 at 6:31 AM
Great work, Rodrigo!
Case in point! Check this nice work out -> arxiv.org/abs/2404.09937
Compression Represents Intelligence Linearly
There is a belief that learning to compress well will lead to intelligence. Recently, language modeling has been shown to be equivalent to compression, which offers a compelling rationale for the succ...
arxiv.org
April 10, 2025 at 11:19 AM
Case in point! Check this nice work out -> arxiv.org/abs/2404.09937
13. 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
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
April 10, 2025 at 11:17 AM
13. 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
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