Giulio Ruffini is a theoretical physicist who left fundamental physics to model brains, and in the process built one of the more rigorous mainstream attempts to say what consciousness is in mathematical terms. His Kolmogorov Theory of consciousness holds that awareness is the signature of a system that has found compressive models of its world — that to be conscious is to model, and to model well is to compress. He occupies a specific structural role here, and it is a more delicate one than the idealist authors: Ruffini arrives at nearly the operative picture developed here — a self-modeling agent that compresses experience and selects among its own states — while starting from the premise that the brain is fundamentally an information processor. The convergence is on the mechanism, not the ontology, and that is exactly what makes it worth marking.
Biography
Ruffini trained as a theoretical physicist, completing a PhD in quantum gravity before his work turned toward the practical physics of signal and measurement. His early career ran through radar and satellite reflectometry — GNSS-R, the use of reflected GPS signals to sense the Earth — the discipline of pulling structure out of noisy electromagnetic returns. The trajectory matters in the same way it does for Kastrup: he came to the mind from inside the hard sciences, trained to distinguish a real signal from an artifact, rather than through the humanities.
In 2000 he co-founded Starlab in Barcelona, a research company translating neuroscience and space science into instruments. In 2011 he co-founded Neuroelectrics, which builds non-invasive electrical brain-stimulation technology — tDCS, tACS, and related transcranial methods — driven by computational models of the cortex. In 2025 he established the Barcelona Computational Foundation, a nonprofit pursuing life, brains, and minds as fundamentally computational phenomena. Across all three the through-line is a single conviction: that the brain is a modeling engine, and that its dynamics can be read, formalized, and driven.
The Kolmogorov Theory
The theory takes its name from Kolmogorov complexity — the length of the shortest program that reproduces a given string. Ruffini’s proposal, developed across the Kolmogorov Manifesto (2007) and An algorithmic information theory of consciousness (2017), is that the brain’s central task is to discover compact generative models of the data it receives, and that structured conscious experience is what an efficient compressive model feels like from the inside. A mind is an algorithmic agent: it builds the shortest program that predicts and controls its world, and it acts to maximize an objective function that the later work grounds in affective valence. On this reading cognition is compression through and through — the search for the shortest model that predicts and controls, with nothing left over. Understanding is compression, and the felt coherence of a conscious moment is the felt coherence of a good model.
The clinical work extends this into computational neuropsychiatry: disorders of mind become disorders of the agent’s modeling and valuation — a depression is a model stuck in a bad basin — and the stimulation technology becomes a way of perturbing the dynamics back toward health. His group’s neural-mass and laminar models (the same lineage surveyed in the Rosetta Stone of Neural Mass Models) are the formal machinery underneath, the mathematics of cortical oscillation treated as the substrate of the modeling agent.
Where It Meets the Framework
The Human Chord describes consciousness as cross-frequency phase-locked resonance and defines agency as a bandwidth-coherence product; the neural-mass-model tradition Ruffini’s lab helped systematize is the rigorous account of how those oscillations arise and couple. On the mechanism, the framework and the establishment are reading the same object, and Ruffini is one of the people who wrote the establishment’s version.
The deeper resonance is with the selection faculty. The framework’s sorting agent pulls order from noise and pays the thermodynamic cost in erasure; the agency-seconds measure the recurrence of self-model-mediated state selection. Ruffini’s algorithmic agent — a system that compresses its world and selects the action that best serves its objective — is that same faculty stated in the vocabulary of information theory. The demon that sorts microstates and the agent that finds the shortest program are two descriptions of one operation: a system reducing the entropy of its own model of the world. That the sorter and the compressor are one faculty is a claim the framework already carries; Ruffini reaches it from the computational side without needing the framework at all.
And the honest boundary is where the value lies. The Kolmogorov Theory is functionalist and near-eliminativist about the hard problem: it begins by assuming the brain is an information processor, which is close to the inverse of consciousness primacy. Ruffini is not an ally on ontology the way Kastrup is; he would likely decline the framework’s foundational move. What he supplies is stronger for the disagreement — an independent reconstruction of the framework’s picture of the conscious faculty, built by someone with no stake in its metaphysics, from the premise it denies. Two accounts that disagree about what is fundamental have converged on what the faculty does. The convergence on the operation, across the ontological divide, is the datum worth keeping, and the divide itself is the honest thing to leave standing.
References
Ruffini, G. “Information, complexity, brains and reality (Kolmogorov Manifesto).” arXiv:0704.1147, 2007.
Ruffini, G. “An algorithmic information theory of consciousness.” Neuroscience of Consciousness, 2017(1), nix019.
Ruffini, G. et al. “The Algorithmic Agent Perspective and Computational Neuropsychiatry.” Entropy, 26(11), 953, 2024.
Castaldo, F., de Palma Aristides, R., Clusella, P., Garcia-Ojalvo, J., Ruffini, G. “Rosetta Stone of Neural Mass Models.” arXiv:2512.10982, 2025.