Neuroscience of Consciousness
January 1, 2017
Giulio Ruffini
75 citations
Conscious experience can be understood as a mental construct arising from information compression. Using algorithmic information theory, specifically Kolmogorov complexity, provides a natural framework to quantify consciousness from brain data, assuming the brain's primary role is information processing. The theory hypothesizes that compressive models in cognitive systems, such as biological recurrent neural networks, enable structured phenomenal experience, with self-awareness emerging naturally as part of a better model in systems interacting bidirectionally with the world. This approach, called KT theory, is compared to other information-centric theories, and methods are described for studying brain complexity as a correlate of conscious state through input probing, spontaneous activity analysis, perturbation, and behavioral quantification.
PLoS computational biology
February 1, 2023
Giulio Ruffini, Giada Damiani, Diego Lozano-Soldevilla et al.
28 citations
Brain dynamics under LSD become more disordered and complex, moving further from the critical point that characterizes healthy brain function. Using Ising spin models fitted to fMRI data from fifteen participants, the authors show that LSD reduces interhemispheric connectivity, especially between corresponding regions in opposite hemispheres. Ising temperatures were significantly higher under LSD than placebo, indicating a shift into a more disordered (paramagnetic) state. Algorithmic complexity of brain activity, measured by block decomposition, correlated with both Ising temperature and condition, supporting the entropic brain hypothesis that psychedelics increase neural disorder.
Entropy
January 22, 2024
Giulio Ruffini, Edmundo Lopez-Sola, Jakub Vohryzek et al.
15 citations
A framework called neural geometrodynamics, inspired by general relativity, describes how neural dynamics unfold at three timescales: fast (momentary activity), slow (synaptic plasticity), and ultraslow (metaplasticity). Psychedelics flatten the neural landscape, increasing entropy and complexity of fast dynamics, which disrupts functional integration. This destabilization counteracts pathological, rigid neural patterns by promoting fluid, adaptable states. The plasticity-enhancing effects of psychedelics amplify this shift, leading to acute systemic disorder and potentially longer-lasting increases in complexity that affect both short-term dynamics and long-term plastic processes, offering a holistic view of psychedelics' acute and lasting impacts.
Entropy
November 6, 2024
Giulio Ruffini, Francesca Castaldo, Edmundo Lopez-Sola et al.
10 citations
Major Depressive Disorder (MDD) is a complex condition that computational neuropsychiatry can help model mechanistically. Using the Kolmogorov theory of consciousness, a model was developed in which algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression—defined as persistently low valence—may arise from inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. The model maps to brain circuits and functional networks, linking with depression biotypes. Brain stimulation, psychotherapy, and psychedelics may synergistically repair neural circuits, with therapies optimized using personalized computational models.
bioRxiv (Cold Spring Harbor Laboratory)
March 19, 2025
Giulio Ruffini, Edmundo Lopez-Sola, Raul P. Aristides et al.
8 citations
preprint
Cross-frequency coupling (CFC), where brain rhythms at different speeds interact, may be the mechanism the brain uses to compare sensory input with internal predictions. Using a laminar neural mass model, the authors show that two forms of CFC—signal-envelope coupling and envelope-envelope coupling—can implement hierarchical prediction-error computation and precision-weighting. In Alzheimer's disease, disruptions in fast-spiking interneurons lead to aberrant prediction errors: inflated early on, then attenuated. Serotonergic psychedelics reduce the influence of predictions, increasing prediction-error signals. These findings suggest that CFC across multiple timescales is a key computational mechanism supporting predictive coding, with disruptions central to certain disorders.
bioRxiv (Cold Spring Harbor Laboratory)
December 16, 2024
Jan C. Gendra, Edmundo Lopez-Sola, Francesca Castaldo et al.
7 citations
preprint
Classical serotonergic psychedelics may help treat neurodegenerative disorders like Alzheimer's disease by altering pathological brain dynamics. Using multimodal neuroimaging data from thirty subjects with mild to moderate Alzheimer's disease, a personalized whole-brain model based on a laminar neural mass framework simulated the effects of serotonin 2A receptor activation. Modulating the excitability of layer 5 pyramidal neurons reproduced hallmark EEG changes seen under psychedelics, including alpha power suppression and gamma power enhancement. These spectral shifts correlated strongly with regional serotonin 2A receptor distribution. Simulated EEG also showed increased complexity and entropy, suggesting restored network function, offering mechanistic insights into potential therapeutic effects in early Alzheimer's disease.
Entropy (Basel, Switzerland)
January 19, 2025
Giulio Ruffini, Francesca Castaldo, Jakub Vohryzek
6 citations
Tracking natural data forces an agent to mirror the symmetry properties of the generative world model, enforcing a hierarchical organization in the agent's neural network consistent with the manifold hypothesis. Using Lie pseudogroups to formalize invariance in natural data and drawing parallels to Noether's theorem, the study shows that data tracking constrains both the agent's constitutive parameters and dynamical repertoire. This bridges algorithmic information theory, symmetry, and dynamics, offering insights into neural correlates of agenthood and structured experience, as well as AI and brain model design.
bioRxiv
August 29, 2022
Giulio Ruffini, Giada Damiani, Diego Lozano-Soldevilla et al.
5 citations
preprint
Using fMRI data from fifteen people who took LSD or a placebo, researchers modeled brain dynamics with an Ising spin model to test whether psychedelics push the brain into a more disordered state. LSD increased the Ising temperature of brain activity, moving it further away from a critical point (the edge between order and disorder) into a more disordered, paramagnetic phase. This shift was accompanied by a decrease in interhemispheric connectivity, especially between corresponding regions in the two hemispheres. Algorithmic complexity of brain signals also increased with LSD. The findings suggest LSD loosens homotopic connections, driving the brain into a more flexible, complex state, consistent with theories that psychedelics increase neural entropy.
August 30, 2022
Giulio Ruffini, Edmundo Lopez-Sola, Jakub Vohryzek
3 citations
preprint
The unfolding argument challenges causal structure theories of consciousness by requiring that a theory specify which physical systems are conscious and which are not. This paper examines how the algorithmic information theory of consciousness (KT), which links subjective experience to the structure of a computational system, is affected by this argument. Considering computational hierarchies and limited physical resources, the authors introduce novel considerations that may extend the unfolding argument, suggesting that the argument's requirements may be more complex when applied to theories that rely on algorithmic information and computational structure.
Philosophy and the Mind Sciences
May 27, 2026
Edmundo Lopez-Sola, Roser Sanchez-Todo, Jakub Vohryzek et al.
1 citation
A computational framework rooted in algorithmic information theory, the algorithmic agent model, is used to investigate the phenomenon of pure awareness central to contemplative traditions. The framework proposes that agents build compressive models of the world, and structured experience arises from running such models. Pure awareness may correspond to experiences with minimal structure achieved through meditation, psychedelics, or other deconstructive practices, such as jhāna meditation. A key hypothesis is that the phenomenology of pure awareness arises from the agent's model of its own modeling process, and this recognition can occur alongside other phenomenal content, as in non-dual awareness. These ideas can be explored through whole-brain computational models based on predictive processing, grounded in meditation and psychedelic research.
bioRxiv (Cold Spring Harbor Laboratory)
December 22, 2024
Jakub Vohryzek, Morten L. Kringelbach, Edmundo Lopez-Sola et al.
1 citation
preprint
Both psychedelic states and reduced states of consciousness flatten the brain's functional hierarchy, yet their behavioral and phenomenological profiles differ. To resolve this paradox, researchers defined hierarchy by the brain's proximity to thermodynamic equilibrium and examined changes induced by three serotonergic psychedelics: psilocybin, LSD, and DMT. All three consistently reduced the functional hierarchy globally. Unlike loss of consciousness, psychedelics moved the brain toward equilibrium while increasing neural activity complexity, indicating a distinct mechanism involving altered configuration and differentiation of resting-state networks. This work demonstrates how statistical mechanics metrics can characterize different global brain states, advancing understanding of consciousness as an emergent collective process.
Zenodo (CERN European Organization for Nuclear Research)
June 28, 2026
Giulio Ruffini, Francesca Castaldo
Pharmacological neuroplastogens like psilocybin and LSD enhance neural plasticity by flattening high-level priors, allowing bottom-up prediction errors to remodel the brain's generative model. The same computational regime can be achieved non-pharmacologically through immersive algorithmic art held in a Goldilocks zone of compressibility. This approach is operationalized in a closed-loop digital therapeutic for adolescent depression. The argument extends to music, where harmonic tension serves as a prediction-error scaffold, and live performance with a chaos-harmony narrative arc. All three modalities sustain structured prediction error in the Goldilocks zone, transiently flatten the dynamical landscape, and push subjective phenomenology into territory typically associated with psychedelics like MDA, psilocybin, and LSD, as measured by altered states of consciousness and mystical experience instruments.
Zenodo (CERN European Organization for Nuclear Research)
June 28, 2026
Giulio Ruffini, Francesca Castaldo
Immersive algorithmic art may enhance neural plasticity through the same computational mechanism as psychedelics: sustained, structured prediction-error signaling. The brain's modeling engine generates predictions of sensory input; mismatches drive model updating via synaptic plasticity. Algorithmic art maximizes these errors while keeping stimuli in a compressible, emotionally rewarding "Goldilocks zone," creating a self-reinforcing loop of engagement, prediction error, plasticity, model updating, and positive valence. The hypothesis is formalized within Kolmogorov Theory, connected to the REBUS model, and supported by convergent evidence from psychedelic neuroimaging and predictive-coding electrophysiology. A translational pathway combining closed-loop EEG-driven algorithmic art with cognitive behavioral therapy for adolescent depression is outlined.
Zenodo (CERN European Organization for Nuclear Research)
June 28, 2026
Giulio Ruffini
A mathematical theory that assigns a continuous 'phenomenality score' to physical systems cannot produce a sharp yes/no classification of consciousness without a discontinuity somewhere. Any such scoring function that varies smoothly must take on intermediate values between zero and a positive threshold. Under certain smoothness conditions, the extreme scores of 0 and 1 are impossible to achieve. These results show that descriptive mathematical models of consciousness can identify boundaries but cannot explain why or how consciousness arises, consistent with the idea of an explanatory gap.
bioRxiv
September 25, 2025
Jakub Vohryzek, Edmundo Lopez-Sola, Winson F.z. Yang et al.
preprint
Advanced concentrative absorption meditation (jhāna) produces a shift in brain dynamics toward near-criticality, a state of heightened flexibility and integration. Using 7T fMRI and whole-brain modeling, the study found that later absorption states, considered minimal phenomenal experiences, show increased large-scale functional integration and a shift of the default mode network from a noise-driven regime to near-critical dynamics. This near-critical regime is interpreted as a form of openness, where constrained brain activity gives way to greater flexibility, correlating with broader attention and reduced narrative thought. The trajectory of these states is non-linear, with major reconfigurations at key meditative milestones.
Frontiers in systems neuroscience
January 1, 2019
Julia Moser, Siouar Bensaid, Eleni Kroupi et al.
Information-based metrics of neural activity can help quantify consciousness before and shortly after birth. Using fetal magnetoencephalography (fMEG) in human fetuses and neonates, researchers evaluated measures of entropy, compressibility, and fractality. Lempel-Ziv-Complexity (LZC) was the most practical metric because it is unequivocal and requires low computational effort, whereas fractality and entropy measures need more parameter adjustments. Comparing a brain-activity channel with a control channel in neonates showed significant differences in most complexity metrics, serving as proof of concept. For fetal data, results were less clear, possibly due to leftover maternal signals. The inconsistency across metrics highlights challenges in using complexity metrics as neural correlates of consciousness.