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.
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.