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Researcher profile

Arturo Tozzi

Arturo Tozzi contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Other Quantitative BiologyBiological PhysicsNeurons and Cognitionphysics.soc-phQuantitative Methods

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Published work

3 published item(s)

preprint2026arXiv

From local defects to shear-organized biofilms in tonsillar crypts via computational simulations

Biofilms in human tonsillar crypts show long term persistence with episodic dispersal that current biochemical and microbiological descriptions do not fully explain, particularly with respect to spatial localization. We introduce a biophysical framework in which tonsillar biofilm dynamics arise from the interaction between two mechanical phenomena: a Kosterlitz Thouless type defect nucleation process and a Kelvin Helmholtz type shear driven interfacial instability. Crypt geometry is modeled as a confined, heterogeneous environment that promotes mechanically persistent surface defects generated by growth induced compression. Tangential shear associated with breathing and swallowing selectively amplifies these defects, producing organized surface deformations. Numerical simulations show that only the coexistence of both mechanisms yields localized, propagating, and persistent interface structures, whereas their absence leads to diffuse, unstructured dynamics.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Internally triggered retrospective learning in neural networks

Learning in artificial neural networks usually relies on continuous, externally driven weight updates, in which parameters are modified at every step in response to incoming data, error signals or reward feedback. In this setting, routine and informative inputs contribute similarly to parameter adjustment. We introduce a learning approach in which parameter updates are governed by internally generated events arising from the network own representational dynamics. During ongoing activity, synaptic interactions are accumulated as latent traces encoding recent coactivation patterns, without immediately modifying the underlying parameters. In parallel, an internal predictive process estimates the evolving latent state, while a scalar measure of discrepancy between predicted and observed states is continuously computed. When discrepancy exceeds an adaptive threshold derived from recent error statistics, a learning event is triggered, inducing a retrospective update selectively integrating past activity into the current configuration. We performed simulations using a minimal neural network exposed to structured sequential inputs with transient perturbations. We found that learning occurs through sparse, temporally localized events associated with increases in prediction error, leading to stepwise changes in synaptic efficacy and discrete transitions in latent state organization. By selectively reorganizing parameters in response to internally detected discrepancies, our episodic updating may reduce unnecessary parameter drift while preserving informative patterns. Potential applications include systems requiring selective adaptation to rare or informative inputs such as physiological, industrial or environmental monitoring, edge computing under limited energy budgets, autonomous systems operating in dynamic conditions and sequential computational data processing.

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preprint2026arXiv

Low-Dimensional Interaction Spaces Impose Geometric Constraints On Collective Organization

Collective organization in physical, biophysical, and biological systems often emerges from many weak, local interactions, yet the resulting global structures display striking regularities and apparent limits in diversity. Existing theoretical approaches typically emphasize specific mechanisms, detailed dynamics, or energetic optimization, making it difficult to identify constraints that are independent of microscopic realization. Here we develop a general theoretical framework showing that, when effective interactions among system components compress into a low-dimensional interaction space, global organization is governed by geometric constraints rather than detailed dynamics. We formalize interaction spaces as metric manifolds derived from coarse-grained effective couplings and show that low interaction dimensionality imposes upper bounds on the number, separability, and robustness of distinct collective organizations. These results yield impossibility statements: many conceivable macroscopic organizations are excluded a priori, even when locally compatible interactions exist. The framework applies across equilibrium and nonequilibrium systems without assuming specific symmetries or conservation laws. By shifting the explanatory focus from generative mechanisms to structural constraints, this work establishes a general, geometry-based perspective on collective organization.

0 citations0 reviewsNo code linkedOpen access