arXiv subjects

Giorgio Nicoletti

Publications and source records attributed to Giorgio Nicoletti.

At least 19 recordsLinked to original sources

arXiv2026

Universal thermokinetic decomposition of short-time information fluctuations

Biological, artificial, and physical systems dissipate energy to accurately transmit information. While tools of information theory have been used to characterize information-processing capabilities, how reliably this information is acquired along individual trajectories, and which aspects require a thermodynamic cost, is an open question. In this work, we focus on the stochastic predictability of an arbitrary Langevin dynamics, defined as the pointwise mutual information between the current and future states of a system. We show that the fluctuations of predictability obey a universal thermokinetic decomposition at short times, which reveals that information fluctuations are suppressed by energy dissipation and become stronger with increased dynamical activity. Remarkably, we find that the average predictability, i.e., the short-time mutual information, does not carry any dependence on the underlying thermodynamic and kinetic features. Thus, the role of dissipation at short times is not to enhance information, but to reduce its fluctuations. Such dissipative control is effective only when instantiated by nonlinear operations. Moreover, energy consumption governs short- and long-time precision in stochastic oscillators through structurally different mechanisms that can be independently tuned. Our decomposition offers a fundamental thermodynamic basis for understanding the reliability of information transmission in nonequilibrium systems, the constraints on precision in biological systems, and the design of energy-limited control strategies.

cond-mat.stat-mech↗

arXiv2026

Emergence of information interference in stochastic systems with non-diagonal noise and switching environments

Stochastic forces in natural systems are rarely isotropic. From hydrodynamically coupled colloids to chemical reaction networks, noise contributions are inherently correlated. Together with internal interactions and changing environments, they shape the dependencies between the degrees of freedom of real-world systems, as quantified by their mutual information. In this work, we focus on linearized stochastic systems with both non-diagonal noise matrices and stochastically switching environments. We study how their presence leads to the emergence of information interference, so that the total mutual information cannot be decomposed as the sum of the contributions from deterministic interactions, noise anisotropy, and environmental switching alone. We identify two distinct sources of information interference: a static term, arising from the simultaneous presence of deterministic coupling and noise anisotropy; and a dynamic term, emerging from the interplay between internal processes and environmental switches. We then apply this framework to different physical systems. In the presence of switching temperatures, the mutual information disentangles exactly into internal and environmental contributions. When the noise anisotropy arises instead from hydrodynamic interactions, we find that the presence of a shared fluid can either mask or enhance the information stemming from a non-conservative force depending on its degree of non-reciprocity. Finally, in a fuel-driven chemical reaction network, we show that information interference is controlled by the non-equilibrium driving. These results establish a general information-theoretic perspective on how anisotropic noise and environmental variability shape statistical dependencies in stochastic systems.

cond-mat.stat-mech↗

arXiv2026

Dispersal diversity buffers species vulnerability to local extinction

Predicting species persistence within ecological communities is a fundamental challenge for both empirical and theoretical ecology. Existing methods span from mechanistic models, whose parameters are difficult to estimate from data, to statistical tools whose context-specific parameters are less interpretable. Here, we present a general framework, grounded in the statistical physics of complex systems, that integrates the key processes governing species survival into a single measurable quantity: the competitive balance. This metric quantifies a focal species' vulnerability to competitive exclusion beyond what is captured by its abundance alone by incorporating the diversity of dispersal strategies and the structure of interspecific interactions within the community. Crucially, it can be inferred from spatial abundance data, thus circumventing the need to estimate species traits or dispersal parameters. Our results reveal that greater heterogeneity in dispersal strategies reduces vulnerability to competitive exclusion for a given abundance. Although we validate the framework using tropical and temperate forest data, it can be applied to a range of different ecosystems, providing a systemic and interpretable tool for assessing a context-dependent species vulnerability that accounts for its interactions with the entire community.

q-bio.PE↗

arXiv2026

Pulse desynchronization of neural populations by targeting the centroid of the limit cycle in phase space

The synchronized activity of neuronal populations can lead to pathological over-synchronization in conditions such as epilepsy and Parkinson disease. Such states can be desynchronized by brief electrical pulses. But when the underlying oscillating system is not known, as in most practical applications, to determine the specific times and intensities of pulses used for desynchronizaton is a difficult inverse problem. Here we propose a desynchronization scheme for neuronal models of bi-variate neural activity, with possible applications in the medical setting. Our main argument is the existence of a peculiar point in the phase space of the system, the centroid, that is both easy to calculate and robust under changes in the coupling constant. This important target point can be used in a control procedure because it lies in the region of minimal return times of the system.

q-bio.NC↗

arXiv2026

Habitat heterogeneity and dispersal network structure as drivers of metacommunity dynamics

Spatial structure and species interactions jointly shape the dynamics and biodiversity of ecological systems, yet most theoretical models either neglect spatial heterogeneity or sacrifice analytical tractability. Here, we provide a unified microscopic, mechanistic framework for deriving effective metapopulation and metacommunity models from individual-based ecological dynamics on arbitrary dispersal networks. The resulting coarse-grained description features an effective dispersal kernel that encodes both microscopic dynamical parameters and network topology. Based on this framework, we demonstrate exact analytical results for species persistence in both homogeneous and heterogeneous landscapes, including a generalization of the classical concept of metapopulation capacity to non-uniform local extinction rates. Incorporating stochasticity arising from finite carrying capacities, we obtain a reduced one-dimensional description that reveals universal finite-size scaling laws for extinction times and fluctuations. Extending the approach to multiple competing species, we prove that in homogeneous environments monodominance can be avoided only in a fine-tuned, marginally stable coexistence state, and that the classic metapopulation capacity gives only a necessary but not sufficient condition for persistence. We demonstrate that heterogeneous habitats can support stable coexistence, but only above a critical level of heterogeneity. Finally, we outline how additional ecological processes can be systematically incorporated within the same formalism. Together, these results provide analytical benchmarks and a general route for constructing spatially explicit ecological theories based on an interpretable underlying mechanistic foundation.

q-bio.PE↗

arXiv2025

Information-directed sampling for bandits: a primer

The Multi-Armed Bandit problem provides a fundamental framework for analyzing the tension between exploration and exploitation in sequential learning. This paper explores Information Directed Sampling (IDS) policies, a class of heuristics that balance immediate regret against information gain. We focus on the tractable environment of two-state Bernoulli bandits as a minimal model to rigorously compare heuristic strategies against the optimal policy. We extend the IDS framework to the discounted infinite-horizon setting by introducing a modified information measure and a tuning parameter to modulate the decision-making behavior. We examine two specific problem classes: symmetric bandits and the scenario involving one fair coin. In the symmetric case we show that IDS achieves bounded cumulative regret, whereas in the one-fair-coin scenario the IDS policy yields a regret that scales logarithmically with the horizon, in agreement with classical asymptotic lower bounds. This work serves as a pedagogical synthesis, aiming to bridge concepts from reinforcement learning and information theory for an audience of statistical physicists.

cs.LG↗

arXiv2025

Balancing information and dissipation with partially observed fluctuating signals

Biological systems sense and extract information from fluctuating signals while operating under energetic constraints and limited resolution. We introduce a general chemical model in which a sensor, coupled to a signaling pathway activated by hidden signals, can allosterically tune the production of a readout molecule. We propose viable strategies for the sensor to estimate, and eventually balance, information gathering on the hidden process and the associated dissipative cost relying solely on counting statistics of observed trajectories. We show that these strategies can be successfully implemented to adapt the readout production even with finite-time measurements and limited dynamic resolution, and remain effective in the presence of inhibitory regulatory mechanisms. Our study provides a plausible mechanism to actively balance information and dissipation, paving the way for an implementable design principle underpinning biological and biochemical adaptation.

cond-mat.stat-mech↗

arXiv2025

Plasticity-induced multistability on fast and slow timescales enables optimal information encoding and spontaneous sequence discrimination

Neural circuits exhibit remarkable computational flexibility, enabling adaptive responses to noisy and ever-changing environmental cues. A fundamental question in neuroscience concerns how a wide range of behaviors can emerge from a relatively limited set of underlying biological mechanisms. In particular, the interaction between activities of neuronal populations and plasticity modulation of synaptic connections may endow neural circuits with a variety of functional responses when coordinated over different characteristic timescales. Here, we develop an information-theoretic framework to quantitatively explore this idea. We consider a stochastic model for neural activities that incorporates the presence of a coupled dynamic plasticity and time-varying stimuli. We show that long-term plasticity modulations play the functional role of steering neural activities towards a regime of optimal information encoding. By constructing the associated phase diagram, we demonstrate that either Hebbian or anti-Hebbian plasticity may become optimal strategies depending on how the external input is projected to the target neural populations. Conversely, short-term plasticity enables the discrimination of temporal ordering in sequences of inputs by navigating the emergent multistable attractor landscape. By allowing a degree of variability in external stimuli, we also highlight the existence of an optimal variability for sequence discrimination at a given plasticity strength. In summary, the timescale of plasticity modulation shapes how inputs are represented in neural activities, thereby fundamentally altering the computational properties of the system. Our approach offers a unifying information-theoretic perspective of the role of plasticity, paving the way for a quantitative understanding of the emergence of complex computations in coupled neuronal-synaptic dynamics.

cond-mat.stat-mech↗

arXiv2025

Stochastic processes with multiple temporal scales: timescale separation and information

Complex systems are often characterized by the interplay of multiple interconnected dynamical processes operating across a range of temporal scales. This phenomenon is widespread in both biological and artificial scenarios, making it crucial to understand how such multiscale dynamics influence the overall functioning and behavior of these systems. Here, we present a general timescale separation approach that is valid for any set of stochastic differential equations coupled across multiple timescales. We show two alternative derivations, one based on an iterative procedure, and the other grounded on an a priori expansion in relevant small parameters associated with faster timescales. We provide an explicit expression for the conditional structure of the joint probability distribution of the whole set of processes that is solely determined by the multiscale interactions. This result has important consequences in determining how information is generated in the system and how it propagates across different timescales. To demonstrate that our findings are valid independently of the specific dynamical model, we test them against four different (linear and non-linear) dynamics. Then, we focus on the scenario in which two degrees of freedom are reciprocally coupled across their timescales. In this case, we show that the statistics of the fastest dynamics is captured by an effective self-interaction term in the slowest one, creating a regulatory loop. Finally, a connection with analogous previous results obtained for discrete-state systems with higher-order interactions is drawn, elucidating similarities and differences in the physical interpretation of the system. The presented framework might open the avenue for a clearer understanding of the interplay between the underlying multiscale structure and emergent functional behavior of biological and artificial systems.

cond-mat.stat-mech↗

arXiv2025

A novel metric for species vulnerability and coexistence in spatially-extended ecosystems

We develop a theoretical framework to understand the persistence and coexistence of competitive species in a spatially explicit metacommunity model with a heterogeneous dispersal kernel. Our analysis, based on methods from the physics of disordered systems and non-Gaussian dynamical mean field theory, reveals that species coexistence is governed by a single key parameter, which we term competitive balance. From competitive balance, we derive a novel metric to quantitatively assess the vulnerability of a species, showing that abundance alone is not sufficient to determine it. Rather, a species' vulnerability crucially depends on the state of the metacommunity as a whole. We test our theory by analyzing two distinct tropical forest datasets, finding excellent agreement with our theoretical predictions. A key step in our analysis is the introduction of a new quantity - the competitive score - which disentangles the abundance distribution and enables us to circumvent the challenge of estimating both the colonization kernel and the joint abundance distribution. Our findings provide novel and fundamental insights into the ecosystem-level trade-offs underlying macroecological patterns and introduce a robust approach for estimating extinction risks.

q-bio.PE↗

arXiv2024

Finite size scaling of survival statistics in metapopulation models

Spatial metapopulation models are fundamental to theoretical ecology, enabling to study how landscape structure influences global species dynamics. Traditional models, including recent generalizations, often rely on the deterministic limit of stochastic processes, assuming large population sizes. However, stochasticity - arising from dispersal events and population fluctuations - profoundly shapes ecological dynamics. In this work, we extend the classical metapopulation framework to account for finite populations, examining the impact of stochasticity on species persistence and dynamics. Specifically, we analyze how the limited capacity of local habitats influences survival, deriving analytical expressions for the finite-size scaling of the survival probability near the critical transition between survival and extinction. Crucially, we demonstrate that the deterministic metapopulation capacity plays a fundamental role in the statistics of survival probability and extinction time moments. These results provide a robust foundation for integrating demographic stochasticity into classical metapopulation models and their extensions.

q-bio.PE↗

arXiv2024

Multiscale nonlinear integration drives accurate encoding of input information

Biological and artificial systems encode information through several complex nonlinear operations, making their exact study a formidable challenge. These internal mechanisms often take place across multiple timescales and process external signals to enable functional output responses. In this work, we focus on two widely implemented paradigms: nonlinear summation, where signals are first processed independently and then combined; and nonlinear integration, where they are combined first and then processed. We study a general model where the input signal is propagated to an output unit through a processing layer via nonlinear activation functions. Further, we distinguish between the two cases of fast and slow processing timescales. We demonstrate that integration and fast-processing capabilities systematically enhance input-output mutual information over a wide range of parameters and system sizes, while simultaneously enabling tunable input discrimination. Moreover, we reveal that high-dimensional embeddings and low-dimensional projections emerge naturally as optimal competing strategies. Our results uncover the foundational features of nonlinear information processing with profound implications for both biological and artificial systems.

cond-mat.stat-mech↗

arXiv2024

Information interference driven by environmental activity

Real-world systems are shaped by both their complex internal interactions and the changes in their noisy environments. In this work, we study how a shared active bath affects the statistical dependencies between two interacting Brownian particles by evaluating their mutual information. We decompose the mutual information into three terms: information stemming from the internal interactions between the particles; information induced by the shared bath, which encodes environmental changes; a term describing information interference that quantifies how the combined presence of both internal interactions and environment either masks (destructive interference) or boosts (constructive interference) information. By studying exactly the case of linear interactions, we find that the sign of information interference depends solely on that of the internal coupling. However, when internal interactions are described by a nonlinear activation function, we show that both constructive and destructive interference appear depending on the interplay between the timescale of the active environment, the internal interactions, and the environmental coupling. Finally, we show that our results generalize to hierarchical systems where asymmetric couplings to the environment mimic the scenario where the active bath is only partially accessible to one particle. This setting allows us to quantify how this asymmetry drives information interference. Our work underscores how information and functional relationships in complex multi-scale systems are fundamentally shaped by the environmental context.

cond-mat.stat-mech↗

arXiv2024

Excitation-inhibition balance controls information encoding in neural populations

Understanding how the complex connectivity structure of the brain shapes its information-processing capabilities is a long-standing question. By focusing on a paradigmatic architecture, we study how the neural activity of excitatory and inhibitory populations encodes information on external signals. We show that at long times information is maximized at the edge of stability, where inhibition balances excitation, both in linear and nonlinear regimes. In the presence of multiple external signals, this maximum corresponds to the entropy of the input dynamics. By analyzing the case of a prolonged stimulus, we find that stronger inhibition is instead needed to maximize the instantaneous sensitivity, revealing an intrinsic trade-off between short-time responses and long-time accuracy. In agreement with recent experimental findings, our results pave the way for a deeper information-theoretic understanding of how the balance between excitation and inhibitions controls optimal information-processing in neural populations.

cond-mat.stat-mech↗

arXiv2024

Information propagation in Gaussian processes on multilayer networks

Complex systems with multiple processes evolving on different temporal scales are naturally described by multilayer networks, where each layer represents a different timescale. In this work, we show how the multilayer structure shapes the generation and propagation of information between layers. We derive a general decomposition of the multilayer probability for continuous stochastic processes described by Fokker-Planck operators. In particular, we focus on Gaussian processes, for which this solution can be obtained analytically. By explicitly computing the mutual information between the layers, we derive the fundamental principles that govern how information is propagated by the topology of the multilayer network. In particular, we unravel how edges between nodes in different layers affect their functional couplings. We find that interactions from fast to slow layers alone do not generate information, leaving the layers statistically independent even if they affect their dynamical evolution. On the other hand, interactions from slow to fast nodes lead to non-zero mutual information, which can then be propagated along specific paths of interactions between layers. We employ our results to study the interplay between information and instability, identifying the critical layers that drive information when pushed to the edge of stability. Our work generalizes previous results obtained in the context of discrete stochastic processes, allowing us to understand how the multilayer nature of complex systems affects their functional structure.

cond-mat.stat-mech↗

arXiv2024

Spatially disordered environments stabilize competitive metacommunities

Metapopulation models have been instrumental in demonstrating the ecological impact of landscape structure on the survival of a focal species in complex environments. However, extensions to multiple species with arbitrary dispersal networks often rely on phenomenological assumptions limiting their scope. Here, we develop a multilayer network model of competitive dispersing metacommunities to investigate how spatially structured environments impact species coexistence and ecosystem stability. We show that homogeneous environments always lead to monodominance unless all species' fitness parameters are in an exact trade-off. However, this precise fine-tuning does not guarantee coexistence in generic heterogeneous environments. By introducing general spatial disorder in the model, we solve it exactly in the mean-field limit, finding that stable coexistence becomes possible in the presence of strong disorder. Crucially, coexistence is supported by the spontaneous localization of species through the emergence of ecological niches. Our results remain qualitatively valid in arbitrary dispersal networks, where topological features can improve species coexistence. Finally, we employ our model to study how correlated disorder promotes spatial ecological patterns in realistic terrestrial and riverine landscapes. Our work provides a novel framework to understand how landscape structure enables coexistence in metacommunities by acting as the substrate for ecological interactions.

q-bio.PE↗

arXiv2024

Tuning transduction from hidden observables to optimize information harvesting

Biological and living organisms sense and process information from their surroundings, typically having access only to a subset of external observables for a limited amount of time. In this work, we uncover how biological systems can exploit these accessible degrees of freedom (DOFs) to transduce information from the inaccessible ones with a limited energy budget. We find that optimal transduction strategies may boost information harvesting over the ideal case in which all DOFs are known, even when only finite-time trajectories are observed, at the price of higher dissipation. We apply our results to red blood cells, inferring the implemented transduction strategy from membrane flickering data and shedding light on the connection between mechanical stress and transduction efficiency. Our framework offers novel insights into the adaptive strategies of biological systems under non-equilibrium conditions.

cond-mat.stat-mech↗

arXiv2023

Information propagation in multilayer systems with higher-order interactions across timescales

Complex systems are characterized by multiple spatial and temporal scales. A natural framework to capture their multiscale nature is that of multilayer networks, where different layers represent distinct physical processes that often regulate each other indirectly. We model these regulatory mechanisms through triadic higher-order interactions between nodes and edges. In this work, we focus on how the different timescales associated with each layer impact their effective couplings in terms of their mutual information. We unravel the general principles governing how such information propagates across the multiscale structure, and apply them to study archetypal examples of biological signaling networks and effective environmental dependencies in stochastic processes. Our framework generalizes to any dynamics on multilayer networks, paving the way for a deeper understanding of how the multiscale nature of real-world systems shapes their information content and complexity.

cond-mat.stat-mech↗