http://sedici.unlp.edu.ar:80/handle/10915/86776 Thu, 12 Nov 2026 07:54:19 GMT 2026-11-12T07:54:19Z http://sedici.unlp.edu.ar:80/handle/10915/198459 Articulo Far East Journal of Dynamical Systems; vol. 11, no. 1 In this article we obtain bounds for large deviations processes in dynamical systems, in which non-additive sequences are considered. More specifically, for sequences [equation] and dynamical maps [equation] such that [equation] converges uniformly we estimate the growing rate of [equation] where n is a finite Borel measure on X and I is a real interval. We consider also a particular case in which the process description easily follows from Ellis theorem. Mon, 23 Feb 2009 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/198459 2009-02-23T00:00:00Z In this article we obtain bounds for large deviations processes in dynamical systems, in which non-additive sequences are considered. More specifically, for sequences [equation] and dynamical maps [equation] such that [equation] converges uniformly we estimate the growing rate of [equation] where n is a finite Borel measure on X and I is a real interval. We consider also a particular case in which the process description easily follows from Ellis theorem. http://sedici.unlp.edu.ar:80/handle/10915/197038 Articulo iScience; vol. 28, no. 11 During tissue development and regeneration, cells interpret and exert mechanical forces that are challenging to measure in vivo. Stress inference algorithms have thus emerged as powerful tools to estimate tissue stresses. Yet, effectively incorporating tissue dynamics into these algorithms remains elusive. Here, we introduce ForSys, a Python-based software that infers intercellular stresses and intracellular pressures from time- lapse microscopy. After validation, we applied ForSys to the migrating zebrafish lateral-line primordium, revealing increased stress during the cell rounding that precedes mitosis and accurately predicting the onset of epithelial rosettogenesis. We further used ForSys to study neuromast development and uncovered mechanical asymmetries linked to cell type-specific adhesion. The software performs both static and dynamic stress inference, supports command-line use, scripting, and a user-friendly graphical interface within Fiji, and accepts segmentation inputs from EPySeg and Cellpose. This versatility of ForSys enables the analysis of spatiotemporal patterns of mechanical forces during tissue morphogenesis in vivo. Wed, 01 Jan 2025 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197038 2025-01-01T00:00:00Z During tissue development and regeneration, cells interpret and exert mechanical forces that are challenging to measure in vivo. Stress inference algorithms have thus emerged as powerful tools to estimate tissue stresses. Yet, effectively incorporating tissue dynamics into these algorithms remains elusive. Here, we introduce ForSys, a Python-based software that infers intercellular stresses and intracellular pressures from time- lapse microscopy. After validation, we applied ForSys to the migrating zebrafish lateral-line primordium, revealing increased stress during the cell rounding that precedes mitosis and accurately predicting the onset of epithelial rosettogenesis. We further used ForSys to study neuromast development and uncovered mechanical asymmetries linked to cell type-specific adhesion. The software performs both static and dynamic stress inference, supports command-line use, scripting, and a user-friendly graphical interface within Fiji, and accepts segmentation inputs from EPySeg and Cellpose. This versatility of ForSys enables the analysis of spatiotemporal patterns of mechanical forces during tissue morphogenesis in vivo. http://sedici.unlp.edu.ar:80/handle/10915/196330 Articulo Journal of Theoretical Biology; vol. 623 Biological systems are never in equilibrium, yet they maintain stability in the face of continuous external disturbances. A prime example of this is organ regeneration, during which organs are reliably rebuilt through controlled cellular proliferation. In this study, we employ a cell-based computational modelling approach to investigate the proliferative response of an organ after injury. We developed a minimal two-dimensional Cellular Potts Model (CPM) using empirical data from regenerating neuromasts in larval zebrafish. Remarkably, the CPM both qualitatively and quantitatively recapitulates the regenerative response of neuromasts following laser-mediated cell ablation. Assuming that cell proliferation is locally regulated by a delayed switch, we discovered that mitotic activity ceases once the type-dependent number of neighbouring cells exceeds a deterministic critical threshold. An intriguing corollary of our findings is that a local negative feedback loop among identical cells may represent a general mechanism underlying organ-level proportional homeostasis. Fri, 01 May 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/196330 2026-05-01T00:00:00Z Biological systems are never in equilibrium, yet they maintain stability in the face of continuous external disturbances. A prime example of this is organ regeneration, during which organs are reliably rebuilt through controlled cellular proliferation. In this study, we employ a cell-based computational modelling approach to investigate the proliferative response of an organ after injury. We developed a minimal two-dimensional Cellular Potts Model (CPM) using empirical data from regenerating neuromasts in larval zebrafish. Remarkably, the CPM both qualitatively and quantitatively recapitulates the regenerative response of neuromasts following laser-mediated cell ablation. Assuming that cell proliferation is locally regulated by a delayed switch, we discovered that mitotic activity ceases once the type-dependent number of neighbouring cells exceeds a deterministic critical threshold. An intriguing corollary of our findings is that a local negative feedback loop among identical cells may represent a general mechanism underlying organ-level proportional homeostasis. http://sedici.unlp.edu.ar:80/handle/10915/196219 Articulo Physica A: Statistical Mechanics and its Applications; vol. 692 We investigate a minimal chase-and-escape model on a two-dimensional square lattice with randomly distributed static obstacles, focusing on how geometric disorder controls collective pursuit dynamics. Chasers and escapers move according to short-range sensing rules, while the density of obstacles tunes the connectivity of the accessible space. Using a combination of geometric analysis, dynamical observables, survival statistics, and transport characterization, we establish a direct link between lattice connectivity and pursuit efficiency. A Breadth-First Search analysis reveals that obstacle-induced fragmentation leads to a progressive loss of accessibility before the percolation threshold, defining the effective initial conditions for the dynamics. The trapping time and capture cost exhibit a non-monotonic dependence on obstacle density, reflecting a competition between path elongation in connected environments and geometric confinement near the percolation threshold. Survival analysis shows that the decay of the escaper population follows a Weibull form, with characteristic time and shape parameters displaying clear crossovers as a function of obstacle density, signaling the coexistence of cooperative capture and confinement-dominated trapping. Transport properties, quantified through the mean-squared displacement exponent, further support this picture, revealing sub- diffusive dynamics and a convergence toward a geometry-controlled regime near percolation. Overall, our results demonstrate that chase-and-escape dynamics in disordered environments are governed by a geometry-driven crossover, where percolation and connectivity act as unifying control parameters for spatial, temporal, and collective behavior. Mon, 01 Jun 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/196219 2026-06-01T00:00:00Z We investigate a minimal chase-and-escape model on a two-dimensional square lattice with randomly distributed static obstacles, focusing on how geometric disorder controls collective pursuit dynamics. Chasers and escapers move according to short-range sensing rules, while the density of obstacles tunes the connectivity of the accessible space. Using a combination of geometric analysis, dynamical observables, survival statistics, and transport characterization, we establish a direct link between lattice connectivity and pursuit efficiency. A Breadth-First Search analysis reveals that obstacle-induced fragmentation leads to a progressive loss of accessibility before the percolation threshold, defining the effective initial conditions for the dynamics. The trapping time and capture cost exhibit a non-monotonic dependence on obstacle density, reflecting a competition between path elongation in connected environments and geometric confinement near the percolation threshold. Survival analysis shows that the decay of the escaper population follows a Weibull form, with characteristic time and shape parameters displaying clear crossovers as a function of obstacle density, signaling the coexistence of cooperative capture and confinement-dominated trapping. Transport properties, quantified through the mean-squared displacement exponent, further support this picture, revealing sub- diffusive dynamics and a convergence toward a geometry-controlled regime near percolation. Overall, our results demonstrate that chase-and-escape dynamics in disordered environments are governed by a geometry-driven crossover, where percolation and connectivity act as unifying control parameters for spatial, temporal, and collective behavior. http://sedici.unlp.edu.ar:80/handle/10915/194182 Articulo Simplified model of a spinal functional unit to determinesurgical parameters XLI Congreso Argentino de Mecánica Computacional (Buenos Aires, 11 al 14 de noviembre de 2025); Mecánica Computacional; vol. 42, no. 19 Se presenta un modelo computacional simplificado de la unidad funcional espinal L4-L5 para explorar parámetros relevantes en cirugías mínimamente invasivas como la discoplastía percutánea ósea. Usando software libre (Gmsh, Dolfinx, Paraview), se pueden personalizar variaciones en la altura del disco y el volumen de cemento óseo. Los resultados incluyen verificación con un caso clínico, esti- mación del volumen inyectado y análisis de tensiones pre y postoperatorias. El trabajo busca aportar a la optimización de parámetros quirúrgicos.; A simplified computational model of the L4-L5 functional spinal unit is presented to explore relevant parameters in minimally invasive surgeries such as percutaneous cement discoplasty. Using open-source software (Gmsh, Dolfinx, Paraview), variations in disc height and bone cement volume can be customized. The results include verification with a clinical case, estimation of injected cement volume, and analysis of pre- and postoperative stresses. This work aims to contribute to the optimization of surgical parameters. Wed, 01 Jan 2025 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/194182 2025-01-01T00:00:00Z Se presenta un modelo computacional simplificado de la unidad funcional espinal L4-L5 para explorar parámetros relevantes en cirugías mínimamente invasivas como la discoplastía percutánea ósea. Usando software libre (Gmsh, Dolfinx, Paraview), se pueden personalizar variaciones en la altura del disco y el volumen de cemento óseo. Los resultados incluyen verificación con un caso clínico, esti- mación del volumen inyectado y análisis de tensiones pre y postoperatorias. El trabajo busca aportar a la optimización de parámetros quirúrgicos. A simplified computational model of the L4-L5 functional spinal unit is presented to explore relevant parameters in minimally invasive surgeries such as percutaneous cement discoplasty. Using open-source software (Gmsh, Dolfinx, Paraview), variations in disc height and bone cement volume can be customized. The results include verification with a clinical case, estimation of injected cement volume, and analysis of pre- and postoperative stresses. This work aims to contribute to the optimization of surgical parameters. http://sedici.unlp.edu.ar:80/handle/10915/167561 Articulo Molecules; vol. 29, no. 7 A monolayer consisting of two types of particles, with energetically favored alternating stripes of the two components, is studied by Monte Carlo simulations and within a mesoscopic theory. We consider a triangular lattice model and assume short-range attraction and long-range repulsion between particles of the same kind, as well as short-range repulsion and long-range attraction for the cross-interaction. The structural evolution of the model upon increasing temperature is studied for equal chemical potentials of the two species. We determine the structure factor, the chemical potential– density isotherms, the specific heat, and the compressibility, and show how these thermodynamic functions are associated with the spontaneous formation of stripes with varying degrees of order. Mon, 01 Jan 2024 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/167561 2024-01-01T00:00:00Z A monolayer consisting of two types of particles, with energetically favored alternating stripes of the two components, is studied by Monte Carlo simulations and within a mesoscopic theory. We consider a triangular lattice model and assume short-range attraction and long-range repulsion between particles of the same kind, as well as short-range repulsion and long-range attraction for the cross-interaction. The structural evolution of the model upon increasing temperature is studied for equal chemical potentials of the two species. We determine the structure factor, the chemical potential– density isotherms, the specific heat, and the compressibility, and show how these thermodynamic functions are associated with the spontaneous formation of stripes with varying degrees of order. http://sedici.unlp.edu.ar:80/handle/10915/160308 Articulo Physica A; vol. 471 Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from -Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter . We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs. Sun, 01 Jan 2017 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/160308 2017-01-01T00:00:00Z Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from -Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter . We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs. http://sedici.unlp.edu.ar:80/handle/10915/160307 Articulo Physica A; vol. 511 Electroencephalograms reflect the electrical activity of the brain, which can be considered ruled by a chaotic nonlinear dynamics. We consider human electroencephalogram recordings during different motor type activities, and when imagining that they perform this activity. We characterize the different dynamics of the cortex according to distinct motor and imagined movement tasks using an information theory approach and a wavelet decomposition. More specifically, we use the entropy-complexity plane H × C in combination with the wavelet decomposition to precisely quantify the dynamics of the neuronal activity showing that the current theoretical framework allows us to distinguish realized and imagined tasks within the cortex. Mon, 01 Jan 2018 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/160307 2018-01-01T00:00:00Z Electroencephalograms reflect the electrical activity of the brain, which can be considered ruled by a chaotic nonlinear dynamics. We consider human electroencephalogram recordings during different motor type activities, and when imagining that they perform this activity. We characterize the different dynamics of the cortex according to distinct motor and imagined movement tasks using an information theory approach and a wavelet decomposition. More specifically, we use the entropy-complexity plane H × C in combination with the wavelet decomposition to precisely quantify the dynamics of the neuronal activity showing that the current theoretical framework allows us to distinguish realized and imagined tasks within the cortex. http://sedici.unlp.edu.ar:80/handle/10915/160305 Articulo Physical Review E; vol. 94, no. 4 Spike correlations among neurons are widely encountered in the brain. Although models accounting for pairwise interactions have proved able to capture some of the most important features of population activity at the level of the retina, the evidence shows that pairwise neuronal correlation analysis does not resolve cooperative population dynamics by itself. By means of a series expansion for short time scales of the mutual information conveyed by a population of neurons, the information transmission can be broken down into firing rate and correlational components. In a proposed extension of this framework, we investigate the information components considering both second- and higher-order correlations.We showthat the existence of a mixed stimulus-dependent correlation term defines a new scenario for the interplay between pairwise and higher-than-pairwise interactions in noise and signal correlations that would lead either to redundancy or synergy in the information-theoretic sense. Fri, 01 Jan 2016 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/160305 2016-01-01T00:00:00Z Spike correlations among neurons are widely encountered in the brain. Although models accounting for pairwise interactions have proved able to capture some of the most important features of population activity at the level of the retina, the evidence shows that pairwise neuronal correlation analysis does not resolve cooperative population dynamics by itself. By means of a series expansion for short time scales of the mutual information conveyed by a population of neurons, the information transmission can be broken down into firing rate and correlational components. In a proposed extension of this framework, we investigate the information components considering both second- and higher-order correlations.We showthat the existence of a mixed stimulus-dependent correlation term defines a new scenario for the interplay between pairwise and higher-than-pairwise interactions in noise and signal correlations that would lead either to redundancy or synergy in the information-theoretic sense. http://sedici.unlp.edu.ar:80/handle/10915/160303 Articulo Physica A; vol. 421 Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how higher-order inputs statistics can shape beyond pairwise spike correlations and affect information coding in the brain, we consider a neuronal pool where each neuron fires stochastically. We develop a simple mathematically tractable model that makes it feasible to account for higher-order spike correlations in a neuronal pool with highly interconnected common inputs beyond second order statistics. In our model, correlations between neurons appear from q-Gaussian inputs into threshold neurons. The approach constitutes the natural extension of the Dichotomized Gaussian model, where the inputs to the model are just Gaussian distributed and therefore have no input interactions beyond second order. We obtain an exact analytical expression for the joint distribution of firing, quantifying the degree of higher-order spike correlations, truly emphasizing the functional aspects of higher-order statistics, as we account for beyond second order inputs correlations seen by each neuron within the pool. We determine how higherorder correlations depend on the interaction structure of the input, showing that the joint distribution of firing is skewed as the parameter q increases inducing larger excursions of synchronized spikes. We show how input nonlinearities can shape higher-order correlations and enhance coding performance by neural populations. Thu, 01 Jan 2015 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/160303 2015-01-01T00:00:00Z Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how higher-order inputs statistics can shape beyond pairwise spike correlations and affect information coding in the brain, we consider a neuronal pool where each neuron fires stochastically. We develop a simple mathematically tractable model that makes it feasible to account for higher-order spike correlations in a neuronal pool with highly interconnected common inputs beyond second order statistics. In our model, correlations between neurons appear from q-Gaussian inputs into threshold neurons. The approach constitutes the natural extension of the Dichotomized Gaussian model, where the inputs to the model are just Gaussian distributed and therefore have no input interactions beyond second order. We obtain an exact analytical expression for the joint distribution of firing, quantifying the degree of higher-order spike correlations, truly emphasizing the functional aspects of higher-order statistics, as we account for beyond second order inputs correlations seen by each neuron within the pool. We determine how higherorder correlations depend on the interaction structure of the input, showing that the joint distribution of firing is skewed as the parameter q increases inducing larger excursions of synchronized spikes. We show how input nonlinearities can shape higher-order correlations and enhance coding performance by neural populations. http://sedici.unlp.edu.ar:80/handle/10915/160299 Articulo International Journal of Neural Systems; vol. 27, no. 02 Whether premotor/motor neurons encode information in terms of spiking frequency or by their relative time of firing, which may display synchronization, is still undetermined. To address this issue, we used an information theory approach to analyze neuronal responses recorded in the premotor (area F5) and primary motor (area F1) cortices of macaque monkeys under four different conditions of visual feedback during hand grasping. To evaluate the sensitivity of spike timing correlation between single neurons, we investigated the stimulus dependent synchronization in our population of pairs. We first investigated the degree of correlation of trial-to-trial fluctuations in response strength between neighboring neurons for each condition, and second estimated the stimulus dependent synchronization by means of an information theoretical approach. We compared the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. The information transmission across pairs of cells in the primary motor cortex seems largely independent, whereas information transmission across pairs of premotor neurons is summed superlinearly. The brain could take advantage of both the accuracy provided by the independency of F1 and the synergy allowed by the superlinear information population coding in F5, distinguishing thus the generalizing role of F5 Sun, 01 Jan 2017 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/160299 2017-01-01T00:00:00Z Whether premotor/motor neurons encode information in terms of spiking frequency or by their relative time of firing, which may display synchronization, is still undetermined. To address this issue, we used an information theory approach to analyze neuronal responses recorded in the premotor (area F5) and primary motor (area F1) cortices of macaque monkeys under four different conditions of visual feedback during hand grasping. To evaluate the sensitivity of spike timing correlation between single neurons, we investigated the stimulus dependent synchronization in our population of pairs. We first investigated the degree of correlation of trial-to-trial fluctuations in response strength between neighboring neurons for each condition, and second estimated the stimulus dependent synchronization by means of an information theoretical approach. We compared the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. The information transmission across pairs of cells in the primary motor cortex seems largely independent, whereas information transmission across pairs of premotor neurons is summed superlinearly. The brain could take advantage of both the accuracy provided by the independency of F1 and the synergy allowed by the superlinear information population coding in F5, distinguishing thus the generalizing role of F5 http://sedici.unlp.edu.ar:80/handle/10915/159864 Articulo Powder Technology; vol. 428 We investigate the effect of a bevelled (or slanted) outlet on the discharge rate of mono-sized spheres from a quasi-two-dimensional silo, using the discrete element method. In contrast to hopper discharges, where the bevelling is across the entire base of the container, we study a bevelled opening that is significantly smaller than the silo width and in which the slanting is limited to half a sphere diameter at the boundary of the outlet. We show that the bevelling increases the flow rate comparably to the inclination in hopper walls. Using Beverloo’s model, we relate this increase in rate to what we define as the ‘effective opening’ of the silo and analyse the velocity profiles associated with the discharges. We show that different openings, having effectively the same discharge rates, give rise to distinctly different internal dynamics in the silo. These results have the potential to aid industrial processes by fine-tuning and improving control of silo discharges, with a minimal impact on silo design, thus significantly reducing production and handling costs. Sun, 01 Jan 2023 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/159864 2023-01-01T00:00:00Z We investigate the effect of a bevelled (or slanted) outlet on the discharge rate of mono-sized spheres from a quasi-two-dimensional silo, using the discrete element method. In contrast to hopper discharges, where the bevelling is across the entire base of the container, we study a bevelled opening that is significantly smaller than the silo width and in which the slanting is limited to half a sphere diameter at the boundary of the outlet. We show that the bevelling increases the flow rate comparably to the inclination in hopper walls. Using Beverloo’s model, we relate this increase in rate to what we define as the ‘effective opening’ of the silo and analyse the velocity profiles associated with the discharges. We show that different openings, having effectively the same discharge rates, give rise to distinctly different internal dynamics in the silo. These results have the potential to aid industrial processes by fine-tuning and improving control of silo discharges, with a minimal impact on silo design, thus significantly reducing production and handling costs. http://sedici.unlp.edu.ar:80/handle/10915/159775 Articulo Ecological Indicators; vol. 146 We analyse population dynamics in Barro Colorado Island (Panama) using census data of a 50 ha forest plot spanning 35 years, and address the question whether this community is in a stationary state. Individual species abundances show large fluctuations, but assessing stationariety requires discriminating random fluctuations from actual trends. This requires evaluating mean quantities as well as the structure (i.e. the correlations) of the fluctuations around this mean. We argue that a species average is the best surrogate for the theoretically required but unfeasible history average. We define the overlap, a species-averaged measure of composition similarity, which reveals that the BCI population dynamics is stationary but not static, displaying fluctuations with a characteristic time of around 15 years, two orders of magnitude less than previously estimated. Sun, 01 Jan 2023 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/159775 2023-01-01T00:00:00Z We analyse population dynamics in Barro Colorado Island (Panama) using census data of a 50 ha forest plot spanning 35 years, and address the question whether this community is in a stationary state. Individual species abundances show large fluctuations, but assessing stationariety requires discriminating random fluctuations from actual trends. This requires evaluating mean quantities as well as the structure (i.e. the correlations) of the fluctuations around this mean. We argue that a species average is the best surrogate for the theoretically required but unfeasible history average. We define the overlap, a species-averaged measure of composition similarity, which reveals that the BCI population dynamics is stationary but not static, displaying fluctuations with a characteristic time of around 15 years, two orders of magnitude less than previously estimated. http://sedici.unlp.edu.ar:80/handle/10915/159774 Articulo Ecological Indicators; vol. 125 The vulnerability of species richness to several factors like, climate change, habitat fragmentation, resource exploitation, etc., poses a challenge to conservation biologists and agencies working to sustain the ecosystem services. Hence, there is a clear need for early warning indicators of species loss generated from empirical data. The tree community of the long-term 50-hectare plot on Barro Colorado Island (BCI), Panama, is one of the most intensively studied in the world. This plot was established in 1981 and fully censused in 1982 then every 5 years from 1985 through 2015. This extensive dataset reveals that some tree species suffered steep population declines. Here we propose an early warning indicator of such tree population crashes and test it against the BCI dataset. The spatial covariance matrices, Cij, of the 20 most abundant tree species in BCI allow us to compute, via MaxEnt, the effective interaction matrices, Jij, among these species for the eight censuses available from 1982 to 2015. For each species i and each census c, the absolute value of the intraspecific competition coefficients Jii(c) are much larger than those of the interspecific interaction coefficients Jij(c) with i ∕= j. We show that this result can be derived from a similar empirical relationship observed for the matrices Cii(c). Our main finding is that for those tree species that suffered steep population declines (of at least 50%), across the eight tree censuses, the drop of Jii is always steeper and occurs before the drop of the corresponding species abundance Ni. Indeed, such sharp declines in Jii occur between 5 and 15 years in advance than comparable declines for Ni, and thus they serve as early warnings of impending population busts. Furthermore, this drop of Jii is linked to the anomalous variance, which is a known early warning of incoming catastrophic shifts. Fri, 01 Jan 2021 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/159774 2021-01-01T00:00:00Z The vulnerability of species richness to several factors like, climate change, habitat fragmentation, resource exploitation, etc., poses a challenge to conservation biologists and agencies working to sustain the ecosystem services. Hence, there is a clear need for early warning indicators of species loss generated from empirical data. The tree community of the long-term 50-hectare plot on Barro Colorado Island (BCI), Panama, is one of the most intensively studied in the world. This plot was established in 1981 and fully censused in 1982 then every 5 years from 1985 through 2015. This extensive dataset reveals that some tree species suffered steep population declines. Here we propose an early warning indicator of such tree population crashes and test it against the BCI dataset. The spatial covariance matrices, Cij, of the 20 most abundant tree species in BCI allow us to compute, via MaxEnt, the effective interaction matrices, Jij, among these species for the eight censuses available from 1982 to 2015. For each species i and each census c, the absolute value of the intraspecific competition coefficients Jii(c) are much larger than those of the interspecific interaction coefficients Jij(c) with i ∕= j. We show that this result can be derived from a similar empirical relationship observed for the matrices Cii(c). Our main finding is that for those tree species that suffered steep population declines (of at least 50%), across the eight tree censuses, the drop of Jii is always steeper and occurs before the drop of the corresponding species abundance Ni. Indeed, such sharp declines in Jii occur between 5 and 15 years in advance than comparable declines for Ni, and thus they serve as early warnings of impending population busts. Furthermore, this drop of Jii is linked to the anomalous variance, which is a known early warning of incoming catastrophic shifts. http://sedici.unlp.edu.ar:80/handle/10915/154732 Articulo Physica A; vol. 486 To understand how single neurons process sensory information, it is necessary to develop suitable stochastic models to describe the response variability of the recorded spike trains. Spikes in a given neuron are produced by the synergistic action of sodium and potassium of the voltage-dependent channels that open or close the gates. Hodgkin and Huxley (HH) equations describe the ionic mechanisms underlying the initiation and propagation of action potentials, through a set of nonlinear ordinary differential equations that approximate the electrical characteristics of the excitable cell. Path integral provides an adequate approach to compute quantities such as transition probabilities, and any stochastic system can be expressed in terms of this methodology. We use the technique of path integrals to determine the analytical solution driven by a non-Gaussian colored noise when considering the HH equations as a stochastic system. The different neuronal dynamics are investigated by estimating the path integral solutions driven by a non-Gaussian colored noise q. More specifically we take into account the correlational structures of the complex neuronal signals not just by estimating the transition probability associated to the Gaussian approach of the stochastic HH equations, but instead considering much more subtle processes accounting for the non-Gaussian noise that could be induced by the surrounding neural network and by feedforward correlations. This allows us to investigate the underlying dynamics of the neural system when different scenarios of noise correlations are considered. Sun, 01 Jan 2017 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/154732 2017-01-01T00:00:00Z To understand how single neurons process sensory information, it is necessary to develop suitable stochastic models to describe the response variability of the recorded spike trains. Spikes in a given neuron are produced by the synergistic action of sodium and potassium of the voltage-dependent channels that open or close the gates. Hodgkin and Huxley (HH) equations describe the ionic mechanisms underlying the initiation and propagation of action potentials, through a set of nonlinear ordinary differential equations that approximate the electrical characteristics of the excitable cell. Path integral provides an adequate approach to compute quantities such as transition probabilities, and any stochastic system can be expressed in terms of this methodology. We use the technique of path integrals to determine the analytical solution driven by a non-Gaussian colored noise when considering the HH equations as a stochastic system. The different neuronal dynamics are investigated by estimating the path integral solutions driven by a non-Gaussian colored noise q. More specifically we take into account the correlational structures of the complex neuronal signals not just by estimating the transition probability associated to the Gaussian approach of the stochastic HH equations, but instead considering much more subtle processes accounting for the non-Gaussian noise that could be induced by the surrounding neural network and by feedforward correlations. This allows us to investigate the underlying dynamics of the neural system when different scenarios of noise correlations are considered. http://sedici.unlp.edu.ar:80/handle/10915/154709 Articulo Communications Materials; vol. 3, no. 1 Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy₂Ti₂O₇, using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system. Sat, 01 Jan 2022 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/154709 2022-01-01T00:00:00Z Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy₂Ti₂O₇, using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system. http://sedici.unlp.edu.ar:80/handle/10915/154459 Articulo Molecules; vol. 28, no. 3 Competing interactions between charged inclusions in membranes of living organisms or charged nanoparticles in near-critical mixtures can lead to self-assembly into various patterns. Motivated by these systems, we developed a simple triangular lattice model for binary mixtures of oppositely charged particles with additional short-range attraction or repulsion between like or different particles, respectively. We determined the ground state for the system in contact with a reservoir of the particles for the whole chemical potentials plane, and the structure of self-assembled conglomerates for fixed numbers of particles. Stability of the low-temperature ordered patterns was verified by Monte Carlo simulations. In addition, we performed molecular dynamics simulations for a continuous model with interactions having similar features, but a larger range and lower strength than in the lattice model. Interactions with and without symmetry between different components were assumed. We investigated both the conglomerate formed in the center of a thin slit with repulsive walls, and the structure of a monolayer adsorbed at an attractive substrate. Both models give the same patterns for large chemical potentials or densities. For low densities, more patterns occur in the lattice model. Different phases coexist with dilute gas on the lattice and in the continuum, leading to different patterns in self-assembled conglomerates (‘rafts’). Sun, 01 Jan 2023 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/154459 2023-01-01T00:00:00Z Competing interactions between charged inclusions in membranes of living organisms or charged nanoparticles in near-critical mixtures can lead to self-assembly into various patterns. Motivated by these systems, we developed a simple triangular lattice model for binary mixtures of oppositely charged particles with additional short-range attraction or repulsion between like or different particles, respectively. We determined the ground state for the system in contact with a reservoir of the particles for the whole chemical potentials plane, and the structure of self-assembled conglomerates for fixed numbers of particles. Stability of the low-temperature ordered patterns was verified by Monte Carlo simulations. In addition, we performed molecular dynamics simulations for a continuous model with interactions having similar features, but a larger range and lower strength than in the lattice model. Interactions with and without symmetry between different components were assumed. We investigated both the conglomerate formed in the center of a thin slit with repulsive walls, and the structure of a monolayer adsorbed at an attractive substrate. Both models give the same patterns for large chemical potentials or densities. For low densities, more patterns occur in the lattice model. Different phases coexist with dilute gas on the lattice and in the continuum, leading to different patterns in self-assembled conglomerates (‘rafts’). http://sedici.unlp.edu.ar:80/handle/10915/148189 Preprint Journal of Dynamical Systems and Geometric Theories; vol. 13 Let (X, f) be a dynamical system with X a compact metric space. Let Xr be the product of r-copies of X, r≥ 1, and Φ: Xr → R. The multifractal decomposition for V –statistics for Φ, f is defined as [fórmula]. The set of points x ∊ X, for which the limit does not exist is called the irregular part, or historic set, of the spectrum. In this article we analyze the irregular part of the V -statistics for systems satisfying a weak form of the known Bowen specification property, called the non-uniform specification property. This concept was introduced by P. Varandas and allows to work in a nonuniformly hyperbolic context. Thu, 01 Jan 2015 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/148189 2015-01-01T00:00:00Z Let (X, f) be a dynamical system with X a compact metric space. Let Xr be the product of r-copies of X, r≥ 1, and Φ: Xr → R. The multifractal decomposition for V –statistics for Φ, f is defined as [fórmula]. The set of points x ∊ X, for which the limit does not exist is called the irregular part, or historic set, of the spectrum. In this article we analyze the irregular part of the V -statistics for systems satisfying a weak form of the known Bowen specification property, called the non-uniform specification property. This concept was introduced by P. Varandas and allows to work in a nonuniformly hyperbolic context. http://sedici.unlp.edu.ar:80/handle/10915/145882 Articulo European Biophysics Journal; vol. 38, no. 2 Plasma membrane vesicles have been widely employed to understand the biophysics of water movements, especially when active aquaporins are present. In general, water permeability coefficients in these preparations outcome from the analysis of the osmotic response of the vesicles by means of light scattering. As from now, this is possible by following a theoretical approach that assumes that scattered light follows a single exponential function and that this behavior is the consequence of vesicle volume changes due to an osmotic challenge. However, some experimental data do not necessarily fit to single exponentials but to double ones. It is argued that the observed double exponential behavior has two possible causes: different vesicle population in terms of permeability or in terms of size distribution. As classical models cannot identify this source of heterogeneity, a mathematical modeling approach was developed based on phenomenological equations of water transport. In the three comparative models presented here, it was assumed that water moves according to an osmotic mechanism across the vesicles, and there is no solute movement across them. Interestingly, when tested in a well described plasma membrane vesicle preparation, the application of these models indicates that the source of heterogeneity in the osmotic response is vesicles having different permeability, clearly discarding the variable size effect. In conclusion, the mathematical approach presented here allows to identify the source of heterogeneity; this information being of particular interest, especially when studying gating mechanisms triggered in water channel activity. Thu, 04 Sep 2008 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/145882 2008-09-04T00:00:00Z Plasma membrane vesicles have been widely employed to understand the biophysics of water movements, especially when active aquaporins are present. In general, water permeability coefficients in these preparations outcome from the analysis of the osmotic response of the vesicles by means of light scattering. As from now, this is possible by following a theoretical approach that assumes that scattered light follows a single exponential function and that this behavior is the consequence of vesicle volume changes due to an osmotic challenge. However, some experimental data do not necessarily fit to single exponentials but to double ones. It is argued that the observed double exponential behavior has two possible causes: different vesicle population in terms of permeability or in terms of size distribution. As classical models cannot identify this source of heterogeneity, a mathematical modeling approach was developed based on phenomenological equations of water transport. In the three comparative models presented here, it was assumed that water moves according to an osmotic mechanism across the vesicles, and there is no solute movement across them. Interestingly, when tested in a well described plasma membrane vesicle preparation, the application of these models indicates that the source of heterogeneity in the osmotic response is vesicles having different permeability, clearly discarding the variable size effect. In conclusion, the mathematical approach presented here allows to identify the source of heterogeneity; this information being of particular interest, especially when studying gating mechanisms triggered in water channel activity. http://sedici.unlp.edu.ar:80/handle/10915/145809 Articulo Journal of Statistical Physics; vol. 184, no. 3 The recent inflow of empirical data about the collective behaviour of strongly correlated biological systems has brought field theory and the renormalization group into the biophysical arena. Experiments on bird flocks and insect swarms show that social forces act on the particles' velocity through the generator of its rotations, namely the spin, indicating that mode-coupling field theories are necessary to reproduce the correct dynamical behaviour. Unfortunately, a theory for three coupled fields - density, velocity and spin - has a prohibitive degree of intricacy. A simplifying path consists in getting rid of density fluctuations by studying incompressible systems. This requires imposing a solenoidal constraint on the primary field, an unsolved problem even for equilibrium mode-coupling theories. Here, we perform an equilibrium dynamic renormalization group analysis of a mode-coupling field theory subject to a solenoidal constraint; using the classification of Halperin and Hohenberg, we can dub this case as a solenoidal Model G. We demonstrate that the constraint produces a new vertex that mixes static and dynamical coupling constants, and that this vertex is essential to grant the closure of the renormalization group structure and the consistency of dynamics with statics. Interestingly, although the solenoidal constraint leads to a modification of the static universality class, we find that it does not change the dynamical universality class, a result that seems to represent an exception to the general rule that dynamical universality classes are narrower than static ones. Our results constitute a solid stepping stone in the admittedly large chasm towards developing an off-equilibrium mode-coupling theory of biological groups. Sat, 28 Aug 2021 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/145809 2021-08-28T00:00:00Z The recent inflow of empirical data about the collective behaviour of strongly correlated biological systems has brought field theory and the renormalization group into the biophysical arena. Experiments on bird flocks and insect swarms show that social forces act on the particles' velocity through the generator of its rotations, namely the spin, indicating that mode-coupling field theories are necessary to reproduce the correct dynamical behaviour. Unfortunately, a theory for three coupled fields - density, velocity and spin - has a prohibitive degree of intricacy. A simplifying path consists in getting rid of density fluctuations by studying incompressible systems. This requires imposing a solenoidal constraint on the primary field, an unsolved problem even for equilibrium mode-coupling theories. Here, we perform an equilibrium dynamic renormalization group analysis of a mode-coupling field theory subject to a solenoidal constraint; using the classification of Halperin and Hohenberg, we can dub this case as a solenoidal Model G. We demonstrate that the constraint produces a new vertex that mixes static and dynamical coupling constants, and that this vertex is essential to grant the closure of the renormalization group structure and the consistency of dynamics with statics. Interestingly, although the solenoidal constraint leads to a modification of the static universality class, we find that it does not change the dynamical universality class, a result that seems to represent an exception to the general rule that dynamical universality classes are narrower than static ones. Our results constitute a solid stepping stone in the admittedly large chasm towards developing an off-equilibrium mode-coupling theory of biological groups.