Understanding the dynamics of segregation bands of simulated granular material in a rotating drum

Axial segregation of a binary mixture of grains in a rotating drum is studied using Molecular Dynamics (MD) simulations. A force scheme leading to a constant restitution coefficient is used and shows that axial segregation is possible between two species of grains made of identical material differing by size. Oscillatory motion of bands is investigated and the influence of the frictional properties elucidated. The mechanism of bands merging is explained using direct imaging of individual grains

Causal Structure Learning with Conditional and Unique Information Groups-Decomposition Inequalities

The causal structure of a system imposes constraints on the joint probability distribution of variables that can be generated by the system. Archetypal constraints consist of conditional independencies between variables. However, particularly in the presence of hidden variables, many causal structures are compatible with the same set of independencies inferred from the marginal distributions of observed variables. Additional constraints allow further testing for the compatibility of data with specific causal structures. An existing family of causally informative inequalities compares the information about a set of target variables contained in a collection of variables, with a sum of the information contained in different groups defined as subsets of that collection. While procedures to identify the form of these groups-decomposition inequalities have been previously derived, we substantially enlarge the applicability of the framework. We derive groups-decomposition inequalities subject to weaker independence conditions, with weaker requirements in the configuration of the groups, and additionally allowing for conditioning sets. Furthermore, we show how constraints with higher inferential power may be derived with collections that include hidden variables, and then converted into testable constraints using data processing inequalities. For this purpose, we apply the standard data processing inequality of conditional mutual information and derive an analogous property for a measure of conditional unique information recently introduced to separate redundant, synergistic, and unique contributions to the information that a set of variables has about a target

Perceptual Learning of Fine Contrast Discrimination Under Non-roving, Roving-Without-Flanker, and Roving-with-Flanker Conditions and its Relation to Neuronal Activity in Macaque V1

\ua9 The Author(s) 2024.Perceptual learning refers to an improvement in perceptual abilities with training. Neural signatures of visual perceptual learning have been demonstrated mostly in mid- and high-level cortical areas, while changes in early sensory cortex were often more limited. We recorded continuously from multiple neuronal clusters in area V1 while macaque monkeys learned a fine contrast categorization task. Monkeys performed the contrast discrimination task initially when a constant-contrast sample stimulus was followed by a test stimulus of variable contrast, whereby they had to indicate whether the test was of lower or higher contrast than the sample. This was followed by sessions where we employed stimulus roving; i.e. the contrast of the sample stimulus varied from trial to trial. Finally, we trained animals, under ‘stimulus roving-with-flanker’ conditions, where the test stimuli to be discriminated were flanked by ‘flanking stimuli’. Perceptual discrimination abilities improved under non-roving conditions and under roving-with-flanker conditions as training progressed. Neuronal discrimination abilities improved with training mostly under non-roving conditions, but the effect was modest and limited to the most difficult contrast. Choice probabilities, quantifying how well neural activity is correlated with choice, equally increased with training during non-roving, but not during either of the roving conditions (with and without flankers). Noise correlations changed with training in both monkeys, but the changes were not consistent between monkeys. In one monkey, noise correlations decreased with training for non-roving and both roving conditions. In the other monkey, noise correlations changed for some conditions, but lacked a systematic pattern. Thus, while perceptual learning occurred under non-roving and roving-with-flanker conditions, the changes in neural activity in V1 were overall modest and were essentially absent under the different roving conditions

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis

The usefulness of different tools for learning Anatomy: what the students think

Resúmenes IV Congreso VetDoc de Docencia Veterinaria, León 2017 (6-7 de Julio)v[ES] La Anatomía es una ciencia básica en la enseñanza tanto médica como veterinaria que, hasta hace no mucho tiempo, utilizaba como únicas herramientas pedagógicas las clases magistrales y las disecciones. Sin embargo, en los últimos años se han empezado a utilizar modelos, simuladores o métodos relacionados con el diagnóstico por imagen, que están cambiando la manera de enseñar esta ciencia (Sugand et al., 2010). En este sentido, siempre es interesante conocer cómo aprovechan los alumnos para su aprendizaje los diferentes materiales a los que tienen acceso

Momentum Imaging of Electrons and Recoil Ions from Anion-neutral Interactions in a Cryogenic Ion Storage Ring

We have momentum-analyzed electrons and recoil ions in triple coincidence with projectiles neutralized in 300-keV Si-+Ar collisions. The experiment was performed at a cryogenic ion storage ring with an in-ring reaction microscope. Differential momentum distributions for detachment with simultaneous target ionization were obtained. At more than 15% of the detachment-only rate the ionization rate is surprisingly large. A correlated two-electron channel is found to be dominant. This is also unexpected because the projectile energy is well below the threshold for the correlated process

Perceptual Learning of Fine Contrast Discrimination Under Non-roving, Roving-Without-Flanker, and Roving-with-Flanker Conditions and its Relation to Neuronal Activity in Macaque V1

Perceptual learning refers to an improvement in perceptual abilities with training. Neural signatures of visual perceptual learning have been demonstrated mostly in mid- and high-level cortical areas, while changes in early sensory cortex were often more limited. We recorded continuously from multiple neuronal clusters in area V1 while macaque monkeys learned a fine contrast categorization task. Monkeys performed the contrast discrimination task initially when a constant-contrast sample stimulus was followed by a test stimulus of variable contrast, whereby they had to indicate whether the test was of lower or higher contrast than the sample. This was followed by sessions where we employed stimulus roving; i.e. the contrast of the sample stimulus varied from trial to trial. Finally, we trained animals, under ‘stimulus roving-with-flanker’ conditions, where the test stimuli to be discriminated were flanked by ‘flanking stimuli’. Perceptual discrimination abilities improved under non-roving conditions and under roving-with-flanker conditions as training progressed. Neuronal discrimination abilities improved with training mostly under non-roving conditions, but the effect was modest and limited to the most difficult contrast. Choice probabilities, quantifying how well neural activity is correlated with choice, equally increased with training during non-roving, but not during either of the roving conditions (with and without flankers). Noise correlations changed with training in both monkeys, but the changes were not consistent between monkeys. In one monkey, noise correlations decreased with training for non-roving and both roving conditions. In the other monkey, noise correlations changed for some conditions, but lacked a systematic pattern. Thus, while perceptual learning occurred under non-roving and roving-with-flanker conditions, the changes in neural activity in V1 were overall modest and were essentially absent under the different roving conditions

Perceptual learning of fine contrast discrimination changes neuronal tuning and population coding in macaque V4

Perceptual learning, the improvement in perceptual abilities with training, is thought to be mediated by an alteration of neuronal tuning. It remains poorly understood how tuning properties change as training progresses, whether improved stimulus tuning directly links to increased behavioural readout of sensory information, or how population coding mechanisms change with training. Here, we recorded continuously from multiple neuronal clusters in area V4 while macaque monkeys learned a fine contrast categorization task. Training increased neuronal coding abilities by shifting the steepest point of contrast response functions towards the categorization boundary. Population coding accuracy of difficult discriminations resulted largely from an increased information coding of individual channels, particularly for those channels that in early learning had larger ability for easy discriminations, but comparatively small encoding abilities for difficult discriminations. Population coding was also enhanced by specific changes in correlations. Neuronal activity became more indicative of upcoming choices with training

When Two Become One: The Limits of Causality Analysis of Brain Dynamics

Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest