Interfacial Thermal Resistive Switching in (Pt,Cr)/SrTiO3 Devices.

The operation of oxide-based memristive devices relies on the fast accumulation and depletion of oxygen vacancies by an electric field close to the metal-oxide interface. Here, we show that the reversible change of the local concentration of oxygen vacancies at this interface also produces a change in the thermal boundary resistance (TBR), i.e., a thermal resistive switching effect. We used frequency domain thermoreflectance to monitor the interfacial metal-oxide TBR in (Pt,Cr)/SrTiO3 devices, showing a change of ≈20% under usual SET/RESET operation voltages, depending on the structure of the device. Time-dependent thermal relaxation experiments suggest ionic rearrangement along the whole area of the metal/oxide interface, apart from the ionic filament responsible for the electrical conductivity switching. The experiments presented in this work provide valuable knowledge about oxide ion dynamics in redox-based memristive devices.

Annealing effects on the magnetic and magnetotransport properties of iron oxide nanoparticles self-assemblies.

In magnetic tunnel junctions based on iron oxide nanoparticles the disorder and the oxidation state of the surface spin as well as the nanoparticles functionalization play a crucial role in the magnetotransport properties. In this work, we report a systematic study of the effects of vacuum annealing on the structural, magnetic and transport properties of self-assembled ∼10 nm Fe3O4nanoparticles. The high temperature treatment (from 573 to 873 K) decomposes the organic coating into amorphous carbon, reducing the electrical resistivity of the assemblies by 4 orders of magnitude. At the same time, the 3.Fe2+/(Fe3++Fe2+) ratio is reduced from 1.11 to 0.13 when the annealing temperature of the sample increases from 573 to 873 K, indicating an important surface oxidation. Although the 2 nm physical gap remains unchanged with the thermal treatment, a monotonous decrease of tunnel barrier width was obtained from the electron transport measurements when the annealing temperature increases, indicating an increment in the number of defects and hot-spots in the gap between the nanoparticles. This is reflected in the reduction of the spin dependent tunneling, which reduces the interparticle magnetoresistance. This work shows new insights about influence of the nanoparticle interfacial composition, as well their the spatial arrangement, on the tunnel transport of self-assemblies, and evidence the importance of optimizing the nanostructure fabrication for increasing the tunneling current without degrading the spin polarized current.

Light-induced bi-directional switching of thermal conductivity in azobenzene-doped liquid crystal mesophases.

The development of systems that can be switched between states with different thermal conductivities is one of the current challenges in materials science. Despite their enormous diversity and chemical richness, molecular materials have been only scarcely explored in this regard. Here, we report a reversible, light-triggered thermal conductivity switching of ≈30-40% in mesophases of pure 4,4'-dialkyloxy-3-methylazobenzene. By doping a liquid crystal matrix with the azobenzene molecules, reversible and bidirectional switching of the thermal conductivity can be achieved by UV/Vis-light irradiation. Given the enormous variety of photoactive molecules and chemically compatible liquid crystal mesophases, this approach opens unforeseen possibilities for developing effective thermal switches based on molecular materials.

COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques.

BACKGROUND AND OBJECTIVES: Epidemiological models of epidemic spread are an essential tool for optimizing decision-making. The current literature is very extensive and covers a wide variety of deterministic and stochastic models. However, with the increase in computing resources, new, more general, and flexible procedures based on simulation models can assess the effectiveness of measures and quantify the current state of the epidemic. This paper illustrates the potential of this approach to build a new dynamic probabilistic model to estimate the prevalence of SARS-CoV-2 infections in different compartments. METHODS: We propose a new probabilistic model in which, for the first time in the epidemic literature, parameter learning is carried out using gradient-free stochastic black-box optimization techniques simulating multiple trajectories of the infection dynamics in a general way, solving an inverse problem that is defined employing the daily information from mortality records. RESULTS: After the application of the new proposal in Spain in the first and successive waves, the result of the model confirms the accuracy to estimate the seroprevalence and allows us to know the real dynamics of the pandemic a posteriori to assess the impact of epidemiological measures by the Spanish government and to plan more efficiently the subsequent decisions with the prior knowledge obtained. CONCLUSIONS: The model results allow us to estimate the daily patterns of COVID-19 infections in Spain retrospectively and examine the population's exposure to the virus dynamically in contrast to seroprevalence surveys. Furthermore, given the flexibility of our simulation framework, we can model situations -even using non-parametric distributions between the different compartments in the model- that other models in the existing literature cannot. Our general optimization strategy remains valid in these cases, and we can easily create other non-standard simulation epidemic models that incorporate more complex and dynamic structures.

Psychophysical evaluation of individual low-level feature influences on visual attention.

In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images.

Dynamic Whitening Saliency.

General dynamic scenes involve multiple rigid and flexible objects, with relative and common motion, camera induced or not. The complexity of the motion events together with their strong spatio-temporal correlations make the estimation of dynamic visual saliency a big computational challenge. In this work, we propose a computational model of saliency based on the assumption that perceptual relevant information is carried by high-order statistical structures. Through whitening, we completely remove the second-order information (correlations and variances) of the data, gaining access to the relevant information. The proposed approach is an analytically tractable and computationally simple framework which we call Dynamic Adaptive Whitening Saliency (AWS-D). For model assessment, the provided saliency maps were used to predict the fixations of human observers over six public video datasets, and also to reproduce the human behavior under certain psychophysical experiments (dynamic pop-out). The results demonstrate that AWS-D beats state-of-the-art dynamic saliency models, and suggest that the model might contain the basis to understand the key mechanisms of visual saliency. Experimental evaluation was performed using an extension to video of the well-known methodology for static images, together with a bootstrap permutation test (random label hypothesis) which yields additional information about temporal evolution of the metrics statistical significance.

On the relationship between optical variability, visual saliency, and eye fixations: a computational approach.

A hierarchical definition of optical variability is proposed that links physical magnitudes to visual saliency and yields a more reductionist interpretation than previous approaches. This definition is shown to be grounded on the classical efficient coding hypothesis. Moreover, we propose that a major goal of contextual adaptation mechanisms is to ensure the invariance of the behavior that the contribution of an image point to optical variability elicits in the visual system. This hypothesis and the necessary assumptions are tested through the comparison with human fixations and state-of-the-art approaches to saliency in three open access eye-tracking datasets, including one devoted to images with faces, as well as in a novel experiment using hyperspectral representations of surface reflectance. The results on faces yield a significant reduction of the potential strength of semantic influences compared to previous works. The results on hyperspectral images support the assumptions to estimate optical variability. As well, the proposed approach explains quantitative results related to a visual illusion observed for images of corners, which does not involve eye movements.

Assessing neural activity related to decision-making through flexible odds ratio curves and their derivatives.

It is well established that neural activity is stochastically modulated over time. Therefore, direct comparisons across experimental conditions and determination of change points or maximum firing rates are not straightforward. This study sought to compare temporal firing probability curves that may vary across groups defined by different experimental conditions. Odds-ratio (OR) curves were used as a measure of comparison, and the main goal was to provide a global test to detect significant differences of such curves through the study of their derivatives. An algorithm is proposed that enables ORs based on generalized additive models, including factor-by-curve-type interactions to be flexibly estimated. Bootstrap methods were used to draw inferences from the derivatives curves, and binning techniques were applied to speed up computation in the estimation and testing processes. A simulation study was conducted to assess the validity of these bootstrap-based tests. This methodology was applied to study premotor ventral cortex neural activity associated with decision-making. The proposed statistical procedures proved very useful in revealing the neural activity correlates of decision-making in a visual discrimination task.

A role for the ventral premotor cortex beyond performance monitoring.

Depending on the circumstances, decision making requires either comparing current sensory information with that showed recently or with that recovered from long-term memory (LTM). In both cases, to learn from past decisions and adapt future ones, memories and outcomes have to be available after the report of a decision. The ventral premotor cortex (PMv) is a good candidate for integrating memory traces and outcomes because it is involved in working-memory, decision-making, and encoding the outcomes. To test this hypothesis we recorded the extracellular unit activity while monkeys performed 2 variants of a visual discrimination task. In one task, the decision was based on the comparison of the orientation of a current stimulus with that of another stimulus recently shown. In the other task, the monkeys had to compare the current orientation of the stimulus with the correct one retrieved from LTM. Here, we report that when the task required retrieval of the stimulus and its use in the following trials, the neurons continue encoding this internal representation together with the outcomes after the monkey has emitted the motor response. However, this codification did not occur when the stimulus was shown recently and updated every trial. These results suggest that the PMv activity represents the information needed to evaluate the consequences of a decision. We interpret these results as evidence that the PMv plays a role in evaluating the outcomes that can serve to learn and thus adapt future decision to environmental demands.

Application of penalized splines in analyzing neuronal data.

Neuron experiments produce high-dimensional data structures. Therefore, application of smoothing techniques in the analysis of neuronal data from electrophysiological experiments has received considerable attention of late. We investigate the use of penalized splines in the analysis of neuronal data. This is first illustrated when interested in the temporal trend of a single neuron. An approach to investigate the maximal firing rate, based on the penalizedspline model is proposed. Determination of the time of maximal firing rate is based on non-linear optimization of the objective function with the corresponding confidence intervals constructed based on the first-order derivative function. To distinguish between the curves from different experimental conditions in a moment-by-moment sense, bias adjusted simulation-based simultaneous confidence bands leading to global inference in the time domain are constructed. The bands are an extension of the approach proposed by Ruppert et al. (2003). These methods are in a second step extended towards the analysis of a population of neurons via a marginal or population-averaged model.

Neural correlates of decisions and their outcomes in the ventral premotor cortex.

Selection of the appropriate action in a changing environment involves a chain of events that goes from perception through decision to action and evaluation of the outcomes. What and where in the brain are the correlates of these events? The ventral premotor cortex (PMv) is a candidate because (1) it is involved in sensory transformations for visually guided actions and in perceptual decisions, and (2) it is connected with sensory, motor, and high-level cognitive areas related to performance monitoring. Therefore, we hypothesized that it would be the site for representing sensory perception for action and for evaluating the decision consequences. Trained monkeys were required to discriminate the orientation of two lines showed in sequence and separated by a delay. Monkeys compared the orientation of the second line with the memory trace of the first and communicated whether the second was to the left or to the right of the first. Here we show that the activity of PMv neurons reflected (1) the first stimuli and its memory trace during the delay and comparison periods, (2) its comparison with the second stimuli, including the strength of the evidence, and (3) the result of the discrimination (choice). After the monkeys reported the choice, there were neurons that only encoded the choices, others only the outcomes, and others the choices and outcomes together. The representation of task cues, decision variables, and their outcomes suggest a role of PMv as part of a supervisory network involved in shaping future behavior and in learning.