A new wavelet-based approach for the automated treatment of large sets of lunar occultation data

The introduction of infrared arrays for lunar occultations (LO) work and the improvement of predictions based on new deep IR catalogues have resulted in a large increase in the number of observable occultations. We provide the means for an automated reduction of large sets of LO data. This frees the user from the tedious task of estimating first-guess parameters for the fit of each LO lightcurve. At the end of the process, ready-made plots and statistics enable the user to identify sources which appear to be resolved or binary and to initiate their detailed interactive analysis. The pipeline is tailored to array data, including the extraction of the lightcurves from FITS cubes. Because of its robustness and efficiency, the wavelet transform has been chosen to compute the initial guess of the parameters of the lightcurve fit. We illustrate and discuss our automatic reduction pipeline by analyzing a large volume of novel occultation data recorded at Calar Alto Observatory. The automated pipeline package is available from the authors.Comment: Accepted to Astronomy & Astrophysics 9 pages, 5 figure

Multiresolution approach for period determination on unevenly sampled data

In this paper we present a multiresolution-based method for period determination that is able to deal with unevenly sampled data. This method allows us to detect superimposed periodic signals with lower signal-to-noise ratios than in classical methods. This multiresolution-based method is a generalization of the wavelet-based method for period detection that is unable to deal with unevenly sampled data, presented by the authors in Otazu et al. (2002). This new method is a useful tool for the analysis of real data and, in particular, for the analysis of astronomical data.Comment: 6 pages, 3 figures, LaTeX, uses mn2e.cls. Accepted for publication in MNRA

A Corticothalamic Circuit Model for Sound Identification in Complex Scenes

The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal

The Calar Alto lunar occultation program: update and new results

We present an update of the lunar occultation program which is routinely carried out in the near-IR at the Calar Alto Observatory. A total of 350 events were recorded since our last report (Fors et al. 2004). In the course of eight runs we have observed, among others, late-type giants, T-Tauri stars, and infrared sources. Noteworthy was a passage of the Moon close to the galactic center, which produced a large number of events during just a few hours in July 2004. Results include the determinations of the angular diameter of RZ Ari, and the projected separations and brightness ratios for one triple and 13 binary stars, almost all of which representing first time detections. Projected separations range from 0.09arcsec to 0.007arcsec. We provide a quantitative analysis of the performance achieved in our observations in terms of angular resolution and sensitivity, which reach about 0.003arcsec and K~8.5mag, respectively. We also present a statistical discussion of our sample, and in particular of the frequency of detection of binaries among field stars.Comment: 8 pages, 2 figures. Accepted for publication in A&

A theory of how active behavior stabilises neural activity: neural gain modulation by closed-loop environmental feedback

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence of neural fluctuations, across the brain, on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone

NICE : A Computational solution to close the gap from colour perception to colour categorization

The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms