Diffusion and Localization of Cold Atoms in 3D Optical Speckle

In this work we re-formulate and solve the self-consistent theory for localization to a Bose-Einstein condensate expanding in a 3D optical speckle. The long-range nature of the fluctuations in the potential energy, treated in the self-consistent Born approximation, make the scattering strongly velocity dependent, and its consequences for mobility edge and fraction of localized atoms have been investigated numerically.Comment: 8 pages, 11 figure

Three-dimensional localization of ultracold atoms in an optical disordered potential

We report a study of three-dimensional (3D) localization of ultracold atoms suspended against gravity, and released in a 3D optical disordered potential with short correlation lengths in all directions. We observe density profiles composed of a steady localized part and a diffusive part. Our observations are compatible with the self-consistent theory of Anderson localization, taking into account the specific features of the experiment, and in particular the broad energy distribution of the atoms placed in the disordered potential. The localization we observe cannot be interpreted as trapping of particles with energy below the classical percolation threshold.Comment: published in Nature Physics; The present version is the initial manuscript (unchanged compared to version 1); The published version is available online at http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys2256.htm

Edge Detection Based on Hodgkin-Huxley Neuron Model Simulation

International audienceIn this paper, we propose a spiking neural network model for edge detection in images. The proposed model is biologically inspired by the mechanisms employed by natural vision systems, more specifically by the biologically fulfilled function of simple cells of the human primary visual cortex that are selective for orientation. Several aspects are studied in this model according to three characteristics: feedforward spiking neural structure; conductance-based model of the Hodgkin-Huxley neuron and Gabor receptive fields structure. A visualized map is generated using the firing rate of neurons representing the orientation map of the visual cortex area. We have simulated the proposed model on different images. Successful computer simulation results are obtained. For comparison, we have chosen five methods for edge detection. We finally evaluate and compare the performances of our model toward contour detection using a public dataset of natural images with associated contour ground truths. Experimental results show the ability and high performance of the proposed network model