Electronic and phononic Raman scattering in detwinned YBa2_2Cu3_3O6.95_{6.95} and Y0.85_{0.85}Ca0.15_{0.15}Ba2_2Cu3_3O6.95_{6.95}: s-wave admixture to the dx2−y2d_{x^2-y^2}-wave order parameter

Inelastic light (Raman) scattering has been used to study electronic excitations and phonon anomalies in detwinned, slightly overdoped YBa$_2$Cu$_3$O$_{6.95}$ and moderately overdoped Y$_{0.85}$Ca$_{0.15}$Ba$_2$Cu$_3$O$_{6.95}$ single crystals. In both samples modifications of the electronic pair-breaking peaks when interchanging the a- and b-axis were observed. The lineshapes of several phonon modes involving plane and apical oxygen vibrations exhibit pronounced anisotropies with respect to the incident and scattered light field configurations. Based on a theoretical model that takes both electronic and phononic contributions to the Raman spectra into account, we attribute the anisotropy of the superconductivity-induced changes in the phonon lineshapes to a small s-wave admixture to the $d_{x^2-y^2}$ pair wave-function. Our theory allows us to disentangle the electronic Raman signal from the phononic part and to identify corresponding interference terms. We argue that the Raman spectra are consistent with an s-wave admixture with an upper limit of 20 percent.Comment: accepted in Phys. Rev. B, 11 page

Efficient population coding

Natural stimulations caused by objects in the surrounding world do not stimulate single sensory receptors in isolation but lead to the activation of large numbers of neurons simultaneously. Thus, typical stimulus variables of interest are represented only implicitly in activation patterns across large neural populations. These patterns are statistical in nature since repeated presentation of the same stimulus usually leads to highly variable responses. The large dimensionality and randomness of the neural responses make it difficult to assess how well different stimuli can be discriminated. Depending on how effectively neurons share the labor of encoding, the accuracy with which stimuli are represented can change dramatically. Thus, studying the efficiency of population codes is important for our understanding of both which information is encoded in neural populations and how it is encoded