Dynamic flexoelectric effect in perovskites from first principles calculations

Using the dynamical matrix of a crystal obtained from ab initio calculations, we have evaluated for the first time the strength of the dynamic flexoelectric effect and found it comparable to that of the static bulk flexoelectric effect, in agreement with earlier order-of-magnitude estimates. We also proposed a method of evaluation of these effects directly from the simulated phonon spectra. This method can also be applied to the analysis of the experimental phonon spectra, being currently the only one enabling experimental characterization of the static bulk flexoelectric effect

Ground-state landscape of 2d +-J Ising spin glasses

Large numbers of ground states of two-dimensional Ising spin glasses with periodic boundary conditions in both directions are calculated for sizes up to 40^2. A combination of a genetic algorithm and Cluster-Exact Approximation is used. For each quenched realization of the bonds up to 40 independent ground states are obtained. For the infinite system a ground-state energy of e=-1.4015(3) is extrapolated. The ground-state landscape is investigated using a finite-size scaling analysis of the distribution of overlaps. The mean-field picture assuming a complex landscape describes the situation better than the droplet-scaling model, where for the infinite system mainly two ground states exist. Strong evidence is found that the ground states are not organized in an ultrametric fashion in contrast to previous results for three-dimensional spin glasses.Comment: 9 pages, revtex, 11 figures, 51 reference

Output Stream of Binding Neuron with Feedback

The binding neuron model is inspired by numerical simulation of Hodgkin-Huxley-type point neuron, as well as by the leaky integrate-and-fire model. In the binding neuron, the trace of an input is remembered for a fixed period of time after which it disappears completely. This is in the contrast with the above two models, where the postsynaptic potentials decay exponentially and can be forgotten only after triggering. The finiteness of memory in the binding neuron allows one to construct fast recurrent networks for computer modeling. Recently, the finiteness is utilized for exact mathematical description of the output stochastic process if the binding neuron is driven with the Poissonian input stream. In this paper, the simplest networking is considered for binding neuron. Namely, it is expected that every output spike of single neuron is immediately fed into its input. For this construction, externally fed with Poissonian stream, the output stream is characterized in terms of interspike interval probability density distribution if the binding neuron has threshold 2. For higher thresholds, the distribution is calculated numerically. The distributions are compared with those found for binding neuron without feedback, and for leaky integrator. Sample distributions for leaky integrator with feedback are calculated numerically as well. It is oncluded that even the simplest networking can radically alter spikng statistics. Information condensation at the level of single neuron is discussed.Comment: Version #1: 4 pages, 5 figures, manuscript submitted to Biological Cybernetics. Version #2 (this version): added 3 pages of new text with additional analytical and numerical calculations, 2 more figures, 11 more references, added Discussion sectio