A Fractional Calculus of Variations for Multiple Integrals with Application to Vibrating String

We introduce a fractional theory of the calculus of variations for multiple integrals. Our approach uses the recent notions of Riemann-Liouville fractional derivatives and integrals in the sense of Jumarie. Main results provide fractional versions of the theorems of Green and Gauss, fractional Euler-Lagrange equations, and fractional natural boundary conditions. As an application we discuss the fractional equation of motion of a vibrating string.Comment: Accepted for publication in the Journal of Mathematical Physics (14/January/2010

Neutral heavy lepton production at next high energy e+e−e^+e^- linear colliders

The discovery potential for detecting new heavy Majorana and Dirac neutrinos at some recently proposed high energy $e^+e^-$ colliders is discussed. These new particles are suggested by grand unified theories and superstring-inspired models. For these models the production of a single heavy neutrino is shown to be more relevant than pair production when comparing cross sections and neutrino mass ranges. The process $e^+e^- \longrightarrow {\nu} e^{\pm} W^{\mp}$ is calculated including on-shell and off-shell heavy neutrino effects. We present a detailed study of cross sections and distributions that shows a clear separation between the signal and standard model contributions, even after including hadronization effects.Comment: 4 pages including 15 figures, 1 table. RevTex. Accepted in Physical Review

Recording from two neurons: second order stimulus reconstruction from spike trains and population coding

We study the reconstruction of visual stimuli from spike trains, recording simultaneously from the two H1 neurons located in the lobula plate of the fly Chrysomya megacephala. The fly views two types of stimuli, corresponding to rotational and translational displacements. If the reconstructed stimulus is to be represented by a Volterra series and correlations between spikes are to be taken into account, first order expansions are insufficient and we have to go to second order, at least. In this case higher order correlation functions have to be manipulated, whose size may become prohibitively large. We therefore develop a Gaussian-like representation for fourth order correlation functions, which works exceedingly well in the case of the fly. The reconstructions using this Gaussian-like representation are very similar to the reconstructions using the experimental correlation functions. The overall contribution to rotational stimulus reconstruction of the second order kernels - measured by a chi-squared averaged over the whole experiment - is only about 8% of the first order contribution. Yet if we introduce an instant-dependent chi-square to measure the contribution of second order kernels at special events, we observe an up to 100% improvement. As may be expected, for translational stimuli the reconstructions are rather poor. The Gaussian-like representation could be a valuable aid in population coding with large number of neurons