Gain in Stochastic Resonance: Precise Numerics versus Linear Response Theory beyond the Two-Mode Approximation

In the context of the phenomenon of Stochastic Resonance (SR) we study the correlation function, the signal-to-noise ratio (SNR) and the ratio of output over input SNR, i.e. the gain, which is associated to the nonlinear response of a bistable system driven by time-periodic forces and white Gaussian noise. These quantifiers for SR are evaluated using the techniques of Linear Response Theory (LRT) beyond the usually employed two-mode approximation scheme. We analytically demonstrate within such an extended LRT description that the gain can indeed not exceed unity. We implement an efficient algorithm, based on work by Greenside and Helfand (detailed in the Appendix), to integrate the driven Langevin equation over a wide range of parameter values. The predictions of LRT are carefully tested against the results obtained from numerical solutions of the corresponding Langevin equation over a wide range of parameter values. We further present an accurate procedure to evaluate the distinct contributions of the coherent and incoherent parts of the correlation function to the SNR and the gain. As a main result we show for subthreshold driving that both, the correlation function and the SNR can deviate substantially from the predictions of LRT and yet, the gain can be either larger or smaller than unity. In particular, we find that the gain can exceed unity in the strongly nonlinear regime which is characterized by weak noise and very slow multifrequency subthreshold input signals with a small duty cycle. This latter result is in agreement with recent analogue simulation results by Gingl et al. in Refs. [18, 19].Comment: 22 pages, 5 eps figures, submitted to PR

Regulation of Axonal HCN1 Trafficking in Perforant Path Involves Expression of Specific TRIP8b Isoforms

The functions of HCN channels in neurons depend critically on their subcellular localization, requiring fine-tuned machinery that regulates subcellular channel trafficking. Here we provide evidence that regulatory mechanisms governing axonal HCN channel trafficking involve association of the channels with specific isoforms of the auxiliary subunit TRIP8b. In the medial perforant path, which normally contains HCN1 channels in axon terminals in immature but not in adult rodents, we found axonal HCN1 significantly increased in adult mice lacking TRIP8b (TRIP8b−/−). Interestingly, adult mice harboring a mutation that results in expression of only the two most abundant TRIP8b isoforms (TRIP8b[1b/2]−/−) exhibited an HCN1 expression pattern similar to wildtype mice, suggesting that presence of one or both of these isoforms (TRIP8b(1a), TRIP8b(1a-4)) prevents HCN1 from being transported to medial perforant path axons in adult mice. Concordantly, expression analyses demonstrated a strong increase of expression of both TRIP8b isoforms in rat entorhinal cortex with age. However, when overexpressed in cultured entorhinal neurons of rats, TRIP8b(1a), but not TRIP8b(1a-4), altered substantially the subcellular distribution of HCN1 by promoting somatodendritic and reducing axonal expression of the channels. Taken together, we conclude that TRIP8b isoforms are important regulators of HCN1 trafficking in entorhinal neurons and that the alternatively-spliced isoform TRIP8b(1a) could be responsible for the age-dependent redistribution of HCN channels out of perforant path axon terminals

A stochastic resonator is able to greatly improve signal-to-noise ratio, Physics Letters A 224

After a decade of doubts, first time in the history of stochastic resonance (SR), we demonstrate that a simple stochastic resonator does greatly improve the signal-to-noise-ratio (SNR) of a periodic signal with additive Gaussian noise. The particular stochastic resonator is a level-crossing detector (LCD) driven by the sum of a periodic spike train signal and a band-limited Gaussian white noise. To reach the improvement of the SNR, the stochastic resonator has to work in the strongly nonlinear response limit and the noise has to have a high cut-off frequency compared to the reciprocal duration of the spikes. We demonstrate by analog and computer simulations that the SNR gain goes beyond 4 orders of magnitude at practical conditions. These findings get a particular importance due the fact that simplest neurone models behave very similarly to our arrangement, so the results might have direct applications in neural systems. Stochastic resonance (SR) phenomenon [1] From its discovery [1] until quite recently [2], researchers had believed that SR phenomena required "dynamical" (often bistable) system

Photoanisotropic polarization gratings beyond the small recording angle regime

Polarization gratings can be realized by polarization holographic recording in photoanisotropic materials. In this paper, we study two types of polarization gratings. One is recorded with two orthogonally circularly (OC) polarized beams and the other one with two orthogonally linearly (OL) polarized beams. The interference of both cases is explored beyond the small recording angle regime. A novel method is proposed to represent the polarization states of the modulation. The diffraction by polarization gratings is studied with rigorous diffraction theory. Simulations based on the Finite Element Method are performed for both OC and OL polarization gratings at small and large recording angles.Imaging Science and TechnologyApplied Science

SPADnet: a fully digital, scalable and networked photonic component for time-of-flight PET applications

The SPADnet FP7 European project is aimed at a new generation of fully digital, scalable and networked photonic components to enable large area image sensors, with primary target gamma-ray and coincidence detection in (Time-of-Flight) Positron Emission Tomography (PET). SPADnet relies on standard CMOS technology, therefore allowing for MRI compatibility. SPADnet innovates in several areas of PET systems, from optical coupling to single-photon sensor architectures, from intelligent ring networks to reconstruction algorithms. It is built around a natively digital, intelligent SPAD (Single-Photon Avalanche Diode)-based sensor device which comprises an array of 8x16 pixels, each composed of 4 mini-SiPMs with in situ time-to-digital conversion, a multi-ring network to filter, carry, and process data produced by the sensors at 2Gbps, and a 130nm CMOS process enabling mass-production of photonic modules that are optically interfaced to scintillator crystals. A few tens of sensor devices are tightly abutted on a single PCB to form a so-called sensor tile, thanks to TSV (Through Silicon Via) connections to their backside (replacing conventional wire bonding). The sensor tile is in turn interfaced to an FPGA-based PCB on its back. The resulting photonic module acts as an autonomous sensing and computing unit, individually detecting gamma photons as well as thermal and Compton events. It determines in real time basic information for each scintillation event, such as exact time of arrival, position and energy, and communicates it to its peers in the field of view. Coincidence detection does therefore occur directly in the ring itself, in a differed and distributed manner to ensure scalability. The selected true coincidence events are then collected by a snooper module, from which they are transferred to an external reconstruction computer using Gigabit Ethernet. We will detail the SPADnet core technologies as well as the latest project achievements in all project areas