Water Vapor and Cloud Formation in the TTL: Simulation Results vs. Satellite Observations

Driven by analyzed winds and temperature, domain-filling forward trajectory calculations are used to reproduce water vapor and cloud formations in the tropical tropopause layer (TTL). As with most Lagrangian models of this type, excess water vapor is instantaneously removed from the parcel to keep the relative humidity with respect to ice from exceeding a specified (super) saturation level. The dehydration occurrences serve as an indication of where and when cloud forms. Convective moistening through ice lofting and gravity waves are also included in our simulations as mechanisms that could affect water vapor abundances and cloud formations in the TTL. Our simulations produce water vapor mixing ratios close to that observed by the Aura Microwave Limb Sounder (MLS) and are consistent with the reanalysis tropical tropopause temperature biases, which proves the importance of the cold-point temperature to the water vapor abundances in the stratosphere. The simulation of cloud formation agrees with the patterns of cirrus distribution from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). It proves that the trajectory calculations fed by the analyzed wind and temperature could produce reasonable simulations of water vapor and cloud formation in the TTL

A Pulse-Gated, Predictive Neural Circuit

Recent evidence suggests that neural information is encoded in packets and may be flexibly routed from region to region. We have hypothesized that neural circuits are split into sub-circuits where one sub-circuit controls information propagation via pulse gating and a second sub-circuit processes graded information under the control of the first sub-circuit. Using an explicit pulse-gating mechanism, we have been able to show how information may be processed by such pulse-controlled circuits and also how, by allowing the information processing circuit to interact with the gating circuit, decisions can be made. Here, we demonstrate how Hebbian plasticity may be used to supplement our pulse-gated information processing framework by implementing a machine learning algorithm. The resulting neural circuit has a number of structures that are similar to biological neural systems, including a layered structure and information propagation driven by oscillatory gating with a complex frequency spectrum.Comment: This invited paper was presented at the 50th Asilomar Conference on Signals, Systems and Computer

Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains

Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain. Coherent activity has now been measured experimentally in many regions of mammalian cortex. Synfire chains are one of the main theoretical constructs that have been appealed to to describe coherent spiking phenomena. However, for some time, it has been known that synchronous activity in feedforward networks asymptotically either approaches an attractor with fixed waveform and amplitude, or fails to propagate. This has limited their ability to explain graded neuronal responses. Recently, we have shown that pulse-gated synfire chains are capable of propagating graded information coded in mean population current or firing rate amplitudes. In particular, we showed that it is possible to use one synfire chain to provide gating pulses and a second, pulse-gated synfire chain to propagate graded information. We called these circuits synfire-gated synfire chains (SGSCs). Here, we present SGSCs in which graded information can rapidly cascade through a neural circuit, and show a correspondence between this type of transfer and a mean-field model in which gating pulses overlap in time. We show that SGSCs are robust in the presence of variability in population size, pulse timing and synaptic strength. Finally, we demonstrate the computational capabilities of SGSC-based information coding by implementing a self-contained, spike-based, modular neural circuit that is triggered by, then reads in streaming input, processes the input, then makes a decision based on the processed information and shuts itself down

Bogoliubov excitation spectrum of an elongated condensate from quasi-one-dimensional to three-dimensional transition

The quasiparticle excitation spectra of a Bose gas trapped in a highly anisotropic trap is studied with respect to varying total number of particles by numerically solving the effective one-dimensional (1D) Gross-Pitaevskii (GP) equation proposed recently by Mateo \textit{et al.}. We obtain the static properties and Bogoliubov spectra of the system in the high energy domain. This method is computationally efficient and highly accurate for a condensate system undergoing a 1D to three-dimensional (3D) cigar-shaped transition, as is shown through a comparison our results with both those calculated by the 3D-GP equation and analytical results obtained in limiting cases. We identify the applicable parameter space for the effective 1D-GP equation and find that this equation fails to describe a system with large number of atoms. We also identify that the description of the transition from 1D Bose-Einstein condensate (BEC) to 3D cigar-shaped BEC using this equation is not smooth, which highlights the fact that for a finite value of $a_\perp/a_s$ the junction between the 1D and 3D crossover is not perfect.Comment: 17 pages, 6 figure