Simulation on the Physical Process of Neural Electromagnetic Signal Generation Based on a Simple but Functional Bionic Na+ Channel

The physical processes occurring at open Na+ channels in neural fibers are essential for understanding the nature of neural signals and the mechanism by which the signals are generated and transmitted along nerves. However, there is less generally accepted description of these physical processes. We studied changes in the transmembrane ionic flux and the resulting two types of electromagnetic signals by simulating the Na+ transport across a bionic nanochannel model simplified from voltage-gated Na+ channels. Results show that the Na+ flux can reach a steady state in approximately 10 ns owing to the dynamic equilibrium of Na+ ions concentration difference between the both sides of membrane. After characterizing the spectrum and transmission of these two electromagnetic signals, the low-frequency transmembrane electric field is regarded as the physical quantity transmitting in waveguide-like lipid dielectric layer and triggering the neighboring voltage-gated channels. Factors influencing the Na+ flux transport are also studied. The impact of the Na+ concentration gradient is found higher than that of the initial transmembrane potential on the Na+ transport rate, and introducing the surface-negative charge in the upper third channel could increase the transmembrane Na+ current. This work can be further studied by improving the simulation model; however, the current work helps to better understand the electrical functions of voltage-gated ion channels in neural systems.

How to Estimate the Far-Side Open Flux Using STEREO Coronal Holes

Global magnetic field models use as input synoptic data, which usually show “aging effects” as the longitudinal $360^{\circ }$ 360 ∘ information is not obtained simultaneously. Especially during times of increased solar activity, the evolution of the magnetic field may yield large uncertainties. A significant source of uncertainty is the Sun’s magnetic field on the side of the Sun invisible to the observer. Various methods have been used to complete the picture: synoptic charts, flux-transport models, and far side helioseismology. In this study, we present a new method to estimate the far-side open flux within coronal holes using STEREO EUV observations. First, we correlate the structure of the photospheric magnetic field as observed with the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory (HMI/SDO) with features in the transition region. From the 304 Å intensity distribution, which we found to be specific to coronal holes, we derive an empirical estimate for the open flux. Then we use a large sample of 313 SDO coronal hole observations to verify this relation. Finally, we perform a cross-instrument calibration from SDO to STEREO data to enable the estimation of the open flux at solar longitudes not visible from Earth. We find that the properties of strong unipolar magnetic elements in the photosphere, which determine the coronal hole’s open flux, can be approximated by open fields in the transition region. We find that structures below a threshold of $78\%$ 78 % (STEREO) or $94\%$ 94 % (SDO) of the solar disk median intensity as seen in 304 Å filtergrams are reasonably well correlated with the mean magnetic flux density of coronal holes (cc$_{\mathrm{sp}} = 0.59$ = sp 0.59 ). Using the area covered by these structures ($A_{\mathrm{OF}}$ A OF ) and the area of the coronal hole ($A_{\mathrm{CH}}$ A CH ), we model the open magnetic flux of a coronal hole as $|\Phi _{\mathrm{CH}}| = 0.25 A_{\mathrm{CH}}~\mathrm{exp}(0.032 A_{\mathrm{OF}})$ | Φ CH | = 0.25 A CH exp ( 0.032 A OF ) with an estimated uncertainty of 40 to $60\%$ 60 % .

Response of the Atmosphere to Orographic Forcings: Insight from Idealised Simulations

Earth’s orography profoundly influences its climate and is a major reason behind the zonally asymmetric features observed in the atmospheric circulation. The response of the atmosphere to orographic forcing, when idealized aqua mountains are placed individually and in pairs (180° apart) at different latitudes, is investigated in the present study using a simplified general circulation model. The investigation reveals that the atmospheric response to orography is dependent on its latitudinal position: orographically triggered stationary waves in the mid-latitudes are most energetic compared to the waves generated due to anomalous divergence in the tropics. The impact on precipitation is confined to the latitude of the orography when it is placed near the tropics, but when it is situated at higher latitudes, it also has a significant remote impact on the tropics. In general, the tropical mountains block the easterly flow, resulting in a weakening of the Hadley cells and a local reduction in the total poleward flux transport by the stationary eddies. On the other hand, the mid-latitudinal orography triggers planetary-scale Rossby waves and enhances the poleward flux transport by stationary eddies. The twin mountains experiments, which are performed by placing orography in pairs at different latitudes, show that the energy fluxes, stationary wave, and precipitation pattern are not merely the linear additive sum of individual orographic responses at these latitudes. The non-linearity in a diagnostic sense is a product interaction of flow between the two mountains, which depends on the background flow, the separation distance between mountains, and wind shear worldwide.

Modeling and Prediction of Near-Earth Plasma Sheet Parameters Using the Rice Convection Model and the Recurrent Neural Network

Interaction between Earth’s magnetotail and its inner magnetosphere plays an important role in the transport of mass and energy in the ionosphere–magnetosphere coupled system. A number of first-principles models are devoted to understanding the associated dynamics. However, running these models, including both magnetohydrodynamic models and kinetic drift models, can be computationally expensive when self-consistency and high spatial resolution are required. In this study, we exploit an approach of building a parallel statistical model, based on the long short-term memory (LSTM) type of recurrent neural network, to forecast the results of a first-principles model, called the Rice Convection Model (RCM). The RCM is used to simulate the transient injection events, in which the flux-tube entropy parameter, dawn-to-dusk electric field component, and cumulative magnetic flux transport are calculated in the central plasma sheet. These key parameters are then used as initial inputs for training the LSTM. Using the trained LSTM multivariate parameters, we are able to forecast the plasma sheet parameters beyond the training time for several tens of minutes that are found to be consistent with the subsequent RCM simulation results. Our tests indicate that the recurrent neural network technique can be efficiently used for forecasting numerical simulations of magnetospheric models. The potential to apply this approach to other models is also discussed.