Inter-areal Balanced Amplification Enhances Signal Propagation in a Large-Scale Circuit Model of the Primate Cortex

The rise concern about mobile communication performance has driven the growing demand for the construction of mobile network signal maps which are widely utilized in network monitoring, spectrum management, and indoor/outdoor localization. Existing studies such as time-consuming and labor-intensive site surveys are difficult to maintain an update-to-date finegrained signal map within a large area. The mobile crowdsensing (MCS) paradigm is a promising approach for building signal maps because collecting large-scale MCS data is low-cost and with little extra-efforts. However, the dynamic environment and the mobility of the crowd cause spatio-temporal uncertainty and sparsity of MCS. In this work, we leverage MCS as an opportunity to conduct the city-wide mobile network signal map construction. We propose a fine-grained city-wide Cellular Signal Map Construction (CSMC) framework to address two challenges including (i) the problem of missing and unreliable MCS data; (ii) spatio-temporal uncertainty of signal propagation. In particular, CSMC captures spatio-temporal characteristics of signals from both inter- and intra- cellular base stations and conducts missing signal recovery with Bayesian tensor decomposition to build large-area fine-grained signal maps. Furthermore, CSMC develops a context-aware multi-view fusion network to make full use of external information and enhance signal map construction accuracy. To evaluate the performance of CSMC, we conduct extensive experiments and ablation studies on a large-scale dataset with over 200GB MCS signal records collected from Shanghai. Experimental results demonstrate that our model outperforms state-of-the-art baselines in the accuracy of signal estimation and user localization.

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Neurite, a Finite Difference Large Scale Parallel Program for the Simulation of Electrical Signal Propagation in Neurites under Mechanical Loading

PLoS ONE ◽

10.1371/journal.pone.0116532 ◽

2015 ◽

Vol 10 (2) ◽

pp. e0116532 ◽

Cited By ~ 14

Author(s):

Julián A. García-Grajales ◽

Gabriel Rucabado ◽

Antonio García-Dopico ◽

José-María Peña ◽

Antoine Jérusalem

Keyword(s):

Finite Difference ◽

Mechanical Loading ◽

Large Scale ◽

Signal Propagation ◽

Electrical Signal ◽

Parallel Program

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Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

10.1101/164483 ◽

2017 ◽

Author(s):

Marwan Abdellah ◽

Juan Hernando ◽

Nicolas Antille ◽

Stefan Eilemann ◽

Henry Markram ◽

...

Keyword(s):

In Silico ◽

Large Scale ◽

Circuit Model ◽

Optical Studies ◽

Domain Experts ◽

Multi Stage ◽

Volumetric Models ◽

Imaging Experiment ◽

Cortical Circuit ◽

Tissue Models

AbstractBackground We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender.Results Our pipeline is applied to create 55 exemplar neurons representing the various morphological types that are reconstructed from the somatsensory cortex of a juvenile rat. The pipeline is then used to reconstruct a volumetric slice of a cortical circuit model that contains ∼210,000 neurons. The applicability of our pipeline to create highly realistic volumetric models of neocortical circuits is demonstrated with an in silico imaging experiment that simulates tissue visualization with brightfield microscopy. The results were evaluated with a group of domain experts to address their demands and also to extend the workflow based on their feedback.Conclusion A systematic workflow is presented to create large scale synthetic tissue models of the neocortical circuitry. This workflow is fundamental to enlarge the scale of in silico neuroscientific optical experiments from several tens of cubic micrometers to a few cubic millimeters.

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Signal Propagation Along Unidimensional Neuronal Networks

Journal of Neurophysiology ◽

10.1152/jn.00264.2005 ◽

2005 ◽

Vol 94 (5) ◽

pp. 3406-3416 ◽

Cited By ~ 47

Author(s):

Ofer Feinerman ◽

Menahem Segal ◽

Elisha Moses

Keyword(s):

Large Scale ◽

Signal Transmission ◽

Signal Propagation ◽

High Amplitude ◽

Adhesive Material ◽

One Dimensional ◽

Calcium Indicator ◽

Scale Population ◽

Population Burst ◽

Linear Signal

Dissociated neurons were cultured on lines of various lengths covered with adhesive material to obtain an experimental model system of linear signal transmission. The neuronal connectivity in the linear culture is characterized, and it is demonstrated that local spiking activity is relayed by synaptic transmission along the line of neurons to develop into a large-scale population burst. Formally, this can be treated as a one-dimensional information channel. Directional propagation of both spontaneous and stimulated bursts along the line, imaged with the calcium indicator Fluo-4, revealed the existence of two different propagation velocities. Initially, a small number of neighboring neurons fire, leading to a slow, small and presumably asynchronous wave of activity. The signal then spontaneously develops to encompass much larger and further populations, and is characterized by fast propagation of high-amplitude activity, which is presumed to be synchronous. These results are well described by an existing theoretical framework for propagation based on an integrate-and-fire model.

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Evidence of 8.2-ka event in Southeast Asia inferred from marginal marine sediments off Kallang River Basin, Singapore

10.5194/egusphere-egu21-13902 ◽

2021 ◽

Author(s):

Yama Dixit ◽

Stephen Chua ◽

Yu Ting Yan ◽

Adam Switzer

Keyword(s):

River Basin ◽

Marine Sediments ◽

Large Scale ◽

Southern Oscillation ◽

Signal Propagation ◽

Warm Pool ◽

Seasonal Migration ◽

Monsoon Circulation ◽

Tropical Regions ◽

The North

<p>The Maritime Continent (MC) is located within the Indo-Pacific Warm Pool, which is known as the largest area of warm sea surface temperatures with the highest rainfall on Earth that drives the global atmospheric and hydrologic circulation. The complex climatic system of the MC is controlled by large-scale phenomena such as the seasonal migration of the Intertropical Convergence Zone which causes the northwest and southeast monsoon circulation in the region as well as tropical Indo-Pacific climate phenomena, the Indian Ocean Dipole in the west and the El Ni&#241;o-Southern Oscillation operating to the east of the MC. In addition to interactions of these climate phenomena, their influence varies across the region due to island topography and ocean&#8211;atmosphere fluxes. Despite dedicated efforts, a comprehensive picture of the impacts of abrupt climate events such as the &#8216;8.2 ka event&#8217; during the Holocene on the MC has proved elusive. Here we use sedimentology and stable isotopes of benthic foraminifera collected from the marginal marine sediments off the Kallang River Basin, Singapore to reconstruct paleoenvironmental history of the early-mid Holocene. Owing to the high sedimentation rate (~4.4 mm/yr), the timing and nature of the &#8216;8.2 ka event&#8217; was examined in detail in this region making this an invaluable and unique archive to study up to sub-centennial changes. Comparison of the Kallang record with other high-resolution marine and absolutely dated terrestrial archives speleothems revealed that the timing of the onset of &#8216;8.2 ka event&#8217; in the western IPWP region lags the cooling in the North Atlantic and that of Asian and Indian monsoon failure, by ~100years possibly implying a north-south signal propagation. The termination of the &#8216;8.2 ka event&#8217;, however may have occurred near synchronously between high and low tropical regions at ~7.96ka BP possibly linked via both atmospheric and oceanic processes.</p><p>&#160;</p>

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A Broadband Enhanced Structure-Preserving Reduced-Order Interconnect Macromodeling Method for Large-Scale Equation Sets of Transient Interconnect Circuit Problems

Energies ◽

10.3390/en13215746 ◽

2020 ◽

Vol 13 (21) ◽

pp. 5746

Author(s):

Ning Wang ◽

Huifang Wang ◽

Shiyou Yang

Keyword(s):

Large Scale ◽

Krylov Subspace ◽

Equivalent Circuit Model ◽

Order Reduction ◽

Wide Band ◽

Circuit Model ◽

Band Frequency ◽

Reduced Order ◽

Structure Preserving ◽

Expansion Point

In the transient analysis of an engineering power electronics device, the order of its equivalent circuit model is excessive large. To eliminate this issue, some model order reduction (MOR) methods are proposed in the literature. Compared to other MOR methods, the structure-preserving reduced-order interconnect macromodeling (SPRIM) based on Krylov subspaces will achieve a higher reduction radio and precision for large multi-port Resistor-Capacitor-Inductor (RCL) circuits. However, for very wide band frequency transients, the performance of a Krylov subspace-based MOR method is not satisfactory. Moreover, the selection of the expansion point in this method has not been comprehensively studied in the literature. From this point of view, a broadband enhanced structure-preserving reduced-order interconnect macromodeling (SPRIM) method is proposed to reduce the order of equation sets of a transient interconnect circuit model. In addition, a method is introduced to determine the optimal expansion point at each frequency in the proposed method. The proposed method is validated by the numerical results on a transient problem of an insulated-gate bipolar transistor (IGBT)-based inverter busbar under different exciting conditions.

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Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain

Science Advances ◽

10.1126/sciadv.aat7854 ◽

2019 ◽

Vol 5 (1) ◽

pp. eaat7854 ◽

Cited By ~ 55

Author(s):

Peng Wang ◽

Ru Kong ◽

Xiaolu Kong ◽

Raphaël Liégeois ◽

Csaba Orban ◽

...

Keyword(s):

Large Scale ◽

Meta Analysis ◽

Circuit Model ◽

Model Parameters ◽

Optimization Approach ◽

Functional Magnetic Resonance ◽

Cortical Hierarchy ◽

Out Of Sample ◽

External Stimuli ◽

Recurrent Connections

We considered a large-scale dynamical circuit model of human cerebral cortex with region-specific microscale properties. The model was inverted using a stochastic optimization approach, yielding markedly better fit to new, out-of-sample resting functional magnetic resonance imaging (fMRI) data. Without assuming the existence of a hierarchy, the estimated model parameters revealed a large-scale cortical gradient. At one end, sensorimotor regions had strong recurrent connections and excitatory subcortical inputs, consistent with localized processing of external stimuli. At the opposing end, default network regions had weak recurrent connections and excitatory subcortical inputs, consistent with their role in internal thought. Furthermore, recurrent connection strength and subcortical inputs provided complementary information for differentiating the levels of the hierarchy, with only the former showing strong associations with other macroscale and microscale proxies of cortical hierarchies (meta-analysis of cognitive functions, principal resting fMRI gradient, myelin, and laminar-specific neuronal density). Overall, this study provides microscale insights into a macroscale cortical hierarchy in the dynamic resting brain.

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