Toward a quantitative description of large-scale neocortical dynamic function and EEG

2000 ◽  
Vol 23 (3) ◽  
pp. 371-398 ◽  
Author(s):  
Paul L. Nunez
Keyword(s):  
Neural Networks ◽  
Conceptual Framework ◽  
Large Scale ◽  
Spatial Scales ◽  
Global Dynamics ◽  
Global Mode ◽  
Local Networks ◽  
Decay Times ◽  
Neocortical Dynamics ◽  
Brain States

A general conceptual framework for large-scale neocortical dynamics based on data from many laboratories is applied to a variety of experimental designs, spatial scales, and brain states. Partly distinct, but interacting local processes (e.g., neural networks) arise from functional segregation. Global processes arise from functional integration and can facilitate (top down) synchronous activity in remote cell groups that function simultaneously at several different spatial scales. Simultaneous local processes may help drive (bottom up) macroscopic global dynamics observed with electroencephalography (EEG) or magnetoencephalography (MEG).A local/global dynamic theory that is consistent with EEG data and the proposed conceptual framework is outlined. This theory is neutral about properties of neural networks embedded in macroscopic fields, but its global component makes several qualitative and semiquantitative predictions about EEG measures of traveling and standing wave phenomena. A more general “metatheory” suggests what large-scale quantitative theories of neocortical dynamics may be like when more accurate treatment of local and nonlinear effects is achieved.The theory describes the dynamics of excitatory and inhibitory synaptic action fields. EEG and MEG provide large-scale estimates of modulation of these synaptic fields around background levels. Brain states are determined by neuromodulatory control parameters. Purely local states are dominated by local feedback gains and rise and decay times of postsynaptic potentials. Dominant local frequencies vary with brain region. Other states are purely global, with moderate to high coherence over large distances. Multiple global mode frequencies arise from a combination of delays in corticocortical axons and neocortical boundary conditions. Global frequencies are identical in all cortical regions, but most states involve dynamic interactions between local networks and the global system. EEG frequencies may involve a “matching” of local resonant frequencies with one or more of the many, closely spaced global frequencies.

scholarly journals Interlocking Firm Networks in the German Knowledge Economy. On Local Networks and Global Connectivity

2011 ◽  
Vol 69 (3) ◽  
pp. 161-174 ◽  
Author(s):  
Stefan Lüthi ◽  
Alain Thierstein ◽  
Michael Bentlage
Keyword(s):  
Knowledge Economy ◽  
Large Scale ◽  
Spatial Scales ◽  
High Tech ◽  
Political Debate ◽  
Urban Hierarchy ◽  
Local Networks ◽  
Metropolitan Regions ◽  
City Regions ◽  
Knowledge Intensive

Abstract The knowledge economy is a key driver of spatial development in metropolitan regions. A relational perspective on its business activities emphasizes the importance of knowledge-intensive firms and their networking strategies. The aim of this paper is to analyse the spatial networking patterns created by the interaction of knowledge-intensive firms and to place these activities in the theoretical context of the knowledge economy. Our central question is which large-scale interlocking networks and functional urban hierarchies are produced by Advanced Producer Services and High-Tech firms located in Germany. The intra-firm locational networks of these companies are analysed on three spatial scales: global, national and regional. The empirical findings show that the functional urban hierarchy in the German city system proves to be steeper than is claimed by the political debate on German Mega-City Regions.

scholarly journals Time Scales and Spatial Patterns of Passive Ocean–Atmosphere Decay Modes*

2008 ◽  
Vol 21 (10) ◽  
pp. 2187-2203 ◽  
Author(s):  
Benjamin R. Lintner ◽  
J. David Neelin
Keyword(s):  
Spatial Scale ◽  
Time Scales ◽  
Decay Time ◽  
Time Scale ◽  
Large Scale ◽  
Spatial Scales ◽  
Scale Dependence ◽  
Decay Modes ◽  
Decay Times ◽  
The Tropics

Abstract The decay characteristics of a mixed layer ocean passively coupled to an atmospheric model are important to the response of the climate system to stochastic or external forcing. Two salient features of such decay—the spatial-scale dependence of sea surface temperature anomaly (SSTA) decay time scales and the spatial inhomogeneities of SSTA decay modes—are addressed using intermediate-level complexity and simple analytic models of the tropical atmosphere. As expected, decay time scales increase with the spatial extent of the SSTA. Most modes decay rapidly—with characteristic decay times of 50–100 days for a 50-m mixed layer—with the decay determined by local surface flux adjustment. Only those modes with spatial scales approaching or larger than the tropical basin scale exhibit decay time scales distinctively longer than the local decay, with the decay time scale of the most slowly decaying mode of the order of 250–300 days in the tropics (500 days globally). Simple analytic prototypes of the spatial-scale dependence and the effect of basic-state inhomogeneities, especially the impact of nonconvecting regions, elucidate these results. Horizontal energy transport sets the transition between fast, essentially local, decay time scales and the slower decay at larger spatial scales; within the tropics, efficient wave dynamics accounts for the small number of slowly decaying modes. Inhomogeneities in the basic-state climate, such as the presence or absence of mean tropical deep convection, strongly impact large-scale SSTA decay characteristics. For nonconvecting regions, SSTA decay is slow because evaporation is limited by relatively slow moisture divergence. The separation of convecting- and nonconvecting-region decay times and the closeness of the slower nonconvecting-region decay time scale to the most slowly decaying modes cause a blending between local nonconvecting modes and the large-scale modes, resulting in pronounced spatial inhomogeneity in the slow decay modes.

A Developmental Method for Evolving Large-Scale Spiking Neural Networks

2012 ◽  
Vol 35 (12) ◽  
pp. 2633 ◽  
Author(s):  
Xiang-Hong LIN ◽  
Tian-Wen ZHANG ◽  
Gui-Cang ZHANG
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spiking Neural Networks

scholarly journals Analysis of 220 Years of Floodplain Population Dynamics in the US at Different Spatial Scales

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 141
Author(s):  
Firoza Akhter ◽  
Maurizio Mazzoleni ◽  
Luigia Brandimarte
Keyword(s):  
Population Dynamics ◽  
Large Scale ◽  
Spatial Scales ◽  
National Level ◽  
State Level ◽  
Economic Activities ◽  
Protection Measures ◽  
Management Planning ◽  
The Us ◽  
Over Time

In this study, we explore the long-term trends of floodplain population dynamics at different spatial scales in the contiguous United States (U.S.). We exploit different types of datasets from 1790–2010—i.e., decadal spatial distribution for the population density in the US, global floodplains dataset, large-scale data of flood occurrence and damage, and structural and nonstructural flood protection measures for the US. At the national level, we found that the population initially settled down within the floodplains and then spread across its territory over time. At the state level, we observed that flood damages and national protection measures might have contributed to a learning effect, which in turn, shaped the floodplain population dynamics over time. Finally, at the county level, other socio-economic factors such as local flood insurances, economic activities, and socio-political context may predominantly influence the dynamics. Our study shows that different influencing factors affect floodplain population dynamics at different spatial scales. These facts are crucial for a reliable development and implementation of flood risk management planning.

scholarly journals Insectivorous songbirds as early indicators of future defoliation by spruce budworm

2021 ◽  
Author(s):  
Marion Germain ◽  
Daniel Kneeshaw ◽  
Louis De Grandpré ◽  
Mélanie Desrochers ◽  
Patrick M. A. James ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Spruce Budworm ◽  
Detection Methods ◽  
Maximum Probability ◽  
Point Count ◽  
Probability Of Presence ◽  
Time Lagged ◽  
One Year ◽  
Local Sampling

Abstract Context Although the spatiotemporal dynamics of spruce budworm outbreaks have been intensively studied, forecasting outbreaks remains challenging. During outbreaks, budworm-linked warblers (Tennessee, Cape May, and bay-breasted warbler) show a strong positive response to increases in spruce budworm, but little is known about the relative timing of these responses. Objectives We hypothesized that these warblers could be used as sentinels of future defoliation of budworm host trees. We examined the timing and magnitude of the relationships between defoliation by spruce budworm and changes in the probability of presence of warblers to determine whether they responded to budworm infestation before local defoliation being observed by standard detection methods. Methods We modelled this relationship using large-scale point count surveys of songbirds and maps of cumulative time-lagged defoliation over multiple spatial scales (2–30 km radius around sampling points) in Quebec, Canada. Results All three warbler species responded positively to defoliation at each spatial scale considered, but the timing of their response differed. Maximum probability of presence of Tennessee and Cape May warbler coincided with observations of local defoliation, or provided a one year warning, making them of little use to guide early interventions. In contrast, the probability of presence of bay-breasted warbler consistently increased 3–4 years before defoliation was detectable. Conclusions Early detection is a critical step in the management of spruce budworm outbreaks and rapid increases in the probability of presence of bay-breasted warbler could be used to identify future epicenters and target ground-based local sampling of spruce budworm.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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