scholarly journals Cortex-Like Complex Systems: What Occurs Within?

2021 ◽  
Vol 7 ◽  
Author(s):  
Peter Grindrod ◽  
Christopher Lester
Keyword(s):  
Complex Systems ◽  
Large Scale ◽  
Time Lags ◽  
Human Cerebral Cortex ◽  
Short Term ◽  
Wide Range ◽  
Strongly Connected ◽  
Generic Class ◽  
The Many ◽  
Large Scale Simulations

We consider cortex-like complex systems in the form of strongly connected, directed networks-of-networks. In such a network, there are spiking dynamics at each of the nodes (modelling neurones), together with non-trivial time-lags associated with each of the directed edges (modelling synapses). The connections of the outer network are sparse, while the many inner networks, called modules, are dense. These systems may process various incoming stimulations by producing whole-system dynamical responses. We specifically discuss a generic class of systems with up to 10 billion nodes simulating the human cerebral cortex. It has recently been argued that such a system’s responses to a wide range of stimulations may be classified into a number of latent, internal dynamical modes. The modes might be interpreted as focussing and biasing the system’s short-term dynamical system responses to any further stimuli. In this work, we illustrate how latent modes may be shown to be both present and significant within very large-scale simulations for a wide and appropriate class of complex systems. We argue that they may explain the inner experience of the human brain.

scholarly journals Polygenic Scores for Cognitive Abilities and their Association with Different Aspects of General Intelligence – a Deep Phenotyping Approach

2020 ◽  
Author(s):  
Erhan Genç ◽  
Caroline Schlüter ◽  
Christoph Fraenz ◽  
Larissa Arning ◽  
Huu Phuc Nguyen ◽  
...  
Keyword(s):  
Cognitive Abilities ◽  
Large Scale ◽  
Short Term Memory ◽  
Life Outcomes ◽  
Short Term ◽  
Polygenic Scores ◽  
Wide Range ◽  
Dna Variants ◽  
Trait Prediction ◽  
Deep Phenotyping

AbstractIntelligence is a highly polygenic trait and GWAS have identified thousands of DNA variants contributing with small effects. Polygenic scores (PGS) can aggregate those effects for trait prediction in independent samples. As large-scale light-phenotyping GWAS operationalized intelligence as performance in rather superficial tests, the question arises which intelligence facets are actually captured. We used deep-phenotyping to investigate the molecular determinantes of individual differences in cognitive ability. We therefore studied the association between PGS of educational attainment (EA-PGS) and intelligence (IQ-PGS) with a wide range of intelligence facets in a sample of 320 healthy adults. EA-PGS and IQ-PGS had the highest incremental R2s for general (3.25%; 1.78%), verbal (2.55%; 2.39%) and numerical intelligence (2.79%; 1.54%) and the weakest for non-verbal intelligence (0.50%; 0.19%) and short-term memory (0.34%; 0.22%). These results indicate that PGS derived from light-phenotyping GWAS do not reflect different facets of intelligence equally well, and thus should not be interpreted as genetic indicators of intelligence per se. The findings refine our understanding of how PGS are related to other traits or life outcomes.

scholarly journals Artist: fast radiative transfer for large-scale simulations of the epoch of reionization

2019 ◽  
Vol 489 (4) ◽  
pp. 5594-5611 ◽  
Author(s):  
Margherita Molaro ◽  
Romeel Davé ◽  
Sultan Hassan ◽  
Mario G Santos ◽  
Kristian Finlator
Keyword(s):  
Radiative Transfer ◽  
Large Scale ◽  
Power Spectra ◽  
Physical Parameters ◽  
Speed Of Light ◽  
Photon Propagation ◽  
Wide Range ◽  
Gas Ionization ◽  
Excursion Set ◽  
Large Scale Simulations

ABSTRACT We introduce the ‘Asymmetric Radiative Transfer In Shells Technique’ (artist), a new method for photon propagation on large scales that explicitly conserves photons, propagates photons at the speed of light, approximately accounts for photon directionality, and closely reproduces results of more detailed radiative transfer (RT) methods. Crucially, it is computationally fast enough to evolve the large cosmological volumes required to predict the 21cm power spectrum on scales that will be probed by future experiments targeting the epoch of reionization (EoR). Most seminumerical models aimed at predicting the EoR 21cm signal on these scales use an excursion set formalism (ESF) to model the gas ionization, which achieves computational viability by making a number of approximations. While artist is still roughly two orders of magnitude slower than ESF, it does allow to model the EoR without the need for such approximations. This is particularly important when considering a wide range of reionization scenarios for which artist would help limit the assumptions made. By implementing our RT method within the seminumerical code simfast21, we show that Artist predicts a significantly different evolution for the EoR ionization field compared to the code’s native ESF. In particular, artist predicts up to a factor of two difference in the power spectra, depending on the physical parameters assumed. Its application to large-scale EoR simulations will therefore allow more physically motivated constraints to be obtained for key EoR parameters. In particular, it will remove the need for the artificial rescaling of the escape fraction.

scholarly journals Profiles of Galaxy Clusters in Cosmological Scenarios

1987 ◽  
Vol 117 ◽  
pp. 287-287
Author(s):  
Michael J. West ◽  
Avishai Dekel ◽  
Augustus Oemler
Keyword(s):  
High Resolution ◽  
Galaxy Clusters ◽  
Hierarchical Clustering ◽  
Large Scale ◽  
Power Spectra ◽  
Clusters Of Galaxies ◽  
Spatial Symmetry ◽  
Abell Clusters ◽  
Wide Range ◽  
Large Scale Simulations

We have studied the properties of rich clusters of galaxies in various cosmological scenarios by comparing high resolution N-body simulations with observations of Abell clusters. The clusters have been simulated in two steps. First, protoclusters are identified in large-scale simulations which represent a wide range of cosmological scenarios (hierarchical clustering, pancake scenarios, and hybrids of the two, spanning a range of power spectra). Then the region around each protocluster is simulated with high resolution, the particles representing L* galaxies. The protoclusters have no spatial symmetry built into them initially. The final clusters are still dynamically young, and of moderate densities, which should be representative of Abell clusters of richness classes 1 and 2.

Cattle

1984 ◽  
Vol 8 ◽  
pp. 83-89
Author(s):  
Ian B. Howie
Keyword(s):  
Large Scale ◽  
Production Systems ◽  
Farming System ◽  
Small Scale ◽  
Market Forces ◽  
Poultry Production ◽  
Short Term ◽  
Fully Integrated ◽  
Beef Producers ◽  
The Many

Matching production to the markets for meat makes the assumption that individual producers can have an influence on market forces. This may well apply nowadays to some of the very large scale poultry production units but, individually, beef producers can have little if any influence on the marketing scene. Although there are farmers who produce several hundred fat cattle a year, the bulk of the beef produced comes from fairly small scale producers. Much of beef production is on a fairly haphazard basis with little or no recording or budgeting.Nevertheless, small scale producers and feeders who move in and out of the market can exploit local or short-term, favourable, market fluctuations and, with skilful buying and selling, make good profits on a quick turnover. Larger scale producers who have pre-planned fully integrated production systems cannot react as quickly to any great extent to short-term marketing opportunities. I regard marketing as only one of the many variable factors to be taken into account when planning a beef enterprise within a whole farming system, in which it is likely to be one of a number of enterprises which have to be kept in balance.

scholarly journals On the maximal quantity of processed information in the physical eschatological context

2001 ◽  
pp. 53-56 ◽  
Author(s):  
M.M. Cirkovic ◽  
M. Radujkov
Keyword(s):  
Complex Systems ◽  
Quantitative Estimate ◽  
Large Scale ◽  
Point Of View ◽  
Computing Power ◽  
Degree Of Confidence ◽  
Large Scale Simulations

An estimate of the maximal computing power available to advanced extraterrestrial or future (post)human civilizations is presented. It is shown that the fundamental thermodynamical considerations may lead to a quantitative estimate of the largest quantity of information to be processed by conceivable computing devices. This issue is interesting from the point of view of physical eschatology, as well as general futurological topics, like the degree of confidence in long-term physical predictions or viability of the large-scale simulations of complex systems.

scholarly journals LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

2020 ◽  
Vol 12 (1) ◽  
pp. 349 ◽  
Author(s):  
Alessandro Crivellari ◽  
Euro Beinat
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Human Mobility ◽  
Short Term ◽  
Motion Patterns ◽  
Motion Data ◽  
Crowd Management ◽  
Wide Range ◽  
Traditional Approaches

The increasing availability of trajectory recordings has led to the mining of a massive amount of historical track data, allowing for a better understanding of travel behaviors by revealing meaningful motion patterns. In the context of human mobility analysis, the problem of motion prediction assumes a central role and is beneficial for a wide range of applications, including for touristic purposes, such as personalized services or targeted recommendations, and sustainability studies related to crowd management and resource redistribution. This paper tackles a particular case of the trajectory prediction problem, focusing on large-scale mobility traces of short-term foreign tourists. These sparse trajectories, short and non-repetitive, lack spatial and temporal regularity, making prediction analysis based on individual historical motion data unreliable. To face this issue, we hereby propose a deep learning-based approach, taking into account the collective mobility of tourists over the territory. The underlying semantics of motion patterns are captured by means of a long short-term memory (LSTM) neural network model trained on pre-processed location sequences, aiming to predict the next visited place in the trajectory. We tested the methodology on a real-world big dataset, demonstrating its higher feasibility with respect to traditional approaches.

scholarly journals Time-coded neurotransmitter release at excitatory and inhibitory synapses

2016 ◽  
Vol 113 (8) ◽  
pp. E1108-E1115 ◽  
Author(s):  
Serafim Rodrigues ◽  
Mathieu Desroches ◽  
Martin Krupa ◽  
Jesus M. Cortes ◽  
Terrence J. Sejnowski ◽  
...  
Keyword(s):  
Neurotransmitter Release ◽  
Large Scale ◽  
Snare Complex ◽  
Inhibitory Synapses ◽  
Short Term ◽  
Asynchronous Release ◽  
Chemical Synapses ◽  
Large Scale Simulations ◽  
Input Spike ◽  
Excitatory And Inhibitory Synapses

Communication between neurons at chemical synapses is regulated by hundreds of different proteins that control the release of neurotransmitter that is packaged in vesicles, transported to an active zone, and released when an input spike occurs. Neurotransmitter can also be released asynchronously, that is, after a delay following the spike, or spontaneously in the absence of a stimulus. The mechanisms underlying asynchronous and spontaneous neurotransmitter release remain elusive. Here, we describe a model of the exocytotic cycle of vesicles at excitatory and inhibitory synapses that accounts for all modes of vesicle release as well as short-term synaptic plasticity (STSP). For asynchronous release, the model predicts a delayed inertial protein unbinding associated with the SNARE complex assembly immediately after vesicle priming. Experiments are proposed to test the model’s molecular predictions for differential exocytosis. The simplicity of the model will also facilitate large-scale simulations of neural circuits.

scholarly journals Ultra-Short-Term Multistep Prediction of Wind Power Based on Representative Unit Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Mao Yang ◽  
Lei Liu ◽  
Yang Cui ◽  
Xin Su
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Power Grid ◽  
Support Vector ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction ◽  
Wide Range

With the continuous expansion of wind power grid scale, wind power prediction is an important means to reduce the adverse impact of large-scale grid integration on power grid: the higher prediction accuracy, the better safety, and economy of grid operation. The existing research shows that the quality of input sample data directly affects the accuracy of wind power prediction. By the analysis of measured power data in wind farms, this paper proposes an ultra-short-term multistep prediction model of wind power based on representative unit method, which can fully excavate data information and select reasonable data samples. It uses the similarity measure of time series in data mining, spectral clustering, and correlation coefficient to select the representative units. The least squares support vector machine (LSSVM) model is used as a prediction model for outputs of the representative units. The power of the whole wind farm is obtained by statistical upscaling method. And the number of representative units has a certain impact on prediction accuracy. The case study shows that this method can effectively improve the prediction accuracy, and it can be used as pretreatment method of data. It has a wide range of adaptability.

scholarly journals Experience of short term earthquake precursors with VLF–VHF electromagnetic emissions

2003 ◽  
Vol 3 (3/4) ◽  
pp. 217-228 ◽  
Author(s):  
K. Eftaxias ◽  
P. Kapiris ◽  
J. Polygiannakis ◽  
A. Peratzakis ◽  
J. Kopanas ◽  
...  
Keyword(s):  
Earthquake Prediction ◽  
Large Scale ◽  
Field Measurements ◽  
Short Term ◽  
Wide Range ◽  
Electromagnetic Emissions ◽  
Electro Magnetic ◽  
Focal Zone

Abstract. Electromagnetic anomalies (EMA) covering a wide range of frequencies from ULF, VLF up to VHF have been observed before recent destructive earthquakes in continental Greece. We show that the features of these signals are possibly correlated with the fault model characteristics of the associated earthquake and with the degree of geotectonic heterogeneity within the focal zone. The time evolution of these electromagnetic sequences reveals striking similarities to that observed in laboratory acoustic and electromagnetic emissions during different stages of failure preparation process in rocks. If we consider that the same dynamics governs the large-scale earthquakes and the microscopic scale sample rheological structure, the results of this analysis suggest that the recorded EMA might reflect the nucleation phase of the associated impending earthquake. We focus on the rise of the statistical view of earthquakes. We find electro-magnetic fingerprints of an underlying critical mechanism. Finally, we conclude that it is useful to combine ULF and VLF-VHF field measurements in an attempt to enhance the understanding of the physics behind these observations and thus to improve the quality of earthquake prediction. Further, the identification of an EMA as a seismogenic one supports the characterization of a sequence of shocks as foreshocks at the time they occur, further helping the earthquake prediction effort.

scholarly journals From Dark to Light: The Many Shades of Sharing Misinformation Online

2021 ◽  
Vol 9 (1) ◽  
pp. 134-143 ◽  
Author(s):  
Miriam J. Metzger ◽  
Andrew J. Flanagin ◽  
Paul Mena ◽  
Shan Jiang ◽  
Christo Wilson
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Language Processing ◽  
Large Scale ◽  
Future Research ◽  
Online Networks ◽  
Wide Range ◽  
Report Data ◽  
Preliminary Study ◽  
The Many

Research typically presumes that people believe misinformation and propagate it through their social networks. Yet, a wide range of motivations for sharing misinformation might impact its spread, as well as people’s belief of it. By examining research on motivations for sharing news information generally, and misinformation specifically, we derive a range of motivations that broaden current understandings of the sharing of misinformation to include factors that may to some extent mitigate the presumed dangers of misinformation for society. To illustrate the utility of our viewpoint we report data from a preliminary study of people’s dis/belief reactions to misinformation shared on social media using natural language processing. Analyses of over 2,5 million comments demonstrate that misinformation on social media is often disbelieved. These insights are leveraged to propose directions for future research that incorporate a more inclusive understanding of the various motivations and strategies for sharing misinformation socially in large-scale online networks.

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