scholarly journals Linear Response of General Observables in Spiking Neuronal Network Models

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 155
Author(s):  
Bruno Cessac ◽  
Ignacio Ampuero ◽  
Rodrigo Cofré
Keyword(s):  
Linear Response ◽  
Network Connectivity ◽  
Network Models ◽  
General Context ◽  
Collective Effect ◽  
Neuronal Dynamics ◽  
The Past ◽  
Spatio Temporal ◽  
External Stimuli ◽  
Spike Dynamics

We establish a general linear response relation for spiking neuronal networks, based on chains with unbounded memory. This relation allow us to predict the influence of a weak amplitude time dependent external stimuli on spatio-temporal spike correlations, from the spontaneous statistics (without stimulus) in a general context where the memory in spike dynamics can extend arbitrarily far in the past. Using this approach, we show how the linear response is explicitly related to the collective effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike train statistics. We illustrate our results with numerical simulations performed over a discrete time integrate and fire model.

Spatio-temporal Variation Characteristics of Surface Net Radiation in China over the Past 50 Years

2013 ◽  
Vol 15 (1) ◽  
pp. 1 ◽  
Author(s):  
Yangzi GAO ◽  
Honglin HE ◽  
Li ZHANG ◽  
Qianqian LU ◽  
Guirui YU ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Net Radiation ◽  
The Past ◽  
Variation Characteristics ◽  
Spatio Temporal

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

scholarly journals Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in Kenya

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Peter M. Macharia ◽  
Emanuele Giorgi ◽  
Abdisalan M. Noor ◽  
Ejersa Waqo ◽  
Rebecca Kiptui ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Malaria Control ◽  
Temporal Analysis ◽  
The Past ◽  
The Future ◽  
Spatio Temporal

scholarly journals Integration, coincidence detection and resonance in networks of spiking neurons expressing Gamma oscillations and asynchronous states

2021 ◽  
Vol 17 (9) ◽  
pp. e1009416
Author(s):  
Eduarda Susin ◽  
Alain Destexhe
Keyword(s):  
Cerebral Cortex ◽  
Pyramidal Neurons ◽  
Network Models ◽  
Cell Types ◽  
Gamma Oscillations ◽  
Coincidence Detection ◽  
Biophysical Models ◽  
Firing Mode ◽  
Different Cell Types ◽  
External Stimuli

Gamma oscillations are widely seen in the awake and sleeping cerebral cortex, but the exact role of these oscillations is still debated. Here, we used biophysical models to examine how Gamma oscillations may participate to the processing of afferent stimuli. We constructed conductance-based network models of Gamma oscillations, based on different cell types found in cerebral cortex. The models were adjusted to extracellular unit recordings in humans, where Gamma oscillations always coexist with the asynchronous firing mode. We considered three different mechanisms to generate Gamma, first a mechanism based on the interaction between pyramidal neurons and interneurons (PING), second a mechanism in which Gamma is generated by interneuron networks (ING) and third, a mechanism which relies on Gamma oscillations generated by pacemaker chattering neurons (CHING). We find that all three mechanisms generate features consistent with human recordings, but that the ING mechanism is most consistent with the firing rate change inside Gamma bursts seen in the human data. We next evaluated the responsiveness and resonant properties of these networks, contrasting Gamma oscillations with the asynchronous mode. We find that for both slowly-varying stimuli and precisely-timed stimuli, the responsiveness is generally lower during Gamma compared to asynchronous states, while resonant properties are similar around the Gamma band. We could not find conditions where Gamma oscillations were more responsive. We therefore predict that asynchronous states provide the highest responsiveness to external stimuli, while Gamma oscillations tend to overall diminish responsiveness.

scholarly journals Spatio-temporal variations of the major meteorological disasters and its response to climate change in Henan Province during the past two millennia

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12365
Author(s):  
Xiang Li ◽  
Hui Lu ◽  
Zhaokang Zhang ◽  
Wei Xing
Keyword(s):  
Low Temperature ◽  
Correlation Analysis ◽  
Natural Disasters ◽  
Temporal Variations ◽  
Henan Province ◽  
Historical Documents ◽  
The Past ◽  
Meteorological Disaster ◽  
Spatio Temporal ◽  
Meteorological Disasters

In China, historical documents have recorded large quantities of information related to natural disasters, and these disasters have had long-lasting effects on economic and social activities. Understanding the occurrence of the natural disasters and their spatio-temporal variation characters is crucial for sustainable of our society. Therefore, based on the collection and collation of historical documents, and adopting mathematical statistics, Kriging interpolation, correlation analysis and other methods, we systematically explored the meteorological disasters in Henan Province during the past two millennia in analyzing their spatio-temporal distribution characters and driving forces. The results demonstrate that there were five major types of meteorological disasters in Henan Province, including drought, flood, hails, low temperature and frost and insect pests, which presented obvious spatio-temporal variations and have occurred frequently during the past two millennia. According to the historical documents, the major meteorological disasters occurred 1,929 times in Henan from 221 BCE to 2000 CE. On the whole, the disaster frequency show that the occurrence cycle of the meteorological disasters has obvious changes, which mainly occurred in the middle and late stages during the past two millennia, especially after 1300 CE. Furthermore, we also find that the variation of meteorological disaster events is consistent with the variation of temperature in eastern China and the frequency of meteorological disaster increases in the cold period, but decreases in the warm period. In addition, there are obvious differences in the spatial distribution of the major meteorological disaster, which were mainly distributed in the northwest and southern part region of the Henan Province before 1911 CE. While after 1911 CE, the northern and southeastern parts were the meteorological disaster-prone areas in this region during this period. Spatial correlation analysis of each meteorological disaster before and after 1911 CE points out the droughts disaster frequency-occurring district has transferred in different periods, while the hail and low temperature and frost disasters just have a smaller transferred during these two periods. Conversely, the frequency-occurring districts of floods and insect pest disasters have no obviously transferred in different periods. These results can provide an important scientific basis for governmental decision makers and local people to prevent and mitigate meteorological disaster in the future.

scholarly journals Within-subject changes in network connectivity occur during an episode of depression: evidence from a longitudinal analysis of social media posts.

2020 ◽  
Author(s):  
Sean Kelley ◽  
Claire Gillan
Keyword(s):  
Social Media ◽  
Network Theory ◽  
Depressive Episode ◽  
Network Connectivity ◽  
Depression Symptoms ◽  
Depression Severity ◽  
Social Media Data ◽  
The Past ◽  
Text Features ◽  
Media Data

Background: Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. This is thought to occur because positive feedback loops between symptoms trigger cascades of further symptom activation. Increasing evidence suggests that depression network connectivity is therefore a risk factor for transitioning and sustaining a depressive state. However, much of the evidence comes from cross-sectional studies that estimate networks across groups, rather than within individuals. We used a novel method to construct personalised depression-relevant networks from social media data to test the hypothesis that network connectivity is linked to depression severity and increases during a depressive episode. Methods: We analysed Twitter data from 946 participants who retrospectively reported the dates of any depressive episodes they experienced in the past 12 months and self-reported current depressive symptom severity. Daily Tweets were subjected to textual analysis, which allowed us to construct personalised, within-subject, depression networks, based on 9 a priori text features previously associated with depression severity. We tested for associations between network connectivity and current depression severity and, in participants who experienced a depressive episode in the past year, we tested if connectivity increased during the dates of a self-reported episode (N = 286). Results: Significant bivariate associations were found between current depression severity and 8/9 of the text features examined. In line with our hypothesis, individuals with greater depression severity had a significantly higher overall network connectivity between these features than those with lesser severity (β = 0.008, SE = 0.003, p = 0.002). Importantly, we observed within-subject changes in overall network connectivity associated with the dates of a self-reported depressive episode (β = 0.03, SE = 0.009, p = 0.005). Conclusions: The connectivity within personalized depression networks changes dynamically with changes in current depression symptoms. Social media data provides a fruitful, albeit noisy, source of data to test key within-subject predictions of network theory.

scholarly journals Comparison of Land Surface Temperature During and Before the Emergence of Covid-19 using Modis Imagery in Wuhan City, China

2020 ◽  
Vol 34 (1) ◽  
Author(s):  
Hamim Zaky Hadibasyir ◽  
Seftiawan Samsu Rijal ◽  
Dewi Ratna Sari
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Anthropogenic Activities ◽  
Temporal Analysis ◽  
The Past ◽  
Modis Imagery ◽  
Rapid Transmission ◽  
The Mean ◽  
Spatio Temporal

Coronavirus disease (COVID-19) was firstly identified in Wuhan, China. By 23rd January 2020, China’s Government made a decision to execute lockdown policy in Wuhan due to the rapid transmission of COVID-19. It is essential to investigate the land surface temperature (LST) dynamics due to changes in level of anthropogenic activities. Therefore, this study aims (1) to investigate mean LST differences between during, i.e., December 2019 to early March 2020, and before the emergence of COVID-19 in Wuhan; (2) to conduct spatio-temporal analysis of mean LST with regards to lockdown policy; and (3) to examine mean LST differences for each land cover type. MODIS data consist of MOD11A2 and MCD12Q1 were employed. The results showed that during the emergence of COVID-19 with lockdown policy applied, the mean LST was lower than the mean LST of the past three years on the same dates. Whereas, during the emergence of COVID-19 without lockdown policy applied, the mean LST was relatively higher than the mean LST of the past three years. In addition, the mean LST of built-up areas experienced the most significant differences between during the emergence of COVID-19 with lockdown policy applied in comparison to the average of the past three years.

scholarly journals Spatio-temporal heterogeneity of water quality(2010-2011) and succession patterns in Lake Bosten during the past 50 years

2011 ◽  
Vol 23 (6) ◽  
pp. 837-846 ◽  
Author(s):  
XIE Guijuan ◽  
◽  
ZHANG Jianping ◽  
TANG Xiangming ◽  
CAI Yongping ◽  
...  
Keyword(s):  
Water Quality ◽  
Temporal Heterogeneity ◽  
The Past ◽  
Spatio Temporal

Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons

2010 ◽  
Vol 22 (5) ◽  
pp. 1272-1311 ◽  
Author(s):  
Lars Büsing ◽  
Benjamin Schrauwen ◽  
Robert Legenstein
Keyword(s):  
Analog Circuits ◽  
Memory Function ◽  
Mean Field ◽  
Network Connectivity ◽  
Network Models ◽  
Reservoir Computing ◽  
Computational Performance ◽  
Kernel Quality ◽  
The One ◽  
Long Timescales

Reservoir computing (RC) systems are powerful models for online computations on input sequences. They consist of a memoryless readout neuron that is trained on top of a randomly connected recurrent neural network. RC systems are commonly used in two flavors: with analog or binary (spiking) neurons in the recurrent circuits. Previous work indicated a fundamental difference in the behavior of these two implementations of the RC idea. The performance of an RC system built from binary neurons seems to depend strongly on the network connectivity structure. In networks of analog neurons, such clear dependency has not been observed. In this letter, we address this apparent dichotomy by investigating the influence of the network connectivity (parameterized by the neuron in-degree) on a family of network models that interpolates between analog and binary networks. Our analyses are based on a novel estimation of the Lyapunov exponent of the network dynamics with the help of branching process theory, rank measures that estimate the kernel quality and generalization capabilities of recurrent networks, and a novel mean field predictor for computational performance. These analyses reveal that the phase transition between ordered and chaotic network behavior of binary circuits qualitatively differs from the one in analog circuits, leading to differences in the integration of information over short and long timescales. This explains the decreased computational performance observed in binary circuits that are densely connected. The mean field predictor is also used to bound the memory function of recurrent circuits of binary neurons.

scholarly journals Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology

2010 ◽  
Vol 365 (1550) ◽  
pp. 2303-2312 ◽  
Author(s):  
Mark Hebblewhite ◽  
Daniel T. Haydon
Keyword(s):  
Animal Ecology ◽  
Animal Movement ◽  
Gps Telemetry ◽  
Long Distance ◽  
Location Data ◽  
The Past ◽  
Movement Data ◽  
Migratory Songbirds ◽  
Spatio Temporal ◽  
Gps Devices

In the past decade, ecologists have witnessed vast improvements in our ability to collect animal movement data through animal-borne technology, such as through GPS or ARGOS systems. However, more data does not necessarily yield greater knowledge in understanding animal ecology and conservation. In this paper, we provide a review of the major benefits, problems and potential misuses of GPS/Argos technology to animal ecology and conservation. Benefits are obvious, and include the ability to collect fine-scale spatio-temporal location data on many previously impossible to study animals, such as ocean-going fish, migratory songbirds and long-distance migratory mammals. These benefits come with significant problems, however, imposed by frequent collar failures and high cost, which often results in weaker study design, reduced sample sizes and poorer statistical inference. In addition, we see the divorcing of biologists from a field-based understanding of animal ecology to be a growing problem. Despite these difficulties, GPS devices have provided significant benefits, particularly in the conservation and ecology of wide-ranging species. We conclude by offering suggestions for ecologists on which kinds of ecological questions would currently benefit the most from GPS/Argos technology, and where the technology has been potentially misused. Significant conceptual challenges remain, however, including the links between movement and behaviour, and movement and population dynamics.

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