Realistic Modeling of Large-Scale Networks: Spatio-temporal Dynamics and Long-Term Synaptic Plasticity in the Cerebellum

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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Increasing frequency of low summer precipitation synchronizes dynamics and compromises metapopulation stability in the Glanville fritillary butterfly

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2015.0173 ◽

2015 ◽

Vol 282 (1806) ◽

pp. 20150173 ◽

Cited By ~ 37

Author(s):

Ayco J. M. Tack ◽

Tommi Mononen ◽

Ilkka Hanski

Keyword(s):

Population Dynamics ◽

Population Change ◽

Temporal Dynamics ◽

Summer Precipitation ◽

Life History Stage ◽

General Effect ◽

Spatial Synchrony ◽

Glanville Fritillary Butterfly ◽

Spatio Temporal

Climate change is known to shift species' geographical ranges, phenologies and abundances, but less is known about other population dynamic consequences. Here, we analyse spatio-temporal dynamics of the Glanville fritillary butterfly ( Melitaea cinxia ) in a network of 4000 dry meadows during 21 years. The results demonstrate two strong, related patterns: the amplitude of year-to-year fluctuations in the size of the metapopulation as a whole has increased, though there is no long-term trend in average abundance; and there is a highly significant increase in the level of spatial synchrony in population dynamics. The increased synchrony cannot be explained by increasing within-year spatial correlation in precipitation, the key environmental driver of population change, or in per capita growth rate. On the other hand, the frequency of drought during a critical life-history stage (early larval instars) has increased over the years, which is sufficient to explain the increasing amplitude and the expanding spatial synchrony in metapopulation dynamics. Increased spatial synchrony has the general effect of reducing long-term metapopulation viability even if there is no change in average metapopulation size. This study demonstrates how temporal changes in weather conditions can lead to striking changes in spatio-temporal population dynamics.

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Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics

Neural Computation ◽

10.1162/neco.2006.18.12.2959 ◽

2006 ◽

Vol 18 (12) ◽

pp. 2959-2993 ◽

Cited By ~ 68

Author(s):

Eduardo Ros ◽

Richard Carrillo ◽

Eva M. Ortigosa ◽

Boris Barbour ◽

Rodrigo Agís

Keyword(s):

Synaptic Plasticity ◽

High Speed ◽

Large Scale ◽

Computational Cost ◽

Brain Structures ◽

Spike Timing ◽

Neuronal Dynamics ◽

Event Driven ◽

Synaptic Dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.

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Diffusion, Buffering, and Binding

Biophysics of Computation ◽

10.1093/oso/9780195104912.003.0017 ◽

1998 ◽

Author(s):

Christof Koch

Keyword(s):

Synaptic Plasticity ◽

Intracellular Calcium ◽

Temporal Dynamics ◽

Critical Concentration ◽

Two Photon Microscopy ◽

Calcium Dependent ◽

Calcium Sensors ◽

Free Intracellular Calcium ◽

Messenger Molecules ◽

Spatio Temporal

In Chap. 9 we introduced calcium ions and alluded to their crucial role in regulating the day-to-day life of neurons. The dynamics of the free intracellular calcium is controlled by a number of physical and chemical processes, foremost among them diffusion and binding to a host of different proteins, which serve as calcium buffers and as calcium sensors or triggers. Whereas buffers simply bind Ca2+ above some critical concentration, releasing it back into the cytoplasm when [Ca2+]i has been reduced below this level, certain proteins— such as calmodulin—change their conformation when they bind with Ca2+ ions, thereby activating or modulating enzymes, ionic channels, or other proteins. The calcium concentration inside the cell not only determines the degree of activation of calcium-dependent potassium currents but—much more importantly—is relevant for determining the changes in structure expressed in synaptic plasticity. As discussed in Chap. 13, it is these changes that are thought to underlie learning. Given the relevance of second messenger molecules, such as Ca2+, IP3, cyclic AMP and others, for the processes underlying growth, sensory adaptation, and the establishment and maintenance of synaptic plasticity, it is crucial that we have some understanding of the role that diffusion and chemical kinetics play in governing the behavior of these substances. Today, we have unprecedented access to the spatio-temporal dynamics of intracellular calcium in individual neurons using fluorescent calcium dyes, such as fura-2 or fluo-3, in combination with confocal or two-photon microscopy in the visible or in the infrared spectrum (Tsien, 1988; Tank et al., 1988; Hernández-Cruz, Sala, and Adams, 1990; Ghosh and Greenberg, 1995).

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Arctic avian predators synchronise their spring migration with the northern progression of snowmelt

Scientific Reports ◽

10.1038/s41598-020-63312-0 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Teja Curk ◽

Ivan Pokrovsky ◽

Nicolas Lecomte ◽

Tomas Aarvak ◽

David F. Brinker ◽

...

Keyword(s):

Large Scale ◽

Temporal Dynamics ◽

The Arctic ◽

Migratory Species ◽

Migration Patterns ◽

Predator Species ◽

Movement Data ◽

Breeding Grounds ◽

Spatio Temporal ◽

Spring Snowmelt

Abstract Migratory species display a range of migration patterns between irruptive (facultative) to regular (obligate), as a response to different predictability of resources. In the Arctic, snow directly influences resource availability. The causes and consequences of different migration patterns of migratory species as a response to the snow conditions remains however unexplored. Birds migrating to the Arctic are expected to follow the spring snowmelt to optimise their arrival time and select for snow-free areas to maximise prey encounter en-route. Based on large-scale movement data, we compared the migration patterns of three top predator species of the tundra in relation to the spatio-temporal dynamics of snow cover. The snowy owl, an irruptive migrant, the rough-legged buzzard, with an intermediary migration pattern, and the peregrine falcon as a regular migrant, all followed, as expected, the spring snowmelt during their migrations. However, the owl stayed ahead, the buzzard stayed on, and the falcon stayed behind the spatio-temporal peak in snowmelt. Although none of the species avoided snow-covered areas, they presumably used snow presence as a cue to time their arrival at their breeding grounds. We show the importance of environmental cues for species with different migration patterns.

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Early alterations of large-scale brain networks temporal dynamics in young children with autism

Communications Biology ◽

10.1038/s42003-021-02494-3 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Aurélie Bochet ◽

Holger Franz Sperdin ◽

Tonia Anahi Rihs ◽

Nada Kojovic ◽

Martina Franchini ◽

...

Keyword(s):

Large Scale ◽

Developmental Stages ◽

Temporal Dynamics ◽

Brain Networks ◽

Autism Spectrum ◽

Brain Network ◽

Clustering Methods ◽

Early Developmental Stages ◽

Spatio Temporal ◽

Early Developmental

AbstractAutism spectrum disorders (ASD) are associated with disruption of large-scale brain network. Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD. Here, we investigated the spatio-temporal dynamics of whole-brain neuronal networks at a subsecond scale in 113 toddlers and preschoolers (66 with ASD) using an EEG microstate approach. We first determined the predominant microstates using established clustering methods. We identified five predominant microstate (labeled as microstate classes A–E) with significant differences in the temporal dynamics of microstate class B between the groups in terms of increased appearance and prolonged duration. Using Markov chains, we found differences in the dynamic syntax between several maps in toddlers and preschoolers with ASD compared to their TD peers. Finally, exploratory analysis of brain–behavioral relationships within the ASD group suggested that the temporal dynamics of some maps were related to conditions comorbid to ASD during early developmental stages.

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Assessing variation in assemblages of large marine fauna off ocean beaches using drones

Marine and Freshwater Research ◽

10.1071/mf18375 ◽

2020 ◽

Vol 71 (1) ◽

pp. 68 ◽

Cited By ~ 12

Author(s):

Brendan P. Kelaher ◽

Andrew P. Colefax ◽

Alejandro Tagliafico ◽

Melanie J. Bishop ◽

Anna Giles ◽

...

Keyword(s):

Temporal Dynamics ◽

Bottlenose Dolphins ◽

Spatial And Temporal Variation ◽

Marine Animals ◽

Marine Fauna ◽

South Wales ◽

Carcharhinus Leucas ◽

Spatio Temporal ◽

Marine Wildlife

The turbulent waters off ocean beaches provide habitat for large marine fauna, including dolphins, sharks, rays, turtles and game fish. Although, historically, these assemblages have proven difficult to quantify, we used a new drone-based approach to assess spatial and temporal variation in assemblages of large marine fauna off four exposed beaches in New South Wales, Australia. In total, 4388 individual large marine animals were identified from 216 drone flights. The most common taxa, bottlenose dolphins (Tursiops spp.) and Australian cownose rays (Rhinoptera neglecta), occurred in 25.5 and 19.9% of flights respectively. White (Carcharodon carcharias), bull (Carcharhinus leucas) and other whaler (Carcharhinus spp.) sharks were observed in <1% of flights. There was significant variation in the structure of assemblages of large fauna among beaches, with those adjacent to riverine estuaries having greater richness and abundance of wildlife. Overall, drone surveys were successful in documenting the spatio-temporal dynamics of an impressive suite of large marine fauna. We contend that emerging drone technology can make a valuable contribution to the ecological information required to ensure the long-term sustainability of sandy-beach ecosystems and associated marine wildlife.

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Spatio-Temporal Dynamics of Feed Grain Demand of Dairy Cows in China

Sustainability ◽

10.3390/su12020663 ◽

2020 ◽

Vol 12 (2) ◽

pp. 663 ◽

Cited By ~ 1

Author(s):

Chao Yang Dong ◽

Bei Bei Ma ◽

Chun Xia LU

Keyword(s):

Dairy Cows ◽

Dairy Cow ◽

Large Scale ◽

Temporal Dynamics ◽

Hot Spot ◽

Market Environment ◽

Relative Level ◽

Resource Endowment ◽

Spatio Temporal ◽

Feeding Technology

As the income of urban and rural residents has increased in recent decades in China, dairy products have become an important part of the Chinese diet. Therefore, keeping up with the growing demand for feed grain for dairy cows is a critical issue of feed grain security. Utilizing traditional statistical and spatial statistical methods, this study analyzes the spatio-temporal dynamics of dairy cow feed grain (DCFG) demand on the provincial, regional, and national levels across China from 1990 to 2016. Additionally, this paper explores the impacts of various factors on the spatio-temporal dynamics of DCFG demand using the Geo-Detector method. The results demonstrate that: (1) the temporal dynamics of DCFG demand can be divided into three stages of slow growth, rapid growth, and high-level stability, and the relative level of DCFG demand in the whole animal husbandry tends to decline; (2) at the regional and national levels, the spatial concentration of high DCFG demand has intensified; in particular, North China was the region where the largest demand for DCFG was localized and was increasing at the highest rate; (3) based on the hot spot analysis of provincial DCFG demand, the high and low demand provinces of DCFG have sharp characteristic contrast from north to south China; (4) the spatio-temporal dynamics of DCFG demand in China were essentially co-affected by the four groups of factors (e.g., resource endowment, feeding scale, feeding technology, and market environment), of which resource endowment and feeding scale were the dominant factors. Therefore, in the future, dairy cow feeding in China should promote grain-saving feeding technology, improve the utilization of forage, expand large-scale feeding, and create a good market environment to ensure the reasonable development and sustainability of DCFG demand.

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Brain network dynamics are hierarchically organized in time

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1705120114 ◽

2017 ◽

Vol 114 (48) ◽

pp. 12827-12832 ◽

Cited By ~ 192

Author(s):

Diego Vidaurre ◽

Stephen M. Smith ◽

Mark W. Woolrich

Keyword(s):

Large Scale ◽

Temporal Dynamics ◽

Brain Network ◽

Specific Measure ◽

Brain Areas ◽

Neuronal Populations ◽

Subject Specific ◽

Large Scale Networks ◽

Network Patterns ◽

The Brain

The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

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