Traveling Waves of Excitation in Neural Field Models: Equivalence of Rate Descriptions and Integrate-and-Fire Dynamics

2002 ◽  
Vol 14 (7) ◽  
pp. 1651-1667 ◽  
Author(s):  
Daniel Cremers ◽  
Andreas V. M. Herz
Keyword(s):  
Traveling Waves ◽  
Neural Activity ◽  
Large Scale ◽  
Plane Waves ◽  
Network Connectivity ◽  
Microscopic Level ◽  
Mathematical Framework ◽  
Fire Dynamics ◽  
Integrate And Fire ◽  
Quantitative Results

Field models provide an elegant mathematical framework to analyze large-scale patterns of neural activity. On the microscopic level, these models are usually based on either a firing-rate picture or integrate-andfire dynamics. This article shows that in spite of the large conceptual differences between the two types of dynamics, both generate closely related plane-wave solutions. Furthermore, for a large group of models, estimates about the network connectivity derived from the speed of these plane waves only marginally depend on the assumed class of microscopic dynamics. We derive quantitative results about this phenomenon and discuss consequences for the interpretation of experimental data.

scholarly journals Thalamocortical network connectivity controls spatiotemporal dynamics of cortical and thalamic traveling waves

2019 ◽  
Author(s):  
Sayak Bhattacharya ◽  
Matthieu B. Le Cauchois ◽  
Pablo A. Iglesias ◽  
Zhe S. Chen
Keyword(s):  
Computational Model ◽  
Traveling Waves ◽  
Neural Activity ◽  
Traveling Wave ◽  
Network Connectivity ◽  
Wave Characteristics ◽  
Wave Patterns ◽  
Thalamocortical Network ◽  
Theoretical Insight ◽  
Insight Into

AbstractPropagation of neural activity in spatially structured neuronal networks has been observed in awake, anesthetized and sleeping brains. However, it remains unclear how traveling waves are coordinated temporally across recurrently connected brain structures, and how network connectivity affects spatiotemporal neural activity. Here we develop a computational model of a two-dimensional thalamocortical network that enables us to investigate traveling wave characteristics in space-time. We show that thalamocortical and intracortical network connectivity, excitation/inhibition balance, thalamocortical/corticothalamic delay can independently or jointly change the spatiotemporal patterns (radial, planar and rotating waves) and characteristics (speed, direction and frequency) of cortical and thalamic traveling waves. Simulations of our model further predict that increased thalamic inhibition induces slower cortical wave frequency, and enhanced cortical excitation increases cortical wave speed and oscillation frequencies. Overall, the model study provides not only theoretical insight into the basis for spatiotemporal wave patterns, but also experimental predictions that potentially control these dynamics.Author SummaryCognition or sensorimotor control requires the coordination of neural activity across widespread brain circuits. Propagating waves of oscillatory neural activities have been observed at both macroscopic and mesoscopic levels, with various frequencies, spatial coverage, and modalities. However, a complete understanding how thalamocortical traveling waves are originated and temporally coordinated in the thalamus and cortex are still unclear. Furthermore, it remains unknown how the network connectivity, excitation/inhibition balance, thalamocortical or corticothalamic delay determine the spatiotemporal wave patterns and characteristics of cortical and thalamic traveling waves. Here we develop a computational model of a two-dimensional thalamocortical network to investigate the thalamic and neocortical traveling wave characteristics in space-time, which allows us to quantitatively assess the impact of thalamocortical network properties on the formation and maintenance of complex traveling wave patterns. Our computational model provides strong theoretical insight into the basis of spatiotemporal wave propagation, as well as experimental predictions that control these wave dynamics.

scholarly journals Theta and alpha oscillations are traveling waves in the human neocortex

2017 ◽  
Author(s):  
Honghui Zhang ◽  
Andrew J. Watrous ◽  
Ansh Patel ◽  
Joshua Jacobs
Keyword(s):  
Traveling Waves ◽  
Neural Activity ◽  
Coupled Oscillators ◽  
Large Scale ◽  
Brain Connectivity ◽  
Memory Task ◽  
Human Cognition ◽  
Alpha Oscillations ◽  
Neural Processes ◽  
Neurosurgical Patients

SummaryHuman cognition requires the coordination of neural activity across widespread brain networks. Here we describe a new mechanism for large-scale coordination in the human brain: traveling waves of theta and alpha oscillations. Examining direct brain recordings from neurosurgical patients performing a memory task, we found contiguous clusters of cortex in individual patients with oscillations at specific frequencies between 2 to 15 Hz. These clusters displayed spatial phase gradients, indicating that the oscillations were traveling waves that propagated across the cortex at ∼0.25-0.75 m/s. Traveling waves were relevant behaviorally because their propagation correlated with task events and was more consistent when subjects performed the task well. Our findings suggest that traveling waves can be modeled by a network of coupled oscillators because the direction of wave propagation correlated with the spatial orientation of local frequency gradients. These findings suggest a role for traveling waves in supporting brain connectivity by organizing neural processes across space and time.

scholarly journals Global waves synchronize the brain’s functional systems with fluctuating arousal

2021 ◽  
Vol 7 (30) ◽  
pp. eabf2709
Author(s):  
Ryan V. Raut ◽  
Abraham Z. Snyder ◽  
Anish Mitra ◽  
Dov Yellin ◽  
Naotaka Fujii ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Functional Connectivity ◽  
Traveling Waves ◽  
Neural Activity ◽  
Large Scale ◽  
Functional Organization ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Fmri Signal ◽  
The Brain

We propose and empirically support a parsimonious account of intrinsic, brain-wide spatiotemporal organization arising from traveling waves linked to arousal. We hypothesize that these waves are the predominant physiological process reflected in spontaneous functional magnetic resonance imaging (fMRI) signal fluctuations. The correlation structure (“functional connectivity”) of these fluctuations recapitulates the large-scale functional organization of the brain. However, a unifying physiological account of this structure has so far been lacking. Here, using fMRI in humans, we show that ongoing arousal fluctuations are associated with global waves of activity that slowly propagate in parallel throughout the neocortex, thalamus, striatum, and cerebellum. We show that these waves can parsimoniously account for many features of spontaneous fMRI signal fluctuations, including topographically organized functional connectivity. Last, we demonstrate similar, cortex-wide propagation of neural activity measured with electrocorticography in macaques. These findings suggest that traveling waves spatiotemporally pattern brain-wide excitability in relation to arousal.

scholarly journals Controllable and decomposable multidirectional synchronizations

2021 ◽  
Author(s):  
Gábor Bergmann
Keyword(s):  
Systems Engineering ◽  
Large Scale ◽  
Mathematical Framework ◽  
Collaborative Systems ◽  
Consistency Constraints ◽  
Restoration Mechanism ◽  
Common Goals ◽  
Access Privileges ◽  
Novel Concept ◽  
Engineering Projects

AbstractStudying large-scale collaborative systems engineering projects across teams with differing intellectual property clearances, or healthcare solutions where sensitive patient data needs to be partially shared, or similar multi-user information systems over databases, all boils down to a common mathematical framework. Updateable views (lenses) and more generally bidirectional transformations are abstractions to study the challenge of exchanging information between participants with different read access privileges. The view provided to each participant must be different due to access control or other limitations, yet also consistent in a certain sense, to enable collaboration towards common goals. A collaboration system must apply bidirectional synchronization to ensure that after a participant modifies their view, the views of other participants are updated so that they are consistent again. While bidirectional transformations (synchronizations) have been extensively studied, there are new challenges that are unique to the multidirectional case. If complex consistency constraints have to be maintained, synchronizations that work fine in isolation may not compose well. We demonstrate and characterize a failure mode of the emergent behaviour, where a consistency restoration mechanism undoes the work of other participants. On the other end of the spectrum, we study the case where synchronizations work especially well together: we characterize very well-behaved multidirectional transformations, a non-trivial generalization from the bidirectional case. For the former challenge, we introduce a novel concept of controllability, while for the latter one, we propose a novel formal notion of faithful decomposition. Additionally, the paper proposes several novel properties of multidirectional transformations.

scholarly journals Holocene vegetation, fire and land use dynamics at Lake Svityaz, an agriculturally marginal site in northwestern Ukraine

2021 ◽  
Author(s):  
Christoph Schwörer ◽  
Erika Gobet ◽  
Jacqueline F. N. van Leeuwen ◽  
Sarah Bögli ◽  
Rachel Imboden ◽  
...  
Keyword(s):  
Bronze Age ◽  
Human Impact ◽  
Central Europe ◽  
Large Scale ◽  
Mixed Forest ◽  
Fire Risk ◽  
Late Glacial ◽  
Future Climate Change ◽  
Agricultural Activity ◽  
Fire Dynamics

AbstractObserving natural vegetation dynamics over the entire Holocene is difficult in Central Europe, due to pervasive and increasing human disturbance since the Neolithic. One strategy to minimize this limitation is to select a study site in an area that is marginal for agricultural activity. Here, we present a new sediment record from Lake Svityaz in northwestern Ukraine. We have reconstructed regional and local vegetation and fire dynamics since the Late Glacial using pollen, spores, macrofossils and charcoal. Boreal forest composed of Pinus sylvestris and Betula with continental Larix decidua and Pinus cembra established in the region around 13,450 cal bp, replacing an open, steppic landscape. The first temperate tree to expand was Ulmus at 11,800 cal bp, followed by Quercus, Fraxinus excelsior, Tilia and Corylus ca. 1,000 years later. Fire activity was highest during the Early Holocene, when summer solar insolation reached its maximum. Carpinus betulus and Fagus sylvatica established at ca. 6,000 cal bp, coinciding with the first indicators of agricultural activity in the region and a transient climatic shift to cooler and moister conditions. Human impact on the vegetation remained initially very low, only increasing during the Bronze Age, at ca. 3,400 cal bp. Large-scale forest openings and the establishment of the present-day cultural landscape occurred only during the past 500 years. The persistence of highly diverse mixed forest under absent or low anthropogenic disturbance until the Early Middle Ages corroborates the role of human impact in the impoverishment of temperate forests elsewhere in Central Europe. The preservation or reestablishment of such diverse forests may mitigate future climate change impacts, specifically by lowering fire risk under warmer and drier conditions.

scholarly journals Dynamic relationships between spontaneous and evoked electrophysiological activity

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Soren Wainio-Theberge ◽  
Annemarie Wolff ◽  
Georg Northoff
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
Behavioral Outcomes ◽  
Low Frequencies ◽  
Scale Free ◽  
Non Invasive ◽  
Spontaneous Neural Activity ◽  
Electrophysiological Signals ◽  
Evoked Activity ◽  
Dynamic Relationships

AbstractSpontaneous neural activity fluctuations have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioral outcomes. However, the complex electrophysiological mechanisms by which these fluctuations shape stimulus-evoked neural activity remain largely to be explored. Employing a large-scale magnetoencephalographic dataset and an electroencephalographic replication dataset, we investigate the relationship between spontaneous and evoked neural activity across a range of electrophysiological variables. We observe that for high-frequency activity, high pre-stimulus amplitudes lead to greater evoked desynchronization, while for low frequencies, high pre-stimulus amplitudes induce larger degrees of event-related synchronization. We further decompose electrophysiological power into oscillatory and scale-free components, demonstrating different patterns of spontaneous-evoked correlation for each component. Finally, we find correlations between spontaneous and evoked time-domain electrophysiological signals. Overall, we demonstrate that the dynamics of multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, a result which carries implications for experimental design and analysis in non-invasive electrophysiology.

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

Application of projection-based model reduction to finite-element plate models for two-dimensional traveling waves

2016 ◽  
Vol 28 (14) ◽  
pp. 1886-1904 ◽  
Author(s):  
Vijaya VN Sriram Malladi ◽  
Mohammad I Albakri ◽  
Serkan Gugercin ◽  
Pablo A Tarazaga
Keyword(s):  
Finite Element ◽  
Model Reduction ◽  
Traveling Waves ◽  
Large Scale ◽  
Fe Model ◽  
Plate Model ◽  
Reduction Techniques ◽  
State Frequency ◽  
Scale Down ◽  
The Stability

A finite element (FE) model simulates an unconstrained aluminum thin plate to which four macro-fiber composites are bonded. This plate model is experimentally validated for single and multiple inputs. While a single input excitation results in the frequency response functions and operational deflection shapes, two input excitations under prescribed conditions result in tailored traveling waves. The emphasis of this article is the application of projection-based model reduction techniques to scale-down the large-scale FE plate model. Four model reduction techniques are applied and their performances are studied. This article also discusses the stability issues associated with the rigid-body modes. Furthermore, the reduced-order models are utilized to simulate the steady-state frequency and time response of the plate. The results are in agreement with the experimental and the full-scale FE model results.

scholarly journals Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Pengshuo Yang ◽  
Chongyang Tan ◽  
Maozhen Han ◽  
Lin Cheng ◽  
Xuefeng Cui ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Large Scale ◽  
Network Connectivity ◽  
Small Range ◽  
Environmental Disturbance ◽  
Target Species ◽  
Important Species ◽  
Metagenome Analysis ◽  
Occurrence Patterns ◽  
Edge Networks

Abstract Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.e. species) of the Species-Centric Networks (SCNs). However, little was understood about the change of correlation between network members (i.e. edges of the SCNs) when the network was disturbed. Here, we introduced a Correlation-Centric Network (CCN) to the microbial research based on the concept of edge networks. In CCN, each node represented a species–species correlation, and edge represented the species shared by two correlations. In this research, we investigated the CCNs and their corresponding SCNs on two large cohorts of microbiome. The results showed that CCNs not only retained the characteristics of SCNs, but also contained information that cannot be detected by SCNs. In addition, when the members of microbial communities were decreased (i.e. environmental disturbance), the CCNs fluctuated within a small range in terms of network connectivity. Therefore, by highlighting the important species correlations, CCNs could unveil new insights when studying not only the functions of target species, but also the stabilities of their residing microbial communities.

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