Dataset Information

Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity.

ABSTRACT: Oscillations in the beta/low gamma range (10-45 Hz) are recorded in diverse neural structures. They have successfully been modeled as sparsely synchronized oscillations arising from reciprocal interactions between randomly connected excitatory (E) pyramidal cells and local interneurons (I). The synchronization of spatially distant oscillatory spiking E-I modules has been well-studied in the rate model framework but less so for modules of spiking neurons. Here, we first show that previously proposed modifications of rate models provide a quantitative description of spiking E-I modules of Exponential Integrate-and-Fire (EIF) neurons. This allows us to analyze the dynamical regimes of sparsely synchronized oscillatory E-I modules connected by long-range excitatory interactions, for two modules, as well as for a chain of such modules. For modules with a large number of neurons (> 105), we obtain results similar to previously obtained ones based on the classic deterministic Wilson-Cowan rate model, with the added bonus that the results quantitatively describe simulations of spiking EIF neurons. However, for modules with a moderate (~ 104) number of neurons, stochastic variations in the spike emission of neurons are important and need to be taken into account. On the one hand, they modify the oscillations in a way that tends to promote synchronization between different modules. On the other hand, independent fluctuations on different modules tend to disrupt synchronization. The correlations between distant oscillatory modules can be described by stochastic equations for the oscillator phases that have been intensely studied in other contexts. On shorter distances, we develop a description that also takes into account amplitude modes and that quantitatively accounts for our simulation data. Stochastic dephasing of neighboring modules produces transient phase gradients and the transient appearance of phase waves. We propose that these stochastically-induced phase waves provide an explanative framework for the observations of traveling waves in the cortex during beta oscillations.

SUBMITTER: Kulkarni A

PROVIDER: S-EPMC7604323 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity.

Kulkarni Anirudh A Ranft Jonas J Hakim Vincent V

Frontiers in computational neuroscience 20201019

Oscillations in the beta/low gamma range (10-45 Hz) are recorded in diverse neural structures. They have successfully been modeled as sparsely synchronized oscillations arising from reciprocal interactions between randomly connected excitatory (E) pyramidal cells and local interneurons (I). The synchronization of spatially distant oscillatory spiking E-I modules has been well-studied in the rate model framework but less so for modules of spiking neurons. Here, we first show that previously propo ...[more]

PMID: 33192427

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Project description:Connectivity is a fundamental concept linking dispersal to the emergent dynamics and persistence of spatially structured populations. Functional measures of connectivity typically seek to integrate aspects of landscape structure and animal movement to describe ecologically meaningful connectedness at the landscape and population scale. Despite this focus on function, traditional measures of landscape connectivity assume it is a static property of the landscape, hence abstracting out the underlying spatiotemporal population dynamics. Connectivity is, arguably, a dynamic property of landscapes, and is inherently related to the spatial distribution of individuals and populations across the landscape. Static representations of connectivity potentially overlook this variation and therefore adopting a dynamic approach should offer improved insights about connectivity and associated ecological processes. Using a large-scale, long-term time series of occupancy data from a metapopulation of water voles Arvicola amphibius, we tested competing hypotheses about how considering the dynamic nature of connectivity improves the ability of spatially explicit occupancy models to recover population dynamics. Iteratively relaxing standing assumptions of connectivity metrics, these models ranged from spatially and temporally fixed connectivity metrics that are widely applied, to the more flexible, but lesser used model that allowed temporally varying connectivity measures that incorporate spatiotemporally dynamic patch occupancy states. Our results provide empirical evidence that demographic weighting using patch occupancy dynamics and temporal variability in connectivity measures are important for describing metapopulation dynamics. We highlight the implications of commonly held assumption in connectivity modelling and demonstrate how they result in different and highly variable predictions of metapopulation capacity. Thus, we argue that the concept of connectivity and its potential applications would benefit from recognizing inherent spatiotemporal variation in connectivity that is explicitly linked to underlying ecological state variables.

Dataset Information

Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity.

Publications

Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity.

Similar Datasets

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