Switching between gamma and theta: Dynamic network control using subthreshold electric fields

Berzhanskaya, Julia; Gorchetchnikov, Anatoli

doi:10.1016/j.neucom.2006.10.124

Cited by 16 publications

(13 citation statements)

References 18 publications

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“…EF effects at the level of single cells in the model are consistent with previous experimental observations [2,7]. Results obtained in our previous model [5] are reproduced.…”

Section: Methods and Resultssupporting

confidence: 90%

“…It has been proposed that theta modulation can synchronize cell assemblies across the hippocampus; that place cell firing phase can code for the animal position; and that different phases of theta correlate with learning vs. recall. To demonstrate control of theta and gamma rhythms by electric field (EF), we developed a simplified 2-dimensional CA1 network based on [4] showing theta-modulated high frequency activity of pyramidal neurons [5]. Application of EF alone was able to shift the firing mode of the simplified network in the theta-gamma range.…”

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confidence: 99%

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Electric field modulation of theta and gamma rhythms: probe into network connectivity

Berzhanskaya

Ascoli

2008

BMC Neurosci

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Subthreshold electric fields are effective in suppressing epilepsy [1], modulating the state of single neurons via polarization [2], and affecting neuronal synchronization [3]. Here we employ detailed computational modeling to test the use of global and local electric fields as a tool for dissecting neural population dynamics and connectivity. For example, modulation of cellular firing by theta rhythms plays an important role in hippocampal cognitive functions. It has been proposed that theta modulation can synchronize cell assemblies across the hippocampus; that place cell firing phase can code for the animal position; and that different phases of theta correlate with learning vs. recall. To demonstrate control of theta and gamma rhythms by electric field (EF), we developed a simplified 2-dimensional CA1 network based on [4] showing theta-modulated high frequency activity of pyramidal neurons [5]. Application of EF alone was able to shift the firing mode of the simplified network in the theta-gamma range. Methods and resultsCurrent implementation of this model (NEURON) includes morphologically realistic pyramidal cells (P) with detailed passive, active, and synaptic properties ConclusionWhile the exact architecture underlying CA3-CA1 rhythmic activity is unknown, it might be possible to distinguish between alternative mechanisms by subthreshold electric field application.

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“…EF effects at the level of single cells in the model are consistent with previous experimental observations [2,7]. Results obtained in our previous model [5] are reproduced.…”

Section: Methods and Resultssupporting

confidence: 90%

mentioning

confidence: 99%

Electric field modulation of theta and gamma rhythms: probe into network connectivity

Berzhanskaya

Ascoli

2008

BMC Neurosci

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“…On one hand, the electromagnetic radiation of organism environment is strengthening rapidly and the phenomenon of induced neurological disease becomes increasingly common because external electric affects the function of nervous system; on the other hand, electrical stimulation has become one of the main experimental means of neurological diseases treatments [16]. Many experiments confirmed that external electric field can affect the dynamic characteristics of neurons [17][18].…”

Section: Consider Micro External Electric Fieldsmentioning

confidence: 99%

“…The object of this study, which is the neuron model brought up by Sherman in 1988 [16], analyzed the influence of different parameters to model discharge rhythm through changing parameters in the model to analyze the model dynamic characteristics.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Behavior Analysis of a Class of Neurons Network Model

Wu¹,

Shi²,

Lu³

et al. 2015

IJSIP

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“…KInNeSS records the voltage of individual compartments, as well as aggregate cell recordings such as local field potentials (LFP) and current source densities (CSD). Typical simulations that have been performed by KInNeSS include models of hippocampal neurons and spatial navigation , models of thalamo-cortical learning Leveille et al, 2008), and models of interactions between electric field and cell activity (Berzhanskaya et al, 2007).…”

Section: Kinness Overviewmentioning

confidence: 99%

KInNeSS: A Modular Framework for Computational Neuroscience

et al. 2008

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Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform. 3 IntroductionAdvances in functional, anatomical, and behavioral neuroscience techniques have led to an increase in the data available for modeling complex dynamics of biologically inspired neural networks at many levels of abstraction, from in-depth descriptions and analyses of individual membrane channels to large-scale investigations of whole brain activity. This wealth of data is essential for creating realistic neural models and increases our understanding of animal and human behavior. Furthermore, it has pushed the modeling community towards the design of increasingly complex models, incorporating unprecedented amount of biophysical and anatomical constraints. These large-scale neural models are often non-linear dynamical systems which can be analytically intractable and require numerical simulation to gain insight into their behavior. Emergent properties of large-scale neural networks often remain unnoticed until the whole system is simulated and components are allowed to interact (Cannon et al., 2002).An additional level of complexity is finding a neural simulator and simulation environment that would enable the large variety of researchers from neurophysiology, psychology and computational modeling to share data and work collaboratively (an excellent review can be found in Brette et al., 2007). Most available software packages are specialized in different applications.

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Switching between gamma and theta: Dynamic network control using subthreshold electric fields

Cited by 16 publications

References 18 publications

Electric field modulation of theta and gamma rhythms: probe into network connectivity

Electric field modulation of theta and gamma rhythms: probe into network connectivity

Dynamic Behavior Analysis of a Class of Neurons Network Model

KInNeSS: A Modular Framework for Computational Neuroscience

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