Including Gap Junctions into Distributed Neuronal Network Simulations

Hahne, Jan; Helias, Moritz; Kunkel, Susanne; Igarashi, Jun; Kitayama, Itaru; Wylie, Brian J. N.; Bolten, Matthias; Frommer, Andreas; Diesmann, Markus

doi:10.1007/978-3-319-50862-7_4

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…We decided not to model gap junctions across BaCs for the sake of simplicity, because we found no argument suggesting that they might be directly instrumental to SW generation. We added gap junctions across MaCs in our model using the infrastructure described in Hahne et al (2016) and implemented in NEST, which allows instantaneous interaction between pairs of neurons. The total gap junction current for unit k is

{I_{g a p}}_{k} = \sum_{l} g_{g a p_{k l}} ((), V_{l} - V_{k})

where

{g_{g a p}}_{kl}

is the conductance of the gap junction between units

k

and

l

.The strength of electrical connections across two cells is typically measured by injecting a step current into one and measuring the coupling coefficient (CC), or the ratio between the steady‐state voltage deflection of the postjunctional cell and that of the prejunctional cell.…”

Section: Methodsmentioning

confidence: 99%

Section: Gap Junctions Modellingmentioning

confidence: 99%

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Sleep and wake in a model of the thalamocortical system with Martinotti cells

Bugnon

Mayner

Cirelli

et al. 2022

Eur J of Neuroscience

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The mechanisms leading to the alternation between active (UP) and silent (DOWN) states during sleep slow waves (SWs) remain poorly understood. Previous models have explained the transition to the DOWN state by a progressive failure of excitation because of the build‐up of adaptation currents or synaptic depression. However, these models are at odds with recent studies suggesting a role for presynaptic inhibition by Martinotti cells (MaCs) in generating SWs. Here, we update a classical large‐scale model of sleep SWs to include MaCs and propose a different mechanism for the generation of SWs. In the wake mode, the network exhibits irregular and selective activity with low firing rates (FRs). Following an increase in the strength of background inputs and a modulation of synaptic strength and potassium leak potential mimicking the reduced effect of acetylcholine during sleep, the network enters a sleep‐like regime in which local increases of network activity trigger bursts of MaC activity, resulting in strong disfacilitation of the local network via presynaptic GABAB1a‐type inhibition. This model replicates findings on slow wave activity (SWA) during sleep that challenge previous models, including low and skewed FRs that are comparable between the wake and sleep modes, higher synchrony of transitions to DOWN states than to UP states, the possibility of triggering SWs by optogenetic stimulation of MaCs, and the local dependence of SWA on synaptic strength. Overall, this work points to a role for presynaptic inhibition by MaCs in the generation of DOWN states during sleep.

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{I_{g a p}}_{k} = \sum_{l} g_{g a p_{k l}} ((), V_{l} - V_{k})

where

{g_{g a p}}_{kl}

is the conductance of the gap junction between units

k

and

l

Section: Methodsmentioning

confidence: 99%

Section: Gap Junctions Modellingmentioning

confidence: 99%

Sleep and wake in a model of the thalamocortical system with Martinotti cells

Bugnon

Mayner

Cirelli

et al. 2022

Eur J of Neuroscience

View full text Add to dashboard Cite

show abstract

“…One example for such a case is the inclusion of electrical synapses, so-called gap junctions. The computational and neuroscientific aspects of the implementation of gap junctions in a spiking neural network simulator (without any special focus on how waveform-relaxation methods can be used to overcome the difficulties of including gap junctions) are described in [15,16]. Here we discuss the missing aspects with regard to the waveform-relaxation method and show how exactly they can improve the inclusion of gap junctions.…”

Section: Introductionmentioning

confidence: 99%

An SDE waveform‐relaxation method with application in distributed neural network simulations

Bolten

Hahne

2019

Proc Appl Math and Mech

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Waveform‐relaxation methods are a set of iterative methods to solve systems of differential equations by dividing them into subsystems. Several of these methods, such as the Jacobi waveform‐relaxation method, enable potential for parallelization across the system and are for that reason interesting in applications with a highly parallel setting. Here we present an SDE waveform‐relaxation methods with applications in the fields of computational neuroscience. We give a short overview how and where the application of the method can speed up the simulation of functionally inspired rate‐based units in a distributed neural network simulator that was originally designed for biologically grounded spiking neuron models.

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Including Gap Junctions into Distributed Neuronal Network Simulations

Cited by 2 publications

References 21 publications

Sleep and wake in a model of the thalamocortical system with Martinotti cells

Sleep and wake in a model of the thalamocortical system with Martinotti cells

An SDE waveform‐relaxation method with application in distributed neural network simulations

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