A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

scientific article published on 09 September 2015

A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations is …
instance of (P31):
scholarly articleQ13442814

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P8978DBLP publication IDjournals/fini/HahneHKIBFD15
P356DOI10.3389/FNINF.2015.00022
P932PMC publication ID4563270
P698PubMed publication ID26441628
P5875ResearchGate publication ID282515626

P50authorAndreas FrommerQ16054368
Moritz HeliasQ39823106
Markus DiesmannQ41045612
Susanne KunkelQ62580896
P2093author name stringJun Igarashi
Matthias Bolten
Jan Hahne
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Computational model of erratic arrhythmias in a cardiac cell network: the role of gap junctionsQ27322666
An Ultrascalable Solution to Large-scale Neural Tissue SimulationQ28740289
Spiking network simulation code for petascale computersQ34320753
Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and CorrelationsQ35761037
Stochastic 16-state model of voltage gating of gap-junction channels enclosing fast and slow gatesQ36010877
Astroglial networks: a step further in neuroglial and gliovascular interactions.Q37678191
The role of gap junctions in the brain in health and disease.Q37888013
Exact digital simulation of time-invariant linear systems with applications to neuronal modelingQ39524277
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Meeting the memory challenges of brain-scale network simulationQ40620891
Supercomputers ready for use as discovery machines for neuroscienceQ41556112
Gap junctions compensate for sublinear dendritic integration in an inhibitory network.Q41646990
On numerical simulations of integrate-and-fire neural networksQ41705443
The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network modelQ41945910
Fully implicit parallel simulation of single neuronsQ41963876
A unified view on weakly correlated recurrent networksQ42684886
Asynchronous states and the emergence of synchrony in large networks of interacting excitatory and inhibitory neuronsQ44318649
Simplicity and efficiency of integrate-and-fire neuron modelsQ47816697
Exact subthreshold integration with continuous spike times in discrete-time neural network simulationsQ47846164
How spike generation mechanisms determine the neuronal response to fluctuating inputs.Q47873660
Electrical synapses and synchrony: the role of intrinsic currents.Q47876843
Multispikes and synchronization in a large neural network with temporal delaysQ47898140
Dynamics of sparsely connected networks of excitatory and inhibitory spiking neuronsQ47899491
Fast global oscillations in networks of integrate-and-fire neurons with low firing ratesQ47903215
Synchronization of electrically coupled pairs of inhibitory interneurons in neocortex.Q48266919
Advancing the boundaries of high-connectivity network simulation with distributed computing.Q48842981
Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model.Q48890720
Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks.Q51540390
Overview of the SpiNNaker System ArchitectureQ56938341
Role of delays in shaping spatiotemporal dynamics of neuronal activity in large networksQ81033332
P4510describes a project that usesNeuronQ7002467
P407language of work or nameEnglishQ1860
P304page(s)22
P577publication date2015-09-09
P1433published inFrontiers in NeuroinformaticsQ25474550
P1476titleA unified framework for spiking and gap-junction interactions in distributed neuronal network simulations
P478volume9

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cites work (P2860)
Q83227824Brian 2, an intuitive and efficient neural simulator
Q38755355Constructing Neuronal Network Models in Massively Parallel Environments
Q52674870Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.
Q38370411Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.
Q41063782Software for Brain Network Simulations: A Comparative Study
Q47551286The Confluence of Perceiving and Thinking in Consciousness Phenomenology
Q38681904The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code

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