Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models.
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Inferring cortical function in the mouse visual system through large-scale systems neuroscienceEnhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience.Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons.Analysis of Neuronal Spike Trains, Deconstructed.Identification of Stable Spike-Timing-Dependent Plasticity from Spiking Activity with Generalized Multilinear Modeling.Generalized leaky integrate-and-fire models classify multiple neuron types.Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types.
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P2860
Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models.
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name
Automated High-Throughput Char ...... of Simplified Spiking Models.
@ast
Automated High-Throughput Char ...... of Simplified Spiking Models.
@en
type
label
Automated High-Throughput Char ...... of Simplified Spiking Models.
@ast
Automated High-Throughput Char ...... of Simplified Spiking Models.
@en
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Automated High-Throughput Char ...... of Simplified Spiking Models.
@ast
Automated High-Throughput Char ...... of Simplified Spiking Models.
@en
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Automated High-Throughput Char ...... of Simplified Spiking Models.
@en
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Christian Pozzorini
Olivier Hagens
Skander Mensi
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P304
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10.1371/JOURNAL.PCBI.1004275
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2015-06-17T00:00:00Z