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NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIFneuroConstruct: a tool for modeling networks of neurons in 3D spaceAnatomy and physiology of the thick-tufted layer 5 pyramidal neuronDendritic signal transmission induced by intracellular charge inhomogeneitiesGeneration of spike trains with controlled auto- and cross-correlation functionsSynaptic clustering within dendrites: an emerging theory of memory formationAn Ultrascalable Solution to Large-scale Neural Tissue SimulationActive dendrites and spike propagation in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneuronsNeural population coding: combining insights from microscopic and mass signalsA bushy cell network in the rat ventral cochlear nucleus.Optimization principles of dendritic structure.Counting Synapses Using FIB/SEM Microscopy: A True Revolution for Ultrastructural Volume Reconstruction.Subcellular topography of visually driven dendritic activity in the vertebrate visual system.Multiple clusters of release sites formed by individual thalamic afferents onto cortical interneurons ensure reliable transmission.Encoding of spatio-temporal input characteristics by a CA1 pyramidal neuron model.Dendritic vulnerability in neurodegenerative disease: insights from analyses of cortical pyramidal neurons in transgenic mouse modelsAn arithmetic rule for spatial summation of excitatory and inhibitory inputs in pyramidal neuronsOne rule to grow them all: a general theory of neuronal branching and its practical applicationSemi-automatic quantification of neurite fasciculation in high-density neurite images by the neurite directional distribution analysis (NDDA).Turtle functions downstream of Cut in differentially regulating class specific dendrite morphogenesis in Drosophila.Effective stimuli for constructing reliable neuron models.Spike-timing-dependent plasticity and relevant mutual information maximization.LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.Electrical advantages of dendritic spines.Somato-dendritic morphology and dendritic signal transfer properties differentiate between fore- and hindlimb innervating motoneurons in the frog Rana esculentaDampening of hyperexcitability in CA1 pyramidal neurons by polyunsaturated fatty acids acting on voltage-gated ion channelsDendritic orientation and branching distinguish a class of multifunctional turtle spinal interneuronsResponses of retinal ganglion cells to extracellular electrical stimulation, from single cell to population: model-based analysisEmerging rules for the distributions of active dendritic conductances.A biopolymer transistor: electrical amplification by microtubules.Dendritic synapse location and neocortical spike-timing-dependent plasticityVersatile morphometric analysis and visualization of the three-dimensional structure of neurons.An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites.An approach to electrical modeling of single and multiple cells.Long-Term Potentiation at CA3-CA1 Hippocampal Synapses with Special Emphasis on Aging, Disease, and Stress.Asymmetry in signal propagation between the soma and dendrites plays a key role in determining dendritic excitability in motoneurons.Neural precursor lineages specify distinct neocortical pyramidal neuron types.Bilinearity in spatiotemporal integration of synaptic inputs.Neurite, a finite difference large scale parallel program for the simulation of electrical signal propagation in neurites under mechanical loadingWhat is the most realistic single-compartment model of spike initiation?
P2860
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P2860
description
2000 nî lūn-bûn
@nan
2000 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2000 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2000年の論文
@ja
2000年論文
@yue
2000年論文
@zh-hant
2000年論文
@zh-hk
2000年論文
@zh-mo
2000年論文
@zh-tw
2000年论文
@wuu
name
Untangling dendrites with quantitative models.
@ast
Untangling dendrites with quantitative models.
@en
Untangling dendrites with quantitative models.
@nl
type
label
Untangling dendrites with quantitative models.
@ast
Untangling dendrites with quantitative models.
@en
Untangling dendrites with quantitative models.
@nl
prefLabel
Untangling dendrites with quantitative models.
@ast
Untangling dendrites with quantitative models.
@en
Untangling dendrites with quantitative models.
@nl
P1433
P1476
Untangling dendrites with quantitative models.
@en
P2093
P304
P356
10.1126/SCIENCE.290.5492.744
P407
P577
2000-10-01T00:00:00Z