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The influence of synaptic weight distribution on neuronal population dynamicsData-driven modeling of synaptic transmission and integration.Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods.A model for the variability of interspike intervals during sustained firing of a retinal neuron.Response properties of an integrate-and-fire model that receives subthreshold inputs.Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentialsSpike-based reinforcement learning in continuous state and action space: when policy gradient methods failRelating neuronal firing patterns to functional differentiation of cerebral cortex.Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors.Motor unit discharge characteristics and short term synchrony in paraplegic humans.Instantaneous non-linear processing by pulse-coupled threshold units.Democratic population decisions result in robust policy-gradient learning: a parametric study with GPU simulations.Spike-interval triggered averaging reveals a quasi-periodic spiking alternative for stochastic resonance in catfish electroreceptors.Firing rate of a retinal neuron are not predictable from interspike interval statistics.Statistical analysis of membrane potential fluctuations. Relation with presynaptic spike train.Amplitude metrics for cellular circadian bioluminescence reporters.Information theoretic analysis of proprioceptive encoding during finger flexion in the monkey sensorimotor systemThe influence of spatiotemporal structure of noisy stimuli in decision making.Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo.Similarity in Neuronal Firing Regimes across Mammalian SpeciesNeuronal variability of MSTd neurons changes differentially with eye movement and visually related variables.Reduction of spike afterdepolarization by increased leak conductance alters interspike interval variabilityFinite post synaptic potentials cause a fast neuronal response.Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times.Bayesian Inference of Synaptic Quantal Parameters from Correlated Vesicle Release.The Gamma renewal process as an output of the diffusion leaky integrate-and-fire neuronal model.A generative joint model for spike trains and saccades during perceptual decision-making.Responses of Leaky Integrate-and-Fire Neurons to a Plurality of Stimuli in Their Receptive FieldsNeural computation and the computational theory of cognition.Influence of temporal correlation of synaptic input on the rate and variability of firing in neurons.A consensus layer V pyramidal neuron can sustain interpulse-interval coding.Statistical inference on spontaneous neuronal discharge patterns. I. Single neuron.Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold.Exact firing time statistics of neurons driven by discrete inhibitory noise.Dynamic statistics of crayfish caudal photoreceptors.Equilibrium and Response Properties of the Integrate-and-Fire Neuron in Discrete Time.Quantifying neural coding of event timing.A Markov model for modulation periods in brain output.On the phase reduction and response dynamics of neural oscillator populations.How sample paths of leaky integrate-and-fire models are influenced by the presence of a firing threshold.
P2860
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P2860
description
1965 nî lūn-bûn
@nan
1965年の論文
@ja
1965年論文
@yue
1965年論文
@zh-hant
1965年論文
@zh-hk
1965年論文
@zh-mo
1965年論文
@zh-tw
1965年论文
@wuu
1965年论文
@zh
1965年论文
@zh-cn
name
A THEORETICAL ANALYSIS OF NEURONAL VARIABILITY.
@en
type
label
A THEORETICAL ANALYSIS OF NEURONAL VARIABILITY.
@en
prefLabel
A THEORETICAL ANALYSIS OF NEURONAL VARIABILITY.
@en
P2860
P1433
P1476
A THEORETICAL ANALYSIS OF NEURONAL VARIABILITY.
@en
P2093
P2860
P304
P356
10.1016/S0006-3495(65)86709-1
P407
P577
1965-03-01T00:00:00Z