Comparative study of different integrate-and-fire neurons: spontaneous activity, dynamical response, and stimulus-induced correlation.
about
Balanced synaptic input shapes the correlation between neural spike trains.Representation of dynamical stimuli in populations of threshold neurons.Impact of network structure and cellular response on spike time correlations.The spatial structure of stimuli shapes the timescale of correlations in population spiking activity.Noise suppression and surplus synchrony by coincidence detection.Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics.A-current and type I/type II transition determine collective spiking from common input.Information filtering by synchronous spikes in a neural population.Cross-correlations and joint gaussianity in multivariate level crossing models.Signatures of synchrony in pairwise count correlations.How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regimeWhen do microcircuits produce beyond-pairwise correlations?Spike correlations - what can they tell about synchrony?Interplay of two signals in a neuron with heterogeneous synaptic short-term plasticity.Synchronous spikes are necessary but not sufficient for a synchrony code in populations of spiking neurons.Shifting Spike Times or Adding and Deleting Spikes-How Different Types of Noise Shape Signal Transmission in Neural Populations.The shaping of the coherence function of resonate-and-fire neuron models.Mutual information density of stochastic integrate-and-fire models.Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity.Colored noise and memory effects on formal spiking neuron models.Coding of time-dependent stimuli in homogeneous and heterogeneous neural populations.Accuracy of rate coding: When shorter time window and higher spontaneous activity help.Solving the two-dimensional Fokker-Planck equation for strongly correlated neurons.Spike-count distribution in a neuronal population under weak common stimulation.Information filtering in resonant neurons.Statistics of a neuron model driven by asymmetric colored noise.Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.A frequency-resolved mutual information rate and its application to neural systems.Finite volume and asymptotic methods for stochastic neuron models with correlated inputs.Interspike interval distributions of spiking neurons driven by fluctuating inputs.The dynamics of integrate-and-fire: mean versus variance modulations and dependence on baseline parameters.Up-Down-Like Background Spiking Can Enhance Neural Information Transmission.Evoking prescribed spike times in stochastic neurons.Kv7 channels regulate pairwise spiking covariability in health and disease.Membrane potential and spike train statistics depend distinctly on input statistics.Mechanisms that modulate the transfer of spiking correlations.Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.Optimal Detection of a Localized Perturbation in Random Networks of Integrate-and-Fire Neurons.The human motor neuron pools receive a dominant slow-varying common synaptic input.
P2860
Q31044407-455B2747-11CD-445B-84F6-2F201507B40AQ34058085-B352F478-57B6-46C8-8DB8-923257BBDEF4Q34211424-C635E693-6955-4CEB-A515-DF829DD7D12EQ34426320-EEEE63F3-9093-4735-893D-5BC232FB69BAQ34671650-BBFD0170-EC42-4398-9BF0-71E3134A5B0AQ35882788-F022705B-6F2C-427C-9B73-9ACF74470EABQ36532258-2516060A-2F58-4B42-B973-56527F0B7444Q36708136-A25FA315-4C9A-4A18-AB7B-64A9E8D8B464Q37708575-AD9A0748-3C86-475B-AC71-9C4010E1CFC5Q40134273-44B5B22E-6AD9-41E0-917A-CF0AD41110A8Q41854434-E6519CF3-4AC9-469F-A8AF-6DD0C0A488EDQ41921342-7BAA77C8-8586-4970-A186-4DA9469271FDQ42098179-E2778EEC-7054-47FB-9C11-D050D1647961Q42115942-EB2FAD1C-912A-4869-83C7-675761FFED28Q42257502-8259CBC3-805B-4F20-9F6A-53FBDC772780Q43225852-5A1A86B8-F992-4D54-A16C-10C800237CEBQ46137999-8254261C-41CD-4053-BE91-17543ADA9728Q46143090-3FC4AAEE-109A-4362-83BC-6CDCCEC7438DQ46591235-5FC6A0DB-536C-4C96-BDBB-50A33928B67AQ47565918-7863EFD1-220A-4685-B0D3-C1570747782BQ47676362-F94E58C0-160E-4E57-BF08-CBC546A6DCC7Q47691759-176E75D3-01CE-4411-A56C-26F612481BC5Q47693224-3E775D64-D47E-45B9-8EED-756ED768C932Q47719560-0DA2713E-F009-4BC1-8942-4834C0EE7EA8Q47721410-4F69D865-00C5-4A6D-A844-4EEBD52A73BEQ47733574-0038891F-66DB-48A4-96DA-7D3B9169541DQ47735868-3DD52F60-8A78-46E1-A3A5-4C9D51C93398Q47737998-B48DBB9C-E901-42D6-B095-FC471F9E5A8EQ47789595-F6FDAC6F-BE9D-4E6D-BCC9-6E85A5F28919Q47792193-A5189E94-24DA-4BDC-988D-8931AC36FBACQ47794926-ED667125-D363-491D-AF97-FC129A75E07EQ47848909-CABFF753-2896-4133-8200-03E6A7CAF03EQ49962290-23ECA987-6E57-45A0-AF04-DF38D1483901Q51090363-02E35F35-C4EA-4AF8-9D4B-81117909272EQ51469789-4DB608B2-0E63-48CB-8C24-C26043F322EAQ51605696-B8CF2668-2CB7-4223-804E-49F28258CCD7Q52351192-5C5F9727-90E8-4685-BFAD-F4859217596EQ52837180-116BC82C-A85C-4CB9-A044-A0FA37EB2A8CQ53105872-3D1516AE-4F51-4125-93EA-3875E4B8178F
P2860
Comparative study of different integrate-and-fire neurons: spontaneous activity, dynamical response, and stimulus-induced correlation.
description
2009 nî lūn-bûn
@nan
2009年の論文
@ja
2009年学术文章
@wuu
2009年学术文章
@zh
2009年学术文章
@zh-cn
2009年学术文章
@zh-hans
2009年学术文章
@zh-my
2009年学术文章
@zh-sg
2009年學術文章
@yue
2009年學術文章
@zh-hant
name
Comparative study of different ...... stimulus-induced correlation.
@en
Comparative study of different ...... stimulus-induced correlation.
@nl
type
label
Comparative study of different ...... stimulus-induced correlation.
@en
Comparative study of different ...... stimulus-induced correlation.
@nl
prefLabel
Comparative study of different ...... stimulus-induced correlation.
@en
Comparative study of different ...... stimulus-induced correlation.
@nl
P2860
P1433
P1476
Comparative study of different ...... stimulus-induced correlation.
@en
P2093
Benjamin Lindner
Rafael D Vilela
P2860
P304
P356
10.1103/PHYSREVE.80.031909
P407
P433
P577
2009-09-21T00:00:00Z