about
Multiple neural spike train data analysis: state-of-the-art and future challengesSleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill ConsolidationMeasuring the Performance of Neural ModelsReconstructing the Population Activity of Olfactory Output Neurons that Innervate Identifiable Processing Units.The episodic nature of spike trains in the early visual pathway.System identification of Drosophila olfactory sensory neurons.Statistical issues in the analysis of neuronal data.Flexible models for spike count data with both over- and under- dispersion.A new look at state-space models for neural data.Decoding movement trajectories through a T-maze using point process filters applied to place field data from rat hippocampal region CA1.An Implementation of Bayesian Adaptive Regression Splines (BARS) in C with S and R Wrappers.Comparison of two populations of curves with an application in neuronal data analysis.Semiparametric Bayesian approaches to joinpoint regression for population-based cancer survival data.Effective connectivity of hippocampal neural network and its alteration in Mg2+-free epilepsy modelOptogenetically induced spatiotemporal gamma oscillations and neuronal spiking activity in primate motor cortexAnalysis of between-trial and within-trial neural spiking dynamics.Action-outcome relationships are represented differently by medial prefrontal and orbitofrontal cortex neurons during action execution.Temporal properties of dual-peak responses of mouse retinal ganglion cells and effects of inhibitory pathways.Coding Properties of Mouse Retinal Ganglion Cells with Dual-Peak Patterns with Respect to Stimulus Intervals.Compressed and distributed sensing of neuronal activity for real time spike train decoding.Optimizing for generalization in the decoding of internally generated activity in the hippocampus.On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship.Misinformation in the conjugate prior for the linear model with implications for free-knot spline modelling.A framework for evaluating pairwise and multiway synchrony among stimulus-driven neurons.Functional implications of orientation maps in primary visual cortex.Kernel bandwidth optimization in spike rate estimation.State-space algorithms for estimating spike rate functions.Hidden Markov models for the stimulus-response relationships of multistate neural systems.Assessing neural activity related to decision-making through flexible odds ratio curves and their derivatives.Task Performance Changes the Amplitude and Timing of the BOLD Signal.Fast maximum likelihood estimation using continuous-time neural point process models.A general likelihood framework for characterizing the time course of neural activity.A reproducing kernel Hilbert space framework for spike train signal processing.Statistical assessment of time-varying dependency between two neurons.
P2860
Q22337230-B60A19CC-4DF7-42DE-A612-78860FF17EA5Q27346625-8576B103-31DB-4A10-838E-B91F2DCE8499Q30390584-59B64AAE-9EF5-4EE3-B5B8-1395F76C6E04Q30483261-D3ACEF4C-CADC-475A-AB5A-393DBE4D48F7Q30497727-1A395A02-651D-470C-BDC9-0F1FDBAD8E43Q30542420-5DA7EC5E-3D28-45BE-A6DB-84112D58EB47Q30993161-A9AAB055-7672-4C42-9383-B779E442B9D0Q31062189-D858B984-762B-4161-A9AE-52E671D70403Q33489684-749E2B2F-86E7-413B-AD63-6B04EA548F0FQ33504790-096AC199-9894-4568-82A6-80106F50047FQ33506423-97EDAE54-63E8-479F-B902-85CB2094EC62Q33826577-F25EC30A-B89E-47BE-B59F-9C7B00EDA3D6Q34115299-C3329A65-D616-4218-9276-F9D93F8C6A50Q35127970-9F7D22D5-E89D-424B-9ED8-52E95205B498Q35709327-418A17BC-95C5-4B69-8F6E-C471E06BE218Q36726015-60633AB7-58F7-48AC-849B-74C0CB7D8493Q36904754-616819EC-B98F-4149-9CFB-65033F1FB132Q36910077-88F01E50-9772-43C5-B1DF-81A334C6089EQ37104531-215749FB-5550-41A1-8D22-FBC3FB34225EQ37435388-B3332082-DC70-4BD7-A078-F7B1AF84F310Q38972256-BBDDC8F0-9FFF-4A60-A701-20EB2E1DEBF6Q39006943-86AD0C57-A94F-4829-B59C-5A3F342670D2Q42147899-679DF36B-A2FA-413F-95FA-0A7A1060C23CQ42201098-FFEC194C-917A-411B-A5EC-C201C55DA9CDQ42363517-E824D00F-E0A2-41A6-B6E2-8C12D62790A7Q42426350-74C901C3-49BA-4FFB-9467-8F7BFC19860EQ42623489-825B8310-FF03-4624-AD1D-A648559C9633Q43466397-19E5BC80-55FF-4F62-9AA4-4A03695CAF75Q46175370-29600E57-3BDB-4758-972D-9AAE4A9041FCQ47557492-3EF2DCF6-35B7-4387-94E7-9A359053BE4AQ48273358-5D5A9879-7F7A-4CD4-8102-5117FF1C98A7Q51550629-EC2EF35B-795E-41DC-9562-FBCF3F6240A8Q51831608-2F7C4A3C-0288-4490-89D6-E7B3418611F1Q51964487-999D07FF-0C54-4EE5-A840-7606A24E8CB3
P2860
description
2003 nî lūn-bûn
@nan
2003 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2003 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2003年の論文
@ja
2003年論文
@yue
2003年論文
@zh-hant
2003年論文
@zh-hk
2003年論文
@zh-mo
2003年論文
@zh-tw
2003年论文
@wuu
name
Statistical smoothing of neuronal data.
@ast
Statistical smoothing of neuronal data.
@en
type
label
Statistical smoothing of neuronal data.
@ast
Statistical smoothing of neuronal data.
@en
prefLabel
Statistical smoothing of neuronal data.
@ast
Statistical smoothing of neuronal data.
@en
P2860
P356
P1433
P1476
Statistical smoothing of neuronal data.
@en
P2093
Robert E Kass
P2860
P356
10.1088/0954-898X/14/1/301
P577
2003-02-01T00:00:00Z