Common-input models for multiple neural spike-train data.
about
State-space analysis of time-varying higher-order spike correlation for multiple neural spike train dataImpact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model.An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine InterfacesThe Development and Analysis of Integrated Neuroscience DataDenoising neural data with state-space smoothing: method and application.A new look at state-space models for neural data.On the similarity of functional connectivity between neurons estimated across timescalesIncremental mutual information: a new method for characterizing the strength and dynamics of connections in neuronal circuitsFunctional connectivity and tuning curves in populations of simultaneously recorded neurons.Applying the multivariate time-rescaling theorem to neural population models.Statistical assessment of the stability of neural movement representationsDimensionality reduction for large-scale neural recordings.Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activityExtracting Low-Dimensional Latent Structure from Time Series in the Presence of DelaysModeling the impact of common noise inputs on the network activity of retinal ganglion cellsIntra-day signal instabilities affect decoding performance in an intracortical neural interface system.Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activityFactor-analysis methods for higher-performance neural prostheses.Inferring functional connections between neurons.On measures of dissimilarity between point patterns: classification based on prototypes and multidimensional scaling.Attention stabilizes the shared gain of V4 populations.Analysis of Neuronal Spike Trains, Deconstructed.Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings.Consistent estimation of complete neuronal connectivity in large neuronal populations using sparse "shotgun" neuronal activity sampling.Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise CorrelationsBayesian inference for generalized linear models for spiking neurons.Coupling Time Decoding and Trajectory Decoding using a Target-Included Model in the Motor Cortex.Neural decoding of hand motion using a linear state-space model with hidden states.Estimating short-term synaptic plasticity from pre- and postsynaptic spiking.Bayesian inference of functional connectivity and network structure from spikes.Statistical Signal Processing and the Motor Cortex.Population decoding of motor cortical activity using a generalized linear model with hidden states.The potential of corticomuscular and intermuscular coherence for research on human motor controlSpike train decoding without spike sortingA regularized point process generalized linear model for assessing the functional connectivity in the cat motor cortex.A Simple Mechanism for Beyond-Pairwise Correlations in Integrate-and-Fire Neurons.Hidden Markov models for the stimulus-response relationships of multistate neural systems.Encoding through patterns: regression tree-based neuronal population models.Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness.Extracting state transition dynamics from multiple spike trains using hidden Markov models with correlated poisson distribution.
P2860
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P2860
Common-input models for multiple neural spike-train data.
description
2007 nî lūn-bûn
@nan
2007 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年学术文章
@wuu
2007年学术文章
@zh-cn
2007年学术文章
@zh-hans
2007年学术文章
@zh-my
2007年学术文章
@zh-sg
2007年學術文章
@yue
name
Common-input models for multiple neural spike-train data.
@ast
Common-input models for multiple neural spike-train data.
@en
type
label
Common-input models for multiple neural spike-train data.
@ast
Common-input models for multiple neural spike-train data.
@en
prefLabel
Common-input models for multiple neural spike-train data.
@ast
Common-input models for multiple neural spike-train data.
@en
P2860
P1433
P1476
Common-input models for multiple neural spike-train data.
@en
P2093
Jayant E Kulkarni
P2860
P304
P356
10.1080/09548980701625173
P577
2007-12-01T00:00:00Z