Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays
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An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine InterfacesVariational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains.Unsupervised 2D Dimensionality Reduction with Adaptive Structure Learning.Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings.From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.Noise-Robust Modes of the Retinal Population Code Have the Geometry of "Ridges" and Correspond to Neuronal Communities.
P2860
Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays
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2015 nî lūn-bûn
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2015年の論文
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2015年論文
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2015年論文
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2015年論文
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2015年論文
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2015年論文
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2015年论文
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2015年论文
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2015年论文
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name
Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays
@ast
Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays
@en
type
label
Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays
@ast
Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays
@en
prefLabel
Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays
@ast
Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays
@en
P2093
P2860
P356
P1433
P1476
Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays
@en
P2093
Byron M Yu
Karthik C Lakshmanan
Patrick T Sadtler
P2860
P304
P356
10.1162/NECO_A_00759
P577
2015-06-16T00:00:00Z