Statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data.
about
Uncovering spatial topology represented by rat hippocampal population neuronal codesA Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data.Injection of fully-defined signal mixtures: a novel high-throughput tool to study neuronal encoding and computations.Motor cortical networks for skilled movements have dynamic properties that are related to accurate reaching.A statistical method of identifying interactions in neuron-glia systems based on functional multicell Ca2+ imaging.Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activityEmpirical Bayesian significance measure of neuronal spike response.On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs.A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation.Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions.An overview of Bayesian methods for neural spike train analysis.Neural representation of spatial topology in the rodent hippocampus.Predicting single-neuron activity in locally connected networks.From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.Computational search for hypotheses concerning the endocannabinoid contribution to the extinction of fear conditioning.Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modelingAssessing neuronal interactions of cell assemblies during general anesthesia.Modeling task-specific neuronal ensembles improves decoding of grasp.Systems approaches to optimizing deep brain stimulation therapies in Parkinson's disease.A common goodness-of-fit framework for neural population models using marked point process time-rescaling
P2860
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P2860
Statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data.
description
2010 nî lūn-bûn
@nan
2010 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年学术文章
@wuu
2010年学术文章
@zh-cn
2010年学术文章
@zh-hans
2010年学术文章
@zh-my
2010年学术文章
@zh-sg
2010年學術文章
@yue
name
Statistical inference for asse ...... bles with sparse spiking data.
@ast
Statistical inference for asse ...... bles with sparse spiking data.
@en
type
label
Statistical inference for asse ...... bles with sparse spiking data.
@ast
Statistical inference for asse ...... bles with sparse spiking data.
@en
prefLabel
Statistical inference for asse ...... bles with sparse spiking data.
@ast
Statistical inference for asse ...... bles with sparse spiking data.
@en
P2860
P50
P1476
Statistical inference for asse ...... bles with sparse spiking data.
@en
P2093
Emery N Brown
Soumya Ghosh
P2860
P304
P356
10.1109/TNSRE.2010.2086079
P577
2010-10-11T00:00:00Z