An overview of Bayesian methods for neural spike train analysis. - Wikidata - wikimedia - TriplyDB

An overview of Bayesian methods for neural spike train analysis.

An overview of Bayesian methods for neural spike train analysis.

http://www.wikidata.org/entity/Q37375141

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Sequential data assimilation for single-molecule FRET photon-counting data.Estimating latent attentional states based on simultaneous binary and continuous behavioral measures.The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation.Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks A hidden Markov model for decoding and the analysis of replay in spike trains.A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields.The Cluster Variation Method: A Primer for Neuroscientists.Comparing offline decoding performance in physiologically defined neuronal classes.Deciphering neuronal population codes for acute thermal pain.Skellam process with resetting: a neural spike train model.

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An overview of Bayesian methods for neural spike train analysis.

http://www.wikidata.org/entity/Q37375141

description

2013 nî lūn-bûn

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2013年の論文

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2013年学术文章

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2013年学术文章

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2013年学术文章

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2013年学术文章

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2013年學術文章

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2013年學術文章

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2013年學術文章

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name

An overview of Bayesian methods for neural spike train analysis.

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An overview of Bayesian methods for neural spike train analysis.

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type

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An overview of Bayesian methods for neural spike train analysis.

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An overview of Bayesian methods for neural spike train analysis.

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An overview of Bayesian methods for neural spike train analysis.

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An overview of Bayesian methods for neural spike train analysis.

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An overview of Bayesian methods for neural spike train analysis

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Zhe Chen

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P2860

A Granger causality measure for point process models of ensemble neural spiking activity

Multiple neural spike train data analysis: state-of-the-art and future challenges

Simulation-based model selection for dynamical systems in systems and population biology.

Ruling out and ruling in neural codes

Weak pairwise correlations imply strongly correlated network states in a neural population

Uncovering spatial topology represented by rat hippocampal population neuronal codes

Signal processing for neural spike trains

Reversible jump Markov chain Monte Carlo computation and Bayesian model determination

State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data

Incorporating naturalistic correlation structure improves spectrogram reconstruction from neuronal activity in the songbird auditory midbrain.

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