标题
Applying the Multivariate Time-Rescaling Theorem to Neural Population Models
作者
关键词
-
出版物
NEURAL COMPUTATION
Volume 23, Issue 6, Pages 1452-1483
出版商
MIT Press - Journals
发表日期
2011-05-07
DOI
10.1162/neco_a_00126
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Discrete Time Rescaling Theorem: Determining Goodness of Fit for Discrete Time Statistical Models of Neural Spiking
- (2010) Robert Haslinger et al. NEURAL COMPUTATION
- Decorrelated Neuronal Firing in Cortical Microcircuits
- (2010) A. S. Ecker et al. SCIENCE
- Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models
- (2009) Shinsuke Koyama et al. JOURNAL OF COMPUTATIONAL NEUROSCIENCE
- A new look at state-space models for neural data
- (2009) Liam Paninski et al. JOURNAL OF COMPUTATIONAL NEUROSCIENCE
- Generation of Spatiotemporally Correlated Spike Trains and Local Field Potentials Using a Multivariate Autoregressive Process
- (2009) Diego A. Gutnisky et al. JOURNAL OF NEUROPHYSIOLOGY
- The Structure of Large-Scale Synchronized Firing in Primate Retina
- (2009) J. Shlens et al. JOURNAL OF NEUROSCIENCE
- Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes
- (2009) Wilson Truccolo et al. NATURE NEUROSCIENCE
- Extracting information from neuronal populations: information theory and decoding approaches
- (2009) Rodrigo Quian Quiroga et al. NATURE REVIEWS NEUROSCIENCE
- Estimating Instantaneous Irregularity of Neuronal Firing
- (2009) Takeaki Shimokawa et al. NEURAL COMPUTATION
- Generation of Spike Trains with Controlled Auto- and Cross-Correlation Functions
- (2009) Michael Krumin et al. NEURAL COMPUTATION
- Direct Estimation of Inhomogeneous Markov Interval Models of Spike Trains
- (2009) Daniel K. Wójcik et al. NEURAL COMPUTATION
- Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness
- (2009) Taro Toyoizumi et al. NEURAL COMPUTATION
- Beyond Poisson: Increased Spike-Time Regularity across Primate Parietal Cortex
- (2009) Gaby Maimon et al. NEURON
- Ising model for neural data: Model quality and approximate methods for extracting functional connectivity
- (2009) Yasser Roudi et al. PHYSICAL REVIEW E
- Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations
- (2009) O. Marre et al. PHYSICAL REVIEW LETTERS
- Ruling out and ruling in neural codes
- (2009) A. L. Jacobs et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- How Good Are Neuron Models?
- (2009) W. Gerstner et al. SCIENCE
- Analyzing Short-Term Noise Dependencies of Spike-Counts in Macaque Prefrontal Cortex Using Copulas and the Flashlight Transformation
- (2009) Arno Onken et al. PLoS Computational Biology
- Inferring functional connections between neurons
- (2008) Ian H Stevenson et al. CURRENT OPINION IN NEUROBIOLOGY
- NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events
- (2008) Gordon Pipa et al. JOURNAL OF COMPUTATIONAL NEUROSCIENCE
- Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics
- (2008) Gabriela Czanner et al. JOURNAL OF NEUROPHYSIOLOGY
- A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks In Vitro
- (2008) A. Tang et al. JOURNAL OF NEUROSCIENCE
- Spatio-temporal correlations and visual signalling in a complete neuronal population
- (2008) Jonathan W. Pillow et al. NATURE
- Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons
- (2008) Shinsuke Koyama et al. NEURAL COMPUTATION
- Sequential Optimal Design of Neurophysiology Experiments
- (2008) Jeremy Lewi et al. NEURAL COMPUTATION
- Generating Spike Trains with Specified Correlation Coefficients
- (2008) Jakob H. Macke et al. NEURAL COMPUTATION
- A benchmark test for a quantitative assessment of simple neuron models
- (2007) Renaud Jolivet et al. JOURNAL OF NEUROSCIENCE METHODS
- Valuations for Spike Train Prediction
- (2007) Vladimir Itskov et al. NEURAL COMPUTATION
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started