Pages that link to "Item:Q122872"
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The following pages link to The time-rescaling theorem and its application to neural spike train data analysis (Q122872):
Displaying 50 items.
- ppdiag (Q54366) (← links)
- Modeling neural activity with cumulative damage distributions (Q310175) (← links)
- Estimating summary statistics in the spike-train space (Q385299) (← links)
- Introduction to neural spike train data for phase-amplitude analysis (Q470449) (← links)
- Assessment of synchrony in multiple neural spike trains using loglinear point process models (Q641077) (← links)
- A mathematical framework for inferring connectivity in probabilistic neuronal networks (Q876058) (← links)
- A new framework for Euclidean summary statistics in the neural spike train space (Q902903) (← links)
- Bayesian decoding of neural spike trains (Q904063) (← links)
- Design principles of sensory processing in cerebellum-like structures (Q937767) (← links)
- Information transmission in oscillatory neural activity (Q999425) (← links)
- Discovery, visualization and performance analysis of enterprise workflow (Q1019892) (← links)
- A common goodness-of-fit framework for neural population models using marked point process time-rescaling (Q1628363) (← links)
- Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions (Q1704742) (← links)
- Firing-rate models capture essential response dynamics of LGN relay cells (Q1704744) (← links)
- Fast maximum likelihood estimation using continuous-time neural point process models (Q1704886) (← links)
- Dirichlet depths for point process (Q2044426) (← links)
- Markov-modulated Hawkes processes for modeling sporadic and bursty event occurrences in social interactions (Q2154225) (← links)
- A two-state neuronal model with alternating exponential excitation (Q2160705) (← links)
- Goodness-of-fit tests and nonparametric adaptive estimation for spike train analysis (Q2251601) (← links)
- Nonparametric self-exciting models for computer network traffic (Q2302486) (← links)
- Information processing in the LGN: a comparison of neural codes and cell types (Q2317476) (← links)
- Inference for ETAS models with non-Poissonian mainshock arrival times (Q2329808) (← links)
- Charactering neural spiking activity evoked by acupuncture through state-space model (Q2337566) (← links)
- Stability of point process spiking neuron models (Q2418230) (← links)
- Hawkes processes in insurance: risk model, application to empirical data and optimal investment (Q2665846) (← links)
- A point-process model of tapping along to difficult rhythms (Q2677697) (← links)
- Mapping of visual receptive fields by tomographic reconstruction (Q2840859) (← links)
- Improved similarity measures for small sets of spike trains (Q2887004) (← links)
- Optimizing time histograms for non-Poissonian spike trains (Q2887006) (← links)
- Detection of Hidden Structures in Nonstationary Spike Trains (Q3015451) (← links)
- Applying the Multivariate Time-Rescaling Theorem to Neural Population Models (Q3016184) (← links)
- Discrete Time Rescaling Theorem: Determining Goodness of Fit for Discrete Time Statistical Models of Neural Spiking (Q3057199) (← links)
- Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains (Q3070780) (← links)
- Extracting State Transition Dynamics from Multiple Spike Trains Using Hidden Markov Models with Correlated Poisson Distribution (Q3164224) (← links)
- Automatic Spike Sorting Using Tuning Information (Q3182478) (← links)
- Binless Kernel Machine: Modeling Spike Train Transformation for Cognitive Neural Prostheses (Q3386405) (← links)
- Assessing Goodness-of-Fit in Marked-Point Process Models of Neural Population Coding via Time and Rate Rescaling (Q3386424) (← links)
- Sequential Monte Carlo Point-Process Estimation of Kinematics from Neural Spiking Activity for Brain-Machine Interfaces (Q3399366) (← links)
- Estimating Spiking Irregularities Under Changing Environments (Q3417426) (← links)
- A State-Space Analysis for Reconstruction of Goal-Directed Movements Using Neural Signals (Q3417431) (← links)
- Microscopic approach of a time elapsed neural model (Q3451155) (← links)
- Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States (Q3497606) (← links)
- Maximally Reliable Markov Chains Under Energy Constraints (Q3497607) (← links)
- Estimating Instantaneous Irregularity of Neuronal Firing (Q3497609) (← links)
- Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons (Q3510939) (← links)
- The Computational Structure of Spike Trains (Q3562862) (← links)
- Bayesian Nonparametric Modeling for Comparison of Single-Neuron Firing Intensities (Q3564584) (← links)
- A Computationally Efficient Method for Nonparametric Modeling of Neural Spiking Activity with Point Processes (Q3583488) (← links)
- Nonparametric Modeling of Neural Point Processes via Stochastic Gradient Boosting Regression (Q3591513) (← links)
- A Rate and History-Preserving Resampling Algorithm for Neural Spike Trains (Q3628008) (← links)
