The following pages link to A spike-train probability model (Q2746335):
Displaying 50 items.
- The time-rescaling theorem and its application to neural spike train data analysis (Q122872) (← links)
- Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure (Q127507) (← links)
- Modeling neural activity with cumulative damage distributions (Q310175) (← links)
- Estimating summary statistics in the spike-train space (Q385299) (← links)
- The effect of interspike interval statistics on the information gain under the rate coding hypothesis (Q395709) (← links)
- On a spike train probability model with interacting neural units (Q395724) (← links)
- Modeling some properties of circadian rhythms (Q395738) (← links)
- Introduction to neural spike train data for phase-amplitude analysis (Q470449) (← links)
- Joint probability-based neuronal spike train classification (Q634421) (← links)
- Modeling stochastic spike train responses of neurons: An extended Wiener series analysis of pigeon auditory nerve fibers (Q678753) (← links)
- A new framework for Euclidean summary statistics in the neural spike train space (Q902903) (← links)
- Bayesian decoding of neural spike trains (Q904063) (← links)
- Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds (Q965330) (← links)
- Discovery, visualization and performance analysis of enterprise workflow (Q1019892) (← links)
- Continuous functions determined by spike trains of a neuron subject to stimulation (Q1099798) (← links)
- Maximum likelihood analysis of spike trains of interacting nerve cells (Q1104265) (← links)
- On the description of neuronal output properties using spike train data (Q1117159) (← links)
- A theoretical basis for conditional probability analyses of neural discharge activity (Q1192048) (← links)
- A nonstationary Poisson point process describes the sequence of action potentials over long time scales in lateral-superior-olive auditory neurons (Q1315265) (← links)
- Modeling neural activity using the generalized inverse Gaussian distribution (Q1376195) (← 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)
- Linear-nonlinear-time-warp-Poisson models of neural activity (Q1710562) (← links)
- Construction and analysis of non-Gaussian spatial models of neural spiking activity (Q1851639) (← links)
- Spike train analysis for single trial data (Q1851739) (← links)
- Modeling neuronal firing in epilepsy: fitting Hawkes processes to single-unit activity (Q1982741) (← links)
- Multineuron spike train analysis with R-convolution linear combination kernel (Q2179825) (← links)
- A Markov model for interspike interval distributions of auditory cortical neurons that do not show periodic firings (Q2372990) (← links)
- Stability of point process spiking neuron models (Q2418230) (← links)
- Some theoretical results on neural spike train probability models (Q2473079) (← links)
- Damped oscillations of the probability of random events followed by absolute refractory period: exact analytical results (Q2675505) (← links)
- A numerical study of the time of extinction in a class of systems of spiking neurons (Q2677654) (← links)
- On the relation between encoding and decoding of neuronal spikes (Q2919412) (← links)
- Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems (Q3015447) (← 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)
- Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons (Q3510939) (← links)
- A Continuous Entropy Rate Estimator for Spike Trains Using a K-Means-Based Context Tree (Q3556789) (← links)
- Bayesian Nonparametric Modeling for Comparison of Single-Neuron Firing Intensities (Q3564584) (← links)
- Nonconvergence in Logistic and Poisson Models for Neural Spiking (Q3564829) (← links)
- Conditional Mixture Model for Correlated Neuronal Spikes (Q3576271) (← 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)
- Spike Train Statistics and Dynamics with Synaptic Input from any Renewal Process: A Population Density Approach (Q3612124) (← links)
- A Reproducing Kernel Hilbert Space Framework for Spike Train Signal Processing (Q3612128) (← links)
- Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness (Q3628007) (← links)
- A Rate and History-Preserving Resampling Algorithm for Neural Spike Trains (Q3628008) (← links)
- (Q4444300) (← links)
- An Adjustment to the Time-Rescaling Method for Application to Short-Trial Spike Train Data (Q4813873) (← links)
- Testing for and Estimating Latency Effects for Poisson and Non-Poisson Spike Trains (Q4832454) (← links)
