Pages that link to "Item:Q1104265"

The following pages link to Maximum likelihood analysis of spike trains of interacting nerve cells (Q1104265):

Displaying 50 items.

• The time-rescaling theorem and its application to neural spike train data analysis (Q122872) (← links)
• Measuring the association of stationary point processes using spectral analysis techniques (Q257467) (← links)
• Maximum-likelihood \(q\)-estimator uncovers the role of potassium at neuromuscular junctions (Q310136) (← links)
• Infinite systems of interacting chains with memory of variable length -- a stochastic model for biological neural nets (Q358679) (← links)
• On a spike train probability model with interacting neural units (Q395724) (← links)
• Graphical modelling of multivariate time series (Q438963) (← links)
• Parametric estimation for a simple branching diffusion process (Q600204) (← links)
• A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data (Q641075) (← links)
• Assessment of synchrony in multiple neural spike trains using loglinear point process models (Q641077) (← links)
• Efficient methods for sampling spike trains in networks of coupled neurons (Q652350) (← links)
• On the statistics of binned neural point processes: The Bernoulli approximation and AR representation of the PST histogram (Q922384) (← links)
• Statistical methods and software for risk assessment: applications to a neurophysiological data set (Q957200) (← links)
• A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models (Q999378) (← links)
• The quantitative single-neuron modeling competition (Q999399) (← links)
• Maximum likelihood estimations in a nonlinear self-exciting point process model (Q1091726) (← links)
• Continuous functions determined by spike trains of a neuron subject to stimulation (Q1099798) (← links)
• Maximum likelihood identification of neural point process systems (Q1111960) (← links)
• Minimum mean square error estimation of connectivity in biological neural networks (Q1179398) (← links)
• A theoretical basis for conditional probability analyses of neural discharge activity (Q1192048) (← links)
• Stochastic methods for neural systems (Q1200640) (← links)
• Adjusted regularization in latent graphical models: application to multiple-neuron spike count data (Q1624826) (← links)
• Fast inference in generalized linear models via expected log-likelihoods (Q1704759) (← links)
• Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity (Q1704910) (← links)
• Consistent estimation of complete neuronal connectivity in large neuronal populations using sparse ``shotgun'' neuronal activity sampling (Q1705032) (← links)
• Estimating the interaction graph of stochastic neural dynamics (Q1715553) (← links)
• Non-parametric estimation of the spiking rate in systems of interacting neurons (Q1744222) (← links)
• A new analytical method of studying post-synaptic currents (Q1808678) (← links)
• Parameter estimation in a model for multidimensional recording of neuronal data: a Gibbsian approximation approach (Q1889257) (← links)
• Interacting Hawkes processes with multiplicative inhibition (Q2132533) (← links)
• The Hitchhiker's guide to nonlinear filtering (Q2176457) (← links)
• Nonparametric Bayesian estimation for multivariate Hawkes processes (Q2215756) (← links)
• Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing (Q2251600) (← links)
• A result of metastability for an infinite system of spiking neurons (Q2283156) (← links)
• A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input (Q2373188) (← links)
• Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses (Q2451077) (← links)
• The most likely voltage path and large deviations approximations for integrate-and-fire neurons (Q2500243) (← links)
• A numerical study of the time of extinction in a class of systems of spiking neurons (Q2677654) (← links)
• A spike-train probability model (Q2746335) (← links)
• Computing the optimally fitted spike train for a synapse (Q2784816) (← links)
• Neuronal Spike Train Analysis Using Gaussian Process Models (Q2800200) (← links)
• Predicting single-neuron activity in locally connected networks (Q2840862) (← links)
• Estimating parameters of generalized integrate-and-fire neurons from the maximum likelihood of spike trains (Q2887011) (← links)
• Applying the Multivariate Time-Rescaling Theorem to Neural Population Models (Q3016184) (← links)
• Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains (Q3070780) (← links)
• Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains (Q3070781) (← links)
• A Systematic Method for Configuring VLSI Networks of Spiking Neurons (Q3116937) (← links)
• Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method (Q3301544) (← links)
• Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States (Q3497606) (← links)
• Bayesian Nonparametric Modeling for Comparison of Single-Neuron Firing Intensities (Q3564584) (← links)
• Nonconvergence in Logistic and Poisson Models for Neural Spiking (Q3564829) (← links)