Pages that link to "Item:Q5706655"

The following pages link to Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity (Q5706655):

Displaying 40 items.

• Hawkes processes on large networks (Q259574) (← links)
• Statistical inference versus mean field limit for Hawkes processes (Q286219) (← links)
• Efficient methods for sampling spike trains in networks of coupled neurons (Q652350) (← links)
• A stimulus-dependent connectivity analysis of neuronal networks (Q843292) (← links)
• A mathematical framework for inferring connectivity in probabilistic neuronal networks (Q876058) (← links)
• Semi-parametric dynamic time series modelling with applications to detecting neural dynamics (Q965143) (← links)
• Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions (Q1704742) (← links)
• Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity (Q1704910) (← links)
• A convergence criterion for systems of point processes from the convergence of their stochastic intensities (Q2064803) (← links)
• Mean field limits for interacting Hawkes processes in a diffusive regime (Q2073204) (← links)
• Nonparametric Bayesian estimation for multivariate Hawkes processes (Q2215756) (← links)
• Goodness-of-fit tests and nonparametric adaptive estimation for spike train analysis (Q2251601) (← links)
• NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events (Q2631504) (← links)
• Predicting single-neuron activity in locally connected networks (Q2840862) (← links)
• Graphical Modeling for Multivariate Hawkes Processes with Nonparametric Link Functions (Q2968465) (← links)
• Applying the Multivariate Time-Rescaling Theorem to Neural Population Models (Q3016184) (← links)
• A Systematic Method for Configuring VLSI Networks of Spiking Neurons (Q3116937) (← links)
• Inferring Neuronal Couplings From Spiking Data Using a Systematic Procedure With a Statistical Criterion (Q3386425) (← links)
• (Q3402229) (← links)
• Distinguishing Causal Interactions in Neural Populations (Q3440420) (← links)
• Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons (Q3510939) (← links)
• Measuring asymmetric temporal interdependencies in simulated and biological networks (Q3531702) (← links)
• On the Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles (Q3562864) (← links)
• Nonparametric Modeling of Neural Point Processes via Stochastic Gradient Boosting Regression (Q3591513) (← links)
• Identifying Functional Connectivity in Large-Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach (Q3612129) (← links)
• Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness (Q3628007) (← links)
• (Q5134569) (← links)
• (Q5134573) (← links)
• (Q5134581) (← links)
• (Q5134628) (← links)
• (Q5134873) (← links)
• Nonlinear Modeling of Neural Interaction for Spike Prediction Using the Staged Point-Process Model (Q5157274) (← links)
• Omitted variable bias in GLMs of neural spiking activity (Q5157275) (← links)
• Multi-Input, Multi-Output Neuronal Mode Network Approach to Modeling the Encoding Dynamics and Functional Connectivity of Neural Systems (Q5214348) (← links)
• Point-Process Principal Components Analysis via Geometric Optimization (Q5327158) (← links)
• Encoding Through Patterns: Regression Tree–Based Neuronal Population Models (Q5378240) (← links)
• Likelihood Methods for Point Processes with Refractoriness (Q5378320) (← links)
• Identification of Stable Spike-Timing-Dependent Plasticity from Spiking Activity with Generalized Multilinear Modeling (Q5380586) (← links)
• Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings (Q5380838) (← links)
• Spike Train Decoding Without Spike Sorting (Q5460196) (← links)