Likelihood Methods for Point Processes with Refractoriness

DOI10.1162/NECO_a_00548zbMath1415.92043OpenAlexW2113609708WikidataQ47952594 ScholiaQ47952594MaRDI QIDQ5378320

Riccardo Barbieri, Luca Citi, Emery N. Brown, Demba Bocar Ba

Publication date: 12 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00548

zbMATH Keywords

point-process model of neuronsspiking neural populations

Mathematics Subject Classification ID

Neural biology (92C20) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (7)

Modeling neural activity with cumulative damage distributions ⋮ A stochastic-statistical residential burglary model with independent Poisson clocks ⋮ Fast maximum likelihood estimation using continuous-time neural point process models ⋮ Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity ⋮ A Stochastic-Statistical Residential Burglary Model with Finite Size Effects ⋮ On Quadrature Methods for Refractory Point Process Likelihoods ⋮ Enhanced Signal Detection by Adaptive Decorrelation of Interspike Intervals

Uses Software

• glmnet
• Fahrmeir

Cites Work

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• The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis
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• Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains
• A State-Space Analysis for Reconstruction of Goal-Directed Movements Using Neural Signals
• Nonconvergence in Logistic and Poisson Models for Neural Spiking
• Identifying Functional Connectivity in Large-Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach
• Dynamic Analyses of Information Encoding in Neural Ensembles
• Approximating Point Process Likelihoods with GLIM
• Polynomial Roots from Companion Matrix Eigenvalues
• Optimal Signal Estimation in Neuronal Models
• Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity
• An Introduction to the Theory of Point Processes
• Statistical models based on counting processes
• Multivariate statistical modelling based on generalized linear models.

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