On the Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles

DOI10.1162/neco.2009.11-08-900zbMath1187.92017OpenAlexW2134971580WikidataQ42951822 ScholiaQ42951822MaRDI QIDQ3562864

Rong Jin, Yang Zhou, Seif Eldawlatly, Karim G. Oweiss

Publication date: 28 May 2010

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

Full work available at URL: http://europepmc.org/articles/pmc2794930

Mathematics Subject Classification ID

Bayesian inference (62F15) Neural biology (92C20) Systems biology, networks (92C42)

Related Items (2)

Reconstruction of sparse connectivity in neural networks from spike train covariances ⋮ A Framework for Simulating and Estimating the State and Functional Topology of Complex Dynamic Geometric Networks

Cites Work

• Unnamed Item
• Unnamed Item
• Optimization by Simulated Annealing
• The identification of point process systems
• Estimating the dimension of a model
• Partial correlation analysis for the identification of synaptic connections
• Partial directed coherence: a new concept in neural structure determination
• Response Properties of an Integrate-and-Fire Model That Receives Subthreshold Inputs
• Identifying Functional Connectivity in Large-Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach
• Measure of Correlation Orthogonal to Change in Firing Rate
• Measuring the Association of Point Processes: A Case History
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
• Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity

This page was built for publication: On the Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles