Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons

DOI10.1162/NECO.2008.06-07-540zbMath1138.92009OpenAlexW2168024814WikidataQ41823731 ScholiaQ41823731MaRDI QIDQ3510939

Shinsuke Koyama, Robert E. Kass

Publication date: 3 July 2008

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

Full work available at URL: http://europepmc.org/articles/pmc2715549

zbMATH Keywords

Kullback-Leibler divergence

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural biology (92C20) Applications of Markov renewal processes (reliability, queueing networks, etc.) (60K20)

Related Items (5)

Information Transmission Using Non-Poisson Regular Firing ⋮ The effect of interspike interval statistics on the information gain under the rate coding hypothesis ⋮ Bayesian decoding of neural spike trains ⋮ On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship ⋮ Applying the Multivariate Time-Rescaling Theorem to Neural Population Models

Cites Work

• The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis
• The parameters of the stochastic leaky integrate-and-fire neuronal model
• A Spike-Train Probability Model
• Synchronization of the Neural Response to Noisy Periodic Synaptic Input
• Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
• Empirical Bayes interpretations of random point events
• Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity

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