Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons
From MaRDI portal
Publication:3510939
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
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
This page was built for publication: Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons
