Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems
From MaRDI portal
Publication:3015447
DOI10.1162/NECO_A_00118zbMath1217.92026WikidataQ43466397 ScholiaQ43466397MaRDI QIDQ3015447
Liam Paninski, Don Katz, Sean Escola, Alfredo Fontanini
Publication date: 13 July 2011
Published in: Neural Computation (Search for Journal in Brave)
Full work available at URL: http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00118
Applications of mathematical programming (90C90) Neural biology (92C20) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)
Related Items (4)
Attractor-state itinerancy in neural circuits with synaptic depression ⋮ An Empirical Model for Reliable Spiking Activity ⋮ Efficient methods for sampling spike trains in networks of coupled neurons ⋮ Hidden Markov Models Predict the Future Choice Better Than a PSTH-Based Method
Cites Work
- A Spike-Train Probability Model
- Fitting hidden semi-Markov models to breakpoint rainfall data
- Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States
- Hidden Markov modelling of simultaneously recorded cells in the associative cortex of behaving monkeys
- Estimating a State-Space Model from Point Process Observations
- Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering
- Monte Carlo EM Estimation for Time Series Models Involving Counts
- A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
This page was built for publication: Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems
