Dynamic alignment models for neural coding
| Autor: | Kollmorgen Sepp, Hahnloser Richard H R |
|---|---|
| Přispěvatelé: | University of Zurich, Kollmorgen, Sepp |
| Rok vydání: | 2013 |
| Předmět: |
Time Factors
Models Neurological 2804 Cellular and Molecular Neuroscience Normal Distribution Songbirds 1311 Genetics 1312 Molecular Biology Animals Humans Poisson Distribution lcsh:QH301-705.5 Biology 10194 Institute of Neuroinformatics Probability Neurons Computational Neuroscience Coding Mechanisms Quantitative Biology::Neurons and Cognition Brain Markov Chains Rats 1105 Ecology Evolution Behavior and Systematics lcsh:Biology (General) Nonlinear Dynamics Linear Models 570 Life sciences biology 2303 Ecology Algorithms 2611 Modeling and Simulation 1703 Computational Theory and Mathematics Research Article Neuroscience |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, 10 (3) PLoS Computational Biology, Vol 10, Iss 3, p e1003508 (2014) PLoS computational biology |
| ISSN: | 1553-7358 1553-734X |
| Popis: | Recently, there have been remarkable advances in modeling the relationships between the sensory environment, neuronal responses, and behavior. However, most models cannot encompass variable stimulus-response relationships such as varying response latencies and state or context dependence of the neural code. Here, we consider response modeling as a dynamic alignment problem and model stimulus and response jointly by a mixed pair hidden Markov model (MPH). In MPHs, multiple stimulus-response relationships (e.g., receptive fields) are represented by different states or groups of states in a Markov chain. Each stimulus-response relationship features temporal flexibility, allowing modeling of variable response latencies, including noisy ones. We derive algorithms for learning of MPH parameters and for inference of spike response probabilities. We show that some linear-nonlinear Poisson cascade (LNP) models are a special case of MPHs. We demonstrate the efficiency and usefulness of MPHs in simulations of both jittered and switching spike responses to white noise and natural stimuli. Furthermore, we apply MPHs to extracellular single and multi-unit data recorded in cortical brain areas of singing birds to showcase a novel method for estimating response lag distributions. MPHs allow simultaneous estimation of receptive fields, latency statistics, and hidden state dynamics and so can help to uncover complex stimulus response relationships that are subject to variable timing and involve diverse neural codes. PLoS Computational Biology, 10 (3) ISSN:1553-734X ISSN:1553-7358 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|