Dynamic Alignment Models for Neural Coding

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 neur...

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Veröffentlicht in:PLoS computational biology Jg. 10; H. 3; S. e1003508
Hauptverfasser: Kollmorgen, Sepp, Hahnloser, Richard H. R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.03.2014
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Animals
Biology
Brain - physiology
Hidden Markov models
Humans
Linear Models
Markov Chains
Models, Neurological
Neural circuitry
Neurological research
Neurons - physiology
Noise
Nonlinear Dynamics
Normal Distribution
Parameter estimation
Poisson Distribution
Probability
Rats
Songbirds
Time Factors
Switzerland
Brain
Markov Chains
Neurons
Humans
Probability
Rats
Linear Models
Normal Distribution
Algorithms
Animals
Time Factors
Poisson Distribution
Models, Neurological
Nonlinear Dynamics
Songbirds
ISSN:1553-7358, 1553-734X, 1553-7358
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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The authors have declared that no competing interests exist.
Conceived and designed the experiments: SK RHRH. Analyzed the data: SK. Contributed reagents/materials/analysis tools: SK. Wrote the paper: SK RHRH.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1003508