Probabilistic Encoding Models for Multivariate Neural Data

A key problem in systems neuroscience is to characterize how populations of neurons encode information in their patterns of activity. An understanding of the encoding process is essential both for gaining insight into the origins of perception and for the development of brain-computer interfaces. Ho...

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Bibliographic Details
Published in:Frontiers in neural circuits 2019-01, Vol.13, p.1-1
Main Authors: Triplett, Marcus A, Goodhill, Geoffrey J
Format: Article
Language:English
Subjects:
Animals
Brain-Computer Interfaces
Calcium - metabolism
Calcium imaging
Electrophysiology
Gaussian process
generalized linear model
Generalized linear models
Humans
Information processing
Interfaces
Learning algorithms
Mathematical models
Models, Neurological
Nerve Net
Nervous system
neural coding
Neural Networks, Computer
Neuroimaging
Neurons
Neurons - physiology
Neuroscience
Noise
Population
population code
Probability
Statistical analysis
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Summary:A key problem in systems neuroscience is to characterize how populations of neurons encode information in their patterns of activity. An understanding of the encoding process is essential both for gaining insight into the origins of perception and for the development of brain-computer interfaces. However, this characterization is complicated by the highly variable nature of neural responses, and thus usually requires probabilistic methods for analysis. Drawing on techniques from statistical modeling and machine learning, we review recent methods for extracting important variables that quantitatively describe how sensory information is encoded in neural activity. In particular, we discuss methods for estimating receptive fields, modeling neural population dynamics, and inferring low dimensional latent structure from a population of neurons, in the context of both electrophysiology and calcium imaging data.
ISSN:1662-5110
1662-5110
DOI:10.3389/fncir.2019.00001