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Probabilistic Encoding Models for Multivariate Neural Data
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DOI：10.3389/fncir.2019.00001.png)
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Abstract
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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.
Keywords:
neural coding
calcium imaging
population code
brain-computer interfaces
generalized linear model
Gaussian process
factor analysis
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