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Bayesian latent structure discovery from multi-neuron recordings

Author(s): Linderman, Scott W; Adams, Ryan P; Pillow, Jonathan

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dc.contributor.authorLinderman, Scott W-
dc.contributor.authorAdams, Ryan P-
dc.contributor.authorPillow, Jonathan-
dc.date.accessioned2021-10-08T19:48:21Z-
dc.date.available2021-10-08T19:48:21Z-
dc.date.issued2016-12en_US
dc.identifier.citationLinderman, Scott W., Ryan P. Adams, and Jonathan W. Pillow. "Bayesian latent structure discovery from multi-neuron recordings." In Proceedings of the 30th International Conference on Neural Information Processing Systems (2016): pp. 2010-2018.en_US
dc.identifier.issn1049-5258-
dc.identifier.urihttps://proceedings.neurips.cc/paper/2016/file/708f3cf8100d5e71834b1db77dfa15d6-Paper.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1s83x-
dc.description.abstractNeural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties. However, traditional methods for dimensionality reduction and clustering are ill-suited to recovering the structure underlying the organization of neural circuits. In particular, they do not take advantage of the rich temporal dependencies in multi-neuron recordings and fail to account for the noise in neural spike trains. Here we describe new tools for inferring latent structure from simultaneously recorded spike train data using a hierarchical extension of a multi-neuron point process model commonly known as the generalized linear model (GLM). Our approach combines the GLM with flexible graph-theoretic priors governing the relationship between latent features and neural connectivity patterns. Fully Bayesian inference via P6lya-gamma augmentation of the resulting model allows us to classify neurons and infer latent dimensions of circuit organization from correlated spike trains. We demonstrate the effectiveness of our method with applications to synthetic data and multi-neuron recordings in primate retina, revealing latent patterns of neural types and locations from spike trains alone.en_US
dc.format.extent2010 - 2018en_US
dc.language.isoen_USen_US
dc.relation.ispartofInternational Conference on Neural Information Processing Systemsen_US
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titleBayesian latent structure discovery from multi-neuron recordingsen_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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