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Latent Variable Modeling of Neural Population Dynamics

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Dynamic Neuroscience

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Abstract

Neural activity is noisy (“stochastic”) and dynamic at various spatiotemporal scales. We consider a general class of latent variable models for characterizing neuronal population dynamics or analyzing various sorts of neural data. The inference of latent variable models can lead to novel solutions for signal detection, neural decoding, denoising, dimensionality reduction, and data visualization. We review general modeling and inference strategies for latent variable models. Finally, we illustrate our methods with several neuroscience applications using population spike trains recorded from the animal’s hippocampus and neocortices.

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Acknowledgements

I would like to thank Emery N. Brown for the intellectual inspiration during my postdoctoral career and all coauthors who have contributed to previously published work. I also thank S.L. Hu and S.Z. Liu for assistance in preparing some figures. Reproduction of some copyrighted materials is granted from publishers. The work was partially supported by an NSF-CRCNS award IIS-1307645 from the US National Science Foundation and an NIH-CRCNS award R01-NS100065 from the NINDS.

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  1. New York University School of Medicine, New York, NY, USA

    Zhe Chen

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  1. School of Medicine, New York University, New York, NY, USA

    Zhe Chen

  2. Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA

    Sridevi V. Sarma

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Chen, Z. (2018). Latent Variable Modeling of Neural Population Dynamics. In: Chen, Z., Sarma, S.V. (eds) Dynamic Neuroscience. Springer, Cham. https://doi.org/10.1007/978-3-319-71976-4_3

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