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Neural networks / Nervous system / Neuron / Neural coding / Interneuron / Inhibitory postsynaptic potential / Action potential / Brain / Nonspiking neurons / Computational neuroscience / Biology / Neuroscience


A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data Jasper Snoek∗ Harvard University
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Document Date: 2014-11-25 16:11:32


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Kernel Matrix / /

Company

Neural Systems / Neural Information Processing Systems / MIT Press / E.S. Chornoboy L.P. / /

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Event

Man-Made Disaster / /

Facility

Neural Spiking Data Jasper Snoek∗ Harvard University / University of Toronto / Richard S. Zemel University / /

IndustryTerm

point process systems / /

Organization

Harvard University / MIT / University of Toronto / /

Person

Emily Fox / Carl E. Rasmussen / James Zou / William Bialek / Christopher Williams / Kenji Mizuseki / Anton Sirota / Probabilistic Models / Poisson / J. Ben Hough / Eero P. Simoncelli / David J. Heeger / Ben Taskar / Alex Kulesza / Odelia Schwartz / Ryan P. Adams / Liam Paninski / David R. Brillinger / H. Affandi / Eva Pastalkova / Elad Schneidman / Michael J. Berry / Manjunath Krishnapur / Matteo Carandini / Ronen Segev / Yuval Peres / Blint Vir / /

Position

Determinantal Point Process Latent Variable Model for Inhibition / generalized linear model / head / /

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Kansas / /

PublishedMedium

The Journal of Neuroscience / Machine Learning / /

Technology

Neuroscience / machine learning / /

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