Daniel Tranchina, Ph.D., B.A.
Affiliations: | Mathematics, Biology, Center for Neuroscience | New York University, New York, NY, United States |
Website:
http://biology.as.nyu.edu/object/DanielTranchina.htmlGoogle:
"Daniel Tranchina"Mean distance: 16.62 (cluster 17)
Children
Sign in to add traineeEvan C. Haskell | grad student | 2000 | NYU |
Duane Q. Nykamp | grad student | 2000 | NYU |
Felix J. Apfaltrer | grad student | 2005 | NYU |
Cheng Ly | grad student | 2002-2007 | NYU |
Adam R. Stinchcombe | grad student | 2013 | NYU |
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Publications
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Athanasiadou R, Neymotin B, Brandt N, et al. (2019) A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory. Plos Computational Biology. 15: e1006794 |
Warren TJ, Van Hook MJ, Tranchina D, et al. (2016) Kinetics of Inhibitory Feedback from Horizontal Cells to Photoreceptors: Implications for an Ephaptic Mechanism. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 36: 10075-88 |
Steele SA, Tranchina D, Rinzel J. (2014) An alternating renewal process describes the buildup of perceptual segregation. Frontiers in Computational Neuroscience. 8: 166 |
Ekanadham C, Tranchina D, Simoncelli EP. (2014) A unified framework and method for automatic neural spike identification. Journal of Neuroscience Methods. 222: 47-55 |
Chen CK, Woodruff ML, Chen FS, et al. (2012) Modulation of mouse rod response decay by rhodopsin kinase and recoverin. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 32: 15998-6006 |
Stinchcombe AR, Peskin CS, Tranchina D. (2012) Population density approach for discrete mRNA distributions in generalized switching models for stochastic gene expression. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 85: 061919 |
Ekanadham C, Tranchina D, Simoncelli E. (2011) Recovery of sparse translation-invariant signals with continuous basis pursuit. Ieee Transactions On Signal Processing : a Publication of the Ieee Signal Processing Society. 59 |
Tranchina D. (2010) Population Density Methods in Large-Scale Neural Network Modelling Stochastic Methods in Neuroscience |
Ly C, Tranchina D. (2009) Spike train statistics and dynamics with synaptic input from any renewal process: a population density approach. Neural Computation. 21: 360-96 |
Goodhill G, Baker C, Balasubramanian V, et al. (2008) Network: Computation in Neural Systems: Editorial Network: Computation in Neural Systems. 19: 1-2 |