Casey Diekman

Moye, Matthew, & Diekman, Casey (2018). Data assimilation methods for neuronal state and parameter estimation. Journal of Mathematical Neuroscience, 8(11), 1-38.

Kass, Robert, & Amari, Shun-ichi, & Arai, Kensuke, & Brown, Emery, & Diekman, Casey, & Diesmann, Markus, & Doiron, Brent, & Eden, Uri, & Fairhall, Adrienne, & Fiddyment, Grant, & Fukai, Tomoki, & Grun, Sonja, & Harrison, Matthew, & Helias, Moritz, & Nakahara, Hiroyuki, & Teramae, Jun-nosuke, & Thomas, Peter, & Reimers, Mark, & Rodu, Jordan, & Rotstein, Horacio, & Shea-Brown, Eric, & Shimazaki, Hideaki, & Shinomoto, Shigeru, & Yu, Byron, & Kramer, Mark (2018). Computational neuroscience: mathematical and statistical perspectives. Annual Review of Statistics and its Applications, 5(1), 183-214.

Diekman, Casey, & Bose, Amitabha (2018). Reentrainment of the circadian pacemaker during jet lag: east-west asymmetry and the effects of north-south travel. Journal of Theoretical Biology, 437, 261-285.

Diekman, Casey, & Thomas, Peter, & Wilson, Christopher (2017). Eupnea, tachypnea, and autoresuscitation in a closed-loop respiratory control model. Journal of Neurophysiology,

Wegner, Sven, & Belle, Mino, & Hughes, Alun, & Diekman, Casey, & Piggins, Hugh (2017). Delayed Cryptochrome Degradation Asymmetrically Alters the Daily Rhythm in Suprachiasmatic Clock Neuron Excitability. Journal of Neuroscience,

Diekman, Casey, & Bose, Amitabha (2016). Entrainment maps: a new tool for understanding circadian oscillator models. Journal of Biological Rhythms, 31(6), 598-616.

Flourakis, Matthieu, & Kula-Eversole, Elzbieta , & Hutchison, Alan, & Han, Tae , & Aranda, Kimberly , & Moose, Devon, & White, Kevin, & Dinner, Aaron, & Lear, Bridget, & Ren, Dejian , & Diekman, Casey, & Raman, Indira, & Allada, Ravi (2015). A Conserved Bicycle Model for Circadian Clock Control of Membrane Excitability . Cell, 162(4), 836-848.

Diekman, Casey, & Dasgupta, Kohinoor, & Nair, Vijay, & Unnikrishnan, K.P. (2014). Discovering functional neuronal connectivity from serial patterns in spike train data. Neural Computation, 26(7), 1263-1297.

Diekman, Casey, & Golubitsky, Martin (2014). Network symmetry and binocular rivalry experiments. The Journal of Mathematical Neuroscience, 4(12), 29 pages.

Terman, David, & Rubin, Jonathan, & Diekman, Casey (2013). Irregular activity arises as a natural consequence of synaptic inhibition. Chaos: An Interdisciplinary Journal of Nonlinear Science, 23(4), 20 pages.

Diekman, Casey, & Belle, Mino, & Irwin, Robert, & Allen, Charles, & Piggins, Hugh, & Forger, Daniel (2013). Causes and consequences of hyperexcitation in central clock neurons. PLOS Computational Biology, 9(8), 11 pages.

Diekman, Casey, & Golubitsky, Martin, & Wang, Yunjiao (2013). Derived patterns in binocular rivalry experiments. Journal of Mathematical Neuroscience, 3(6),

Diekman, Casey, & Fall, Christopher, & Lechleiter, James, & Terman, David (2013). Modeling the neuroprotective role of enhancing astrocyte mitochondrial metabolism during stroke. Biophysical Journal, 104, 1752-1763.

Diekman, Casey, & Golubitsky, Martin, & McMillen, Tyler, & Wang, Yunjiao (2012). Reduction and dynamics of a generalized rivalry network with two learned patterns. SIAM Journal of Applied Dynamical Systems, 11, 1270-1309.

Belle, Mino, & Diekman, Casey, & Forger, Daniel, & Piggins, Hugh (2009). Daily electrical silencing in the mammalian circadian clock. Science, 326, 281-284.

Diekman, Casey, & Sastry, P.S., & Unnikrishnan, K.P. (2009). Statistical significance of sequential firing patterns in multi-neuronal spike trains. Journal of Neuroscience Methods, 182, 279-284.

Diekman, Casey, & Forger, Daniel (2009). Clustering predicted by an electrophysiological model of the suprachiasmatic nucleus. Journal of Biological Rhythms, 24, 322-333.

Diekman, Casey, & He, W, & Prabhu, N, & Cramer, H (2003). Hybrid methods for automated diagnosis of breast tumors. Analytical and Quantitative Cytology and Histology , 25, 183-190.