Vladimir Itskov
Associate Professor of Mathematics
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109B McAllister
University Park, PA - vui1@psu.edu
- 814-865-1613
Huck Affiliations
Publication Tags
Brain Neurons Neuron Information Theory Hippocampus Pyramidal Cells Short Term Memory Noise Closed Encoding Data Storage Equipment Dentate Gyrus Code Layer Ring Population Memory Decomposition Neurosciences Running Animals Error Correction Hyperplane Rem Sleep Auditory CortexMost Recent Papers
Hyperplane Neural Codes and the Polar Complex
Vladimir Itskov, Alexander Kunin, Zvi Rosen, 2020, on p. 343-369
On open and closed convex codes
Joshua Cruz, Chad Giusti, Vladimir Itskov, Bill Kronholm, 2019, Discrete and Computational Geometry on p. 247-270
Clique topology reveals intrinsic geometric structure in neural correlations
Chad Giusti, Eva Pastalkova, Carina Pamela Curto, Vladimir Itskov, 2015, Proceedings of the National Academy of Sciences of the United States of America on p. 13455-13460
A no-go theorem for one-layer feedforward networks
Chad Giusti, Vladimir Itskov, 2014, Neural Computation on p. 2527-2540
Encoding binary neural codes in networks of threshold-linear neurons
Carina Pamela Curto, Anda Degeratu, Vladimir Itskov, 2013, Neural Computation on p. 2858-2903
The neural ring: An algebraic tool for analyzing the intrinsic structure of neural codes
Carina Pamela Curto, Vladimir Itskov, Alan Veliz-Cuba, Nora Youngs, 2013, The Bulletin of Mathematical Biophysics on p. 1571-1611
Combinatorial neural codes from a mathematical coding theory perspective
Carina Curto, Vladimir Itskov, Katherine Morrison, Zachary Roth, Judy L. Walker, 2013, Neural Computation on p. 1891-1925
Flexible memory networks
Carina Pamela Curto, Anda Degeratu, Vladimir Itskov, 2012, The Bulletin of Mathematical Biophysics on p. 590-614
Effects of adult-generated granule cells on coordinated network activity in the dentate gyrus
Clay O. Lacefield, Vladimir Itskov, Thomas Reardon, René Hen, Joshua A. Gordon, 2012, Hippocampus on p. 106-116
Short-term facilitation may stabilize parametric working memory trace
Vladimir Itskov, David Hansel, Misha Tsodyks, 2011, Frontiers in Computational Neuroscience
Most-Cited Papers
Effects of adult-generated granule cells on coordinated network activity in the dentate gyrus
Clay O. Lacefield, Vladimir Itskov, Thomas Reardon, René Hen, Joshua A. Gordon, 2012, Hippocampus on p. 106-116
Clique topology reveals intrinsic geometric structure in neural correlations
Chad Giusti, Eva Pastalkova, Carina Pamela Curto, Vladimir Itskov, 2015, Proceedings of the National Academy of Sciences of the United States of America on p. 13455-13460
Cell assembly sequences arising from spike threshold adaptation keep track of time in the hippocampus
Vladimir Itskov, Carina Pamela Curto, Eva Pastalkova, György Buzsáki, 2011, Journal of Neuroscience on p. 2828-2834
Short-term facilitation may stabilize parametric working memory trace
Vladimir Itskov, David Hansel, Misha Tsodyks, 2011, Frontiers in Computational Neuroscience
The neural ring: An algebraic tool for analyzing the intrinsic structure of neural codes
Carina Pamela Curto, Vladimir Itskov, Alan Veliz-Cuba, Nora Youngs, 2013, The Bulletin of Mathematical Biophysics on p. 1571-1611
How do neurons work together? Lessons from auditory cortex
Kenneth D. Harris, Peter Bartho, Paul Chadderton, Carina Pamela Curto, Jaime de la Rocha, Liad Hollender, Vladimir Itskov, Artur Luczak, Stephan L. Marguet, Alfonso Renart, Shuzo Sakata, 2011, Hearing Research on p. 37-53
Combinatorial neural codes from a mathematical coding theory perspective
Carina Curto, Vladimir Itskov, Katherine Morrison, Zachary Roth, Judy L. Walker, 2013, Neural Computation on p. 1891-1925
A no-go theorem for one-layer feedforward networks
Chad Giusti, Vladimir Itskov, 2014, Neural Computation on p. 2527-2540
On open and closed convex codes
Joshua Cruz, Chad Giusti, Vladimir Itskov, Bill Kronholm, 2019, Discrete and Computational Geometry on p. 247-270
Encoding binary neural codes in networks of threshold-linear neurons
Carina Pamela Curto, Anda Degeratu, Vladimir Itskov, 2013, Neural Computation on p. 2858-2903