Vladimir Itskov
Associate Professor of Mathematics
-
109B McAllister
University Park, PA - vui1@psu.edu
- 814-865-1613
Huck Affiliations
Publication Tags
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Feedforward Network Response Pattern Brain Rapid Eye Movement Sleep Meaningful Structure Noise Sensing Connectivity Data Nerve Potential Geometric Structure Eigenvalue Layer Hyperplane Behavior (Neuroscience) No Go Theorem Data Invariant Neural Correlations Closed Neurons Matrix Analysis Recurrent Connections Feedforward Architecture Combinatorial Topology Quasiconvex Functions NeuronMost Recent Publications
A Topological Approach to Inferring the Intrinsic Dimension of Convex Sensing Data.
Min-Chun Wu, Vladimir Itskov, Journal of Applied and Computational Topology on p. 49
Diversity of Emergent Dynamics in Competitive Threshold-Linear Networks
Katherine Morrison, Anda Degeratu, Vladimir Itskov, Carina Curto, 2024, SIAM Journal on Applied Dynamical Systems on p. 855-884
A topological approach to inferring the intrinsic dimension of convex sensing data
Min Chun Wu, Vladimir Itskov, 2022, Journal of Applied and Computational Topology on p. 127-176
Hyperplane neural codes and the polar complex.
Vladimir Itskov, Alexander Kunin, Zvi Rosen, 2020, on p. 343-370
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
Discrete biomathematics
Stephen F. Altschul, Mihai Pop, Joseph Rusinko, Elena Dimitrova, Andy Jenkins, Matthew Macauley, Qijun He, Svetlana Poznanovic, Carina Curto, Vladimir Itskov, Margaret Cozzens, 2017, on p. 1425-1514
Clique topology reveals intrinsic geometric structure in neural correlations
Chad Giusti, Eva Pastalkova, Carina 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 Curto, Anda Degeratu, Vladimir Itskov, 2013, Neural Computation on p. 2858-2903
Most-Cited Papers
Clique topology reveals intrinsic geometric structure in neural correlations
Chad Giusti, Eva Pastalkova, Carina 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
On Open and Closed Convex Codes
Joshua Cruz, Chad Giusti, Vladimir Itskov, Bill Kronholm, 2019, Discrete and Computational Geometry on p. 247-270
Hyperplane Neural Codes and the Polar Complex
Vladimir Itskov, Alexander Kunin, Zvi Rosen, 2020, on p. 343-369
Diversity of Emergent Dynamics in Competitive Threshold-Linear Networks
Katherine Morrison, Anda Degeratu, Vladimir Itskov, Carina Curto, 2024, SIAM Journal on Applied Dynamical Systems on p. 855-884
A topological approach to inferring the intrinsic dimension of convex sensing data
Min Chun Wu, Vladimir Itskov, 2022, Journal of Applied and Computational Topology on p. 127-176
Discrete biomathematics
Stephen F. Altschul, Mihai Pop, Joseph Rusinko, Elena Dimitrova, Andy Jenkins, Matthew Macauley, Qijun He, Svetlana Poznanovic, Carina Curto, Vladimir Itskov, Margaret Cozzens, 2017, on p. 1425-1514
Hyperplane neural codes and the polar complex.
Vladimir Itskov, Alexander Kunin, Zvi Rosen, 2020, on p. 343-370