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Neural Computation
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August 24, 2014
A no-go theorem for one-layer feedforward networks
Chad Giusti, Vladimir Itskov
Journal of Computational Neuroscience
|
June 12, 2016
Two's company, three (or more) is a simplex : Algebraic-topological tools for understanding higher-order structure in neural data
Chad Giusti, Robert Ghrist, Danielle S Bassett
Discrete & Computational Geometry
|
October 2, 2019
On open and closed convex codes
Joshua Cruz, Chad Giusti, Vladimir Itskov, et al.
Proceedings of the National Academy of Sciences of the United States of America
|
October 22, 2015
Clique topology reveals intrinsic geometric structure in neural correlations
Chad Giusti, Eva Pastalkova, Carina Curto, et al.
Frontiers in Artificial Intelligence
|
October 28, 2021
Minimal Cycle Representatives in Persistent Homology Using Linear Programming: An Empirical Study With User's Guide
Lu Li, Connor Thompson, Gregory Henselman-Petrusek, et al.
Nature Human Behaviour
|
October 19, 2018
Knowledge gaps in the early growth of semantic feature networks
Ann E Sizemore, Elisabeth A Karuza, Chad Giusti, et al.
Plos One
|
June 30, 2016
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction
Zitong Zhang, Qawi K Telesford, Chad Giusti, et al.
Physical Review. E
|
October 15, 2016
Topological and geometric measurements of force-chain structure
Chad Giusti, Lia Papadopoulos, Eli T Owens, et al.
Arxiv
|
April 8, 2025
TRACKING THE TOPOLOGY OF NEURAL MANIFOLDS ACROSS POPULATIONS
Iris H R Yoon, Gregory Henselman-Petrusek, Yiyi Yu, et al.
Journal of Computational Neuroscience
|
November 17, 2017
Cliques and cavities in the human connectome
Ann E Sizemore, Chad Giusti, Ari Kahn, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 13) with videos related to
Sort By:
Page
of 2
Neural Computation
|
August 24, 2014
A no-go theorem for one-layer feedforward networks
Chad Giusti, Vladimir Itskov
Journal of Computational Neuroscience
|
June 12, 2016
Two's company, three (or more) is a simplex : Algebraic-topological tools for understanding higher-order structure in neural data
Chad Giusti, Robert Ghrist, Danielle S Bassett
Discrete & Computational Geometry
|
October 2, 2019
On open and closed convex codes
Joshua Cruz, Chad Giusti, Vladimir Itskov, et al.
Proceedings of the National Academy of Sciences of the United States of America
|
October 22, 2015
Clique topology reveals intrinsic geometric structure in neural correlations
Chad Giusti, Eva Pastalkova, Carina Curto, et al.
Frontiers in Artificial Intelligence
|
October 28, 2021
Minimal Cycle Representatives in Persistent Homology Using Linear Programming: An Empirical Study With User's Guide
Lu Li, Connor Thompson, Gregory Henselman-Petrusek, et al.
Nature Human Behaviour
|
October 19, 2018
Knowledge gaps in the early growth of semantic feature networks
Ann E Sizemore, Elisabeth A Karuza, Chad Giusti, et al.
Plos One
|
June 30, 2016
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction
Zitong Zhang, Qawi K Telesford, Chad Giusti, et al.
Physical Review. E
|
October 15, 2016
Topological and geometric measurements of force-chain structure
Chad Giusti, Lia Papadopoulos, Eli T Owens, et al.
Arxiv
|
April 8, 2025
TRACKING THE TOPOLOGY OF NEURAL MANIFOLDS ACROSS POPULATIONS
Iris H R Yoon, Gregory Henselman-Petrusek, Yiyi Yu, et al.
Journal of Computational Neuroscience
|
November 17, 2017
Cliques and cavities in the human connectome
Ann E Sizemore, Chad Giusti, Ari Kahn, et al.
Page
of 2