Carina Curto

Professor of Mathematics

Carina Curto

Research Summary

Mathematics applied to and arising from theoretical and computational neuroscience. Neural network theory and neural coding. Applied algebra, topology, and geometry

Huck Affiliations

Links

Most Recent Publications

Stable fixed points of combinatorial threshold-linear networks

Carina Curto, Jesse Geneson, Katherine Morrison, 2024, Advances in Applied Mathematics

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

Graph Rules for Recurrent Neural Network Dynamics

Carina Curto, Katherine Morrison, 2023, Notices of the American Mathematical Society on p. 536-551

Graph rules for recurrent neural network dynamics: extended version

Carina Curto, K Morrison, 2023, arXiv preprint arXiv:2301.12638

Periodic neural codes and sound localization in barn owls

Lindsey S. Brown, Carina Curto, 2022, Involve on p. 1-37

Core motifs predict dynamic attractors in combinatorial threshold-linear networks

Caitlyn Parmelee, Samantha Moore, K Morrison, Carina Curto, 2022, PLoS One on p. e0264456

Sequential Attractors in Combinatorial Threshold-Linear Networks

Caitlyn Parmelee, Juliana Alvarez, Carina Curto, K Morrison, 2022, SIAM Journal on Applied Dynamical Systems on p. 1597--1630

Nerve Theorems for Fixed Points of Neural Networks

Daniela Egas Santander, Stefania Ebli, Alice Patania, Nicole Sanderson, Felicia Burtscher, Katherine Morrison, Carina Curto, 2022, on p. 129-165

Nerve theorems for fixed points of neural networks

Daniela Santander, Stefania Ebli, Alice Patania, Nicole Sanderson, Felicia Burtscher, K Morrison, Carina Curto, 2022, on p. 129--165

Sequence generation in inhibition-dominated neural networks

Caitlyn Parmelee, Juliana Alvarez, Carina Curto, K Morrison, 2022, arXiv preprint arXiv:2212.08211

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

What can topology tell us about the neural code?

Carina Curto, 2017, Bulletin of the American Mathematical Society on p. 63-78

What makes a neural code convex?

Carina Curto, Elizabeth Gross, Jack Jeffries, Katherine Morrison, Mohamed Omar, Zvi Rosen, Anne Shiu, Nora Youngs, 2017, SIAM Journal on Applied Algebra and Geometry on p. 222-238

Fixed points of competitive threshold-linear networks

Carina Curto, Jesse Geneson, Katherine Morrison, 2019, Neural Computation on p. 94-155

Pattern completion in symmetric threshold-linear networks

Carina Curto, Katherine Morrison, 2016, Neural Computation on p. 2825-2852

Predicting Neural Network Dynamics via Graphical Analysis

Katherine Morrison, Carina Curto, 2018, on p. 241-277

Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience

Carina Curto, Katherine Morrison, 2019, Current Opinion in Neurobiology on p. 11-20

Modeling and measurement of vesicle pools at the cone ribbon synapse: Changes in release probability are solely responsible for voltage-dependent changes in release

Wallace B. Thoreson, Matthew J. Van hook, Caitlyn Parmelee, Carina Curto, 2016, Synapse on p. 1-14

Algebraic signatures of convex and non-convex codes

Carina Curto, Elizabeth Gross, Jack Jeffries, Katherine Morrison, Zvi Rosen, Anne Shiu, Nora Youngs, 2019, Journal of Pure and Applied Algebra on p. 3919-3940

Core motifs predict dynamic attractors in combinatorial threshold-linear networks

Caitlyn Parmelee, Samantha Moore, K Morrison, Carina Curto, 2022, PLoS One on p. e0264456