Carina Curto

Professor of Mathematics

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

Publication Tags

Brain Neurons Topology Neuron Neuroscience Synapses Ring Data Storage Equipment Neurosciences Fixed Point Nonlinear Dynamics Calcium Information Theory Retinal Cone Photoreceptor Cells Memory Signature Encoding Calculi Neural Networks (Computer) Code Imaging Techniques Computer Neural Networks Completion Threefolds Matrix

Most Recent Papers

Betti Curves of Rank One Symmetric Matrices

Carina Curto, Joshua Paik, Igor Rivin, 2021, on p. 645-655

Neural Ring Homomorphisms and Maps Between Neural Codes

Carina Pamela Curto, Nora Youngs, 2020, on p. 163-180

Relating network connectivity to dynamics

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

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

Predicting Neural Network Dynamics via Graphical Analysis

K Morrison, Carina Curto, 2019, ALGEBRAIC AND COMBINATORIAL COMPUTATIONAL BIOLOGY on p. 241-277

Analysis of Combinatorial Neural Codes: An Algebraic Approach

Carina Curto, Alan Veliz-Cuba, Nora Youngs, 2019, ALGEBRAIC AND COMBINATORIAL COMPUTATIONAL BIOLOGY on p. 213-240

Fixed points of competitive threshold-linear networks

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

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

What can topology tell us about the neural code?

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

Pattern completion in symmetric threshold-linear networks

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

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

The neural ring: An algebraic tool for analyzing the intrinsic structure of neural codes

Carina Curto, Vladimir Itskov, Alan Veliz-Cuba, Nora Youngs, 2013, The Bulletin of Mathematical Biophysics on p. 1571-1611

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

Calmodulin enhances ribbon replenishment and shapes filtering of synaptic transmission by cone photoreceptors

Matthew J. van Hook, Caitlyn M. Parmelee, Minghui Chen, Karlene M. Cork, Carina Curto, Wallace B. Thoreson, 2014, Journal of General Physiology on p. 357-378

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

Modeling and measurement of vesicle pools at the cone ribbon synapse

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

Threefold flops via matrix factorization

Carina Curto, David R. Morrison, 2013, Journal of Algebraic Geometry on p. 599-627

Encoding binary neural codes in networks of threshold-linear neurons

Carina Curto, Anda Degeratu, Vladimir Itskov, 2013, Neural Computation on p. 2858-2903

Flexible memory networks

Carina Curto, Anda Degeratu, Vladimir Itskov, 2012, The Bulletin of Mathematical Biophysics on p. 590-614