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214 Publications

Discriminating image representations with principal distortions

J. Feather, D. Lipshutz, S. Harvey, A. Williams, E. P. Simoncelli

https://arxiv.org/abs/2410.15433

International Conference on Learning Representations (ICLR), 13

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May 16, 2025

Discriminating image representations with principal distortions

@INPROCEEDINGS{Feather25a,
  AUTHOR=       "J Feather* and D Lipshutz* and S E Harvey and A H Williams and  E P Simoncelli",
  TITLE=        "Discriminating image representations with principal
                 distortions",
  BOOKTITLE=    "Int'l Conf on Learning Representations (ICLR)",
  VOLUME=       13,
  MONTH=        "May",
  ADDRESS=      "Singapore",
  YEAR=         2025,
  URL=          "https://arxiv.org/abs/2410.15433",
  PDF-URL=      "https://www.cns.nyu.edu/pub/lcv/feather25a.pdf",
}

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Detection of Moving Objects Using Self-motion Constraints on Optic Flow

H. Lutwak, Bas Rokers, E. P. Simoncelli

arXiv:2505.06686

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May 13, 2025

Detection of Moving Objects Using Self-motion Constraints on Optic Flow

@TECHREPORT{Lutwak25a,
     % TITLE= "3D Constraints on Local Motion to Detect Moving Objects",
     TITLE= "Detection of moving objects using self-motion constraints on optic flow",
     AUTHOR= "Hope Lutwak and Bas Rokers and Eero P. Simoncelli",
     MONTH= "May",
     YEAR= 2025,
     INSTITUTION= "arXiv.org e-prints",
     NUMBER= "2505.06686",
     primaryClass= {q-bio.NC},
     DOI= "10.48550/arXiv.2505.06686",
     URL= "https://arxiv.org/abs/2505.06686",
     PDF-URL= "https://arxiv.org/pdf/2505.06686",
}

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Detecting Moving Objects During Self-motion

H. Lutwak

arXiv:2505.06686v2

New York University ProQuest Dissertations & Theses, 31640598

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May 10, 2025

Detecting Moving Objects During Self-motion

@PHDTHESIS{Lutwak-phd,
     TITLE= "Detecting Moving Objects During Self-motion",
     AUTHOR= "Hope Lutwak",
     ADVISOR= "Eero P. Simoncelli",
     SCHOOL= "Center for Neural Science, New York University",
     ADDRESS= "New York, NY",
     YEAR= 2025,
     MONTH= "Jan",
     PDF-URL= "https://www.cns.nyu.edu/pub/lcv/lutwak-phd.pdf",
}

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The first complete 3D reconstruction and morphofunctional mapping of an insect eye

Anastasia A Makarova, N. Chua, Anna V Diakova, Inna A Desyatirkina, P. Gunn, Song Pang, C Shan Xu, Herald F Hess, D. Chklovskii, Alexey A Polilov

bioRxiv:2024.09.30.615804v1

eLife, 14:RP103247

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May 1, 2025

The first complete 3D reconstruction and morphofunctional mapping of an insect eye

@article{Makarova_Chua_Chklovskii_bioRxiv:2024.09.30.615804_2024,
title = {{The first complete 3D reconstruction and morphofunctional mapping of an insect eye}},
author = {Makarova, Anastasia A. and Chua, Nicholas J. and Diakova, Anna V. and Desyatirkina, Inna A. and Gunn, Pat and Pang, Song and Xu, C. Shan and Hess, Harald and Chklovskii, Dmitri B. and Polilov, Alexey A.},
abstract = {The structure of compound eyes in arthropods has been the subject of many studies, revealing important biological principles. Until recently, these studies were constrained by the two-dimensional nature of available ultrastructural data. By taking advantage of the novel three-dimensional ultrastructural dataset obtained using volume electron microscopy (vEM), we present the first cellular-level reconstruction of the whole compound eye of an insect, the miniaturized parasitoid wasp Megaphragma viggianii. The compound eye of the female M. viggianii consists of 29 ommatidia and contains 478 cells. Despite the almost anucleate brain, all cells of the compound eye contain nuclei. As in larger insects, the dorsal rim area (DRA) of the eye in M. viggianii contains ommatidia that are believed to be specialized in polarized light detection as reflected in their corneal and retinal morphology. We report the presence of three {\textquoteleft}ectopic{\textquoteright} photoreceptors. Our results offer new insights into the miniaturization of compound eyes and scaling of sensory organs in general.},
acrosscenters = {no},
external = {yes},
highlight = {no},
eprint = {bioRxiv:2024.09.30.615804},
archivePrefix = {bioRxiv},
year_posted = {2024},
center = {CCN},
OpenAccessURL = {https://www.biorxiv.org/content/10.1101/2024.09.30.615804v2},
year = {2025}
}

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Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models

Ling-Qi Zhang, Z. Kadkhodaie, E. P. Simoncelli, D. H. Brainard

arXiv:2405.17456v4

Transactions on Machine Learning Research

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2025

Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models

@ARTICLE{Zhang25,
     TITLE= "Generalized compressed sensing for image reconstruction with
     diffusion probabilistic models",
     AUTHOR= "L-Q Zhang and Z Kadkhodaie and EP Simoncelli and DH Brainard",
     JOURNAL= "Trans. Machine Learning Research",
     MONTH= "May",
     YEAR= 2025,
     ISSN= {2835-8856},
     URL= "https://openreview.net/forum?id=lmHh4FmPWZ",
     PDF-URL= "https://openreview.net/pdf?id=lmHh4FmPWZ",
}

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The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models

A. Kirsanov, C. Chou , Kyunghyun Cho, S. Chung

arXiv:2502.08009v1

Findings of the Association for Computational Linguistics NAACL 2025, 2025: 1855–1888

April 29, 2025

Perceptual learning improves discrimination but does not reduce distortions in appearance

Sarit F.A. Szpiro, Charlie S. Burlingham, E. P. Simoncelli, Marisa Carrasco

PLOS Computational Biology, 21(4): e1012980

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April 15, 2025

Perceptual learning improves discrimination but does not reduce distortions in appearance

@ARTICLE{Szpiro25,
     TITLE= "Perceptual learning improves discrimination but does not
     reduce distortions in appearance",
     AUTHOR= "SFA Szpiro and CS Burlingham and EP Simoncelli and M Carrasco",
     MONTH= "Apr",
     YEAR= 2025,
     JOURNAL= "PLOS Computational Biology",
     DOI= "10.1371/journal.pcbi.1012980",
     PDF-URL= "https://www.cns.nyu.edu/pub/lcv/szpiro25.pdf",
}

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Geometry Linked to Untangling Efficiency Reveals Structure and Computation in Neural Populations

C. Chou , Royoung Kim, Luke A. Arend, Yao-Yuan Yang, Brett D. Mensh, Won Mok Shim, Matthew G. Perich, S. Chung

bioRxiv:2024.02.26.582157

March 31, 2025

Disentangling 3D Animal Pose Dynamics With Scrubbed Conditional Latent Variables

Joshua H Wu, Hari Koneru, J Russell Ravenel, Anshuman Sabath, James M Roach, Shaun SX Lim, Michael R Tadross, A. Williams, Timothy W Dunn

The Thirteenth International Conference on Learning Representations, 2025

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March 14, 2025

Disentangling 3D Animal Pose Dynamics With Scrubbed Conditional Latent Variables

@inproceedings{
wu2025disentangling,
title={Disentangling 3D Animal Pose Dynamics with Scrubbed Conditional Latent Variables},
author={Joshua Huang Wu and Hari Koneru and James Russell Ravenel and Anshuman Sabath and James Michael Roach and Shaun Sze-Xian Lim and MICHAEL R TADROSS and Alex H Williams and Timothy W DUNN},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=i7drKWhFCo}
}

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Nonlinear classification of neural manifolds with contextual information

F. Mignacco, C. Chou , S. Chung

arXiv:2405.06851

Phys. Rev. E , 111: 035302

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March 6, 2025

Nonlinear classification of neural manifolds with contextual information

@article{Mignacco_Chung_arXiv:2405.06851,
archivePrefix={arXiv},
author={Francesca Mignacco and Chi-Ning Chou and SueYeon Chung},
eprint={2405.06851},
journal = {arXiv e-prints},
title={{Nonlinear classification of neural manifolds with contextual information}},
OpenAccessUrl={https://arxiv.org/abs/2405.06851},
year_posted={2024},
Center = {CCN},
accrosscenters = {no},
external = {yes},
highlight = {no},
year = {2024},
abstract = {Understanding how neural systems efficiently process information through distributed representations is a fundamental challenge at the interface of neuroscience and machine learning. Recent approaches analyze the statistical and geometrical attributes of neural representations as population-level mechanistic descriptors of task implementation. In particular, manifold capacity has emerged as a promising framework linking population geometry to the separability of neural manifolds. However, this metric has been limited to linear readouts. Here, we propose a theoretical framework that overcomes this limitation by leveraging contextual input information. We derive an exact formula for the context-dependent capacity that depends on manifold geometry and context correlations, and validate it on synthetic and real data. Our framework's increased expressivity captures representation untanglement in deep networks at early stages of the layer hierarchy, previously inaccessible to analysis. As context-dependent nonlinearity is ubiquitous in neural systems, our data-driven and theoretically grounded approach promises to elucidate context-dependent computation across scales, datasets, and models.}
}

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