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181 Publications

Modeling Neural Activity with Conditionally Linear Dynamical Systems

Victor Geadah, A. Nejatbakhsh, D. Lipshutz, J. Pillow, A. Williams

arXiv:2502.18347

2025

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

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

arXiv:2502.08009v1

February 11, 2025

Spatial Frequency Maps in Human Visual Cortex: A Replication and Extension

Jiyeong Ha, B. Broderick, Kendrick Kay, J. Winawer

bioRxiv:2025.01.21.634150

February 5, 2025

Optimized Linear Measurements for Inverse Problems using Diffusion-Based Image Generation

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

arXiv:2405.17456

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February 1, 2025

Optimized Linear Measurements for Inverse Problems using Diffusion-Based Image Generation

@TECHREPORT{Zhang24a,
  TITLE=        "Optimized Linear Measurements for Inverse Problems using Diffusion-Based Image Generation",
  AUTHOR=       "LQ Zhang and Z Kadkhodaie and E P Simoncelli and D H Brainard",
  INSTITUTION=  "arXiv.org e-prints",
  NUMBER=       "2405.17456",
  DOI=          "10.48550/arXiv.2405.17456",
  URL=          "https://doi.org/10.48550/arXiv.2405.17456",
  MONTH=        "May",
  YEAR=         2024,
}

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Sub-cellular population imaging tools reveal stable apical dendrites in hippocampal area CA3

J. Moore, Shannon K. Rashid, Emmett Bicker, Cara D. Johnson, Naomi Codrington, D. Chklovskii, Jayeeta Basu

Nature Communications volume, 16: 1119

January 28, 2025

Discriminating image representations with principal distortions

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

arXiv:2410.15433

International Conference on Learning Representations (ICLR), 13

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January 22, 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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Foundations of visual form selectivity in macaque areas V1 and V2

T. D. Oleskiw , Justin D. Lieber, E. P. Simoncelli, J. A. Movshon

bioRxiv:2024.03.04.583307

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January 18, 2025

Foundations of visual form selectivity in macaque areas V1 and V2

@article{Oleskiw_Simoncelli_biorxiv:2024.03.04.583307_2024,
title = {{Foundations of visual form selectivity in macaque areas V1 and V2}},
author = {Oleskiw,  Timothy D. and Lieber,  Justin D. and Simoncelli,  Eero P. and Movshon,  J. Anthony},
abstract = {Neurons early in the primate visual cortical pathway generate responses by combining signals from other neurons: some from downstream areas, some from within the same area, and others from areas upstream. Here we develop a model that selectively combines afferents derived from a population model of V1 cells. We use this model to account for responses we recorded of both V1 and V2 neurons in awake fixating macaque monkeys to stimuli composed of a sparse collection of locally oriented features ({\textquotedblleft}droplets{\textquotedblright}) designed to drive subsets of V1 neurons. The first stage computes the rectified responses of a fixed population of oriented filters at different scales that cover the visual field. The second stage computes a weighted combination of these first-stage responses, followed by a final nonlinearity, with parameters optimized to fit data from physiological recordings and constrained to encourage sparsity and locality. The fitted model accounts for the responses of both V1 and V2 neurons, capturing an average of 43\% of the explainable variance for V1 and 38\% for V2. The models fitted to droplet recordings predict responses to classical stimuli, such as gratings of different orientations and spatial frequencies, as well as to textures of different spectral content, which are known to be especially effective in driving V2. The models are less effective, however, at capturing the selectivity of responses to textures that include naturalistic image statistics. The pattern of afferents {\textemdash} defined by their weights over the 4 dimensions of spatial position, orientation, and spatial frequency {\textemdash} provides a common and interpretable characterization of the origin of many neuronal response properties in the early visual cortex.Competing Interest StatementThe authors have declared no competing interest.},
acrosscenters = {no},
external = {yes},
highlight = {no},
eprint = {https://www.biorxiv.org/content/early/2025/01/18/2024.03.04.583307.full.pdf},
archivePrefix = {bioRxiv},
year_posted = {2024},
center = {ccn},
OpenAccessURL = {http://dx.doi.org/10.1101/2024.03.04.583307},
Journal = {bioRxiv e-prints},
year = {2024}
}

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Contrastive-equivariant self-supervised learning improves alignment with primate visual area IT

T. Yerxa, J. Feather, E. P. Simoncelli, S. Chung

Advances on Neural Information Processing (NeurIPS), 38

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January 15, 2025

Contrastive-equivariant self-supervised learning improves alignment with primate visual area IT

@INPROCEEDINGS{Yerxa24b,
  TITLE=        "Contrastive-equivariant self-supervised learning improves alignment with primate visual area {IT}",
  AUTHOR=       "T Yerxa and J Feather and E P Simoncelli and SY Chung",
  BOOKTITLE=    "Adv. Neural Information Processing (NeurIPS)",
  VOLUME=       38,
  ADDRESS=      "Vancouver",
  MONTH=        "Dec",
  YEAR=         2024,
  PDF-URL=      "https://www.cns.nyu.edu/pub/lcv/yerxa24b-final.pdf",
}

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Learning predictable and robust neural representations by straightening image sequences

X. Niu, Cristina Savin, E. P. Simoncelli

Advances in Neural Information Processing Systems (NeurIPS 2024), 38

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January 15, 2025

Learning predictable and robust neural representations by straightening image sequences

@INPROCEEDINGS{Niu24b,
  TITLE=        "Learning predictable and robust neural representations by
                 straightening image sequences",
  AUTHOR=       "X Niu and C Savin and E P Simoncelli",
  BOOKTITLE=    "Adv. Neural Information Processing (NeurIPS)",
  VOLUME=       37,
  ADDRESS=      "Vancouver",
  MONTH=        "Dec",
  YEAR=         2024,
  PDF-URL=      "https://www.cns.nyu.edu/pub/lcv/niu24b-final.pdf",
}

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Shaping the distribution of neural responses with interneurons in a recurrent circuit model

D. Lipshutz, E. P. Simoncelli

Advances in Neural Information Processing Systems (NeurIPS), 38

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January 15, 2025

Shaping the distribution of neural responses with interneurons in a recurrent circuit model

@INPROCEEDINGS{Lipshutz24c,
  TITLE=        "Shaping the distribution of neural responses with
                 interneurons in a recurrent circuit model",
  AUTHOR=       "D Lipshutz and E P Simoncelli",
  BOOKTITLE=    "Adv. Neural Information Processing (NeurIPS)",
  VOLUME=       38,
  ADDRESS=      "Vancouver",
  MONTH=        "Dec",
  YEAR= 2024,
  PDF-URL=      "https://www.cns.nyu.edu/pub/lcv/lipshutz24c-final.pdf",
}

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