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189 Publications

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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Quantifying Differences in Neural Population Activity With Shape Metrics

Joao Barbosa, A. Nejatbakhsh, L. Duong, S. Harvey, Scott L. Brincat, Markus Siegel, Earl K. Miller, A. Williams

bioRxiv:2025.01.10.632411v1

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January 11, 2025

Quantifying Differences in Neural Population Activity With Shape Metrics

@article{Barbosa_Nemajatbakhsh_Williams_bioRxiv:2025.01.10.632411_UR2_2024,
title = {{Quantifying Differences in Neural Population Activity With Shape Metrics}},
author = {Joao Barbosa and Amin Nejatbakhsh and Lyndon Duong and Sarah E. Harvey and Scott L. Brincat and Markus Siegel and Earl K. Miller and Alex H. Williams},
year_posted = {2025},
Journal = {bioRxiv e-prints},
center = {CCN},
acrosscenters = {no},
external = {yes},
highlight = {no},
year = {2024},
abstract = {Quantifying differences across species and individuals is fundamental to many fields of biology. However, it remains challenging to draw detailed functional comparisons between large populations of interacting neurons. Here, we introduce a general framework for comparing neural population activity in terms of shape distances. This approach defines similarity in terms of explicit geometric transformations, which can be flexibly specified to obtain different measures of population-level neural similarity. Moreover, differences between systems are defined by a distance that is symmetric and satisfies the triangle inequality, enabling downstream analyses such as clustering and nearest-neighbor regression. We demonstrate this approach on datasets spanning multiple behavioral tasks (navigation, passive viewing of images, and decision making) and species (mice and non-human primates), highlighting its potential to measure functional variability across subjects and brain regions, as well as its ability to relate neural geometry to animal behavior.},
archivePrefix = {bioRxiv},
eprint = {2025.01.10.632411},
OpenAccessURL = {https://www.biorxiv.org/content/10.1101/2025.01.10.632411v1}
}

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How to optimize neuroscience data utilization and experiment design for advancing brain models of visual and linguistic cognition?

Greta Tuckute, Dawn Finzi, Eshed Margalit, Joel Zylberberg, S. Chung , Alona Fyshe, Evelina Fedorenko, Nikolaus Kriegeskorte, Jacob Yates, Kalanit Grill-Spector, Kohitij Kar

arXiv:2401.03376v2

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2024

How to optimize neuroscience data utilization and experiment design for advancing brain models of visual and linguistic cognition?

@article{Tuckute_Chung_arxiv:2401.03376_2024,
title = {{How to optimize neuroscience data utilization and experiment design for advancing brain models of visual and linguistic cognition?}},
author = {Greta Tuckute and Dawn Finzi and Eshed Margalit and Joel Zylberberg and SueYeon Chung and Alona Fyshe and Evelina Fedorenko and Nikolaus Kriegeskorte and Jacob Yates and Kalanit Grill-Spector and Kohitij Kar},
abstract = {In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior. However, there is no consensus on the most efficient ways to collect data and design experiments to develop the next generation of models. This article explores the controversial opinions that have emerged on this topic in the domain of vision and language. Specifically, we address two critical points. First, we weigh the pros and cons of using qualitative insights from empirical results versus raw experimental data to train models. Second, we consider model-free (intuition-based) versus model-based approaches for data collection, specifically experimental design and stimulus selection, for optimal model development. Finally, we consider the challenges of developing a synergistic approach to experimental design and model building, including encouraging data and model sharing and the implications of iterative additions to existing models. The goal of the paper is to discuss decision points and propose directions for both experimenters and model developers in the quest to understand the brain.},
acrosscenters = {no},
external = {yes},
highlight = {no},
eprint = {2401.03376},
archivePrefix = {arXiv},
year_posted = {2024},
center = {CCN},
OpenAccessURL = {https://arxiv.org/abs/2401.03376},
Journal = {arXiv e-prints},
year = {2024}
}

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