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Neural Circuits and Algorithms

133 Publications

Responses of neurons in macaque V4 to object and texture images

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

bioRxiv:2024.02.20.581273

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March 4, 2025

Responses of neurons in macaque V4 to object and texture images

@TECHREPORT{Lieber24b,
TITLE= "Responses of neurons in macaque {V4} to object and texture images",
  AUTHOR=       "J D Lieber and T D Oleskiw and E P Simoncelli and J A Movshon",
  JOURNAL=      "bioRxiv",
  INSTITUTION=  "bioRxiv",
  NUMBER=       "2024.02.20.581273",
  MONTH=        "Feb",
  YEAR=         2024,
  PUBLISHER=    {Cold Spring Harbor Laboratory},
  URL=          "https://www.biorxiv.org/content/10.1101/2024.02.20.581273",
  PDF-URL=      "https://www.biorxiv.org/content/10.1101/2024.02.20.581273v1.full.pdf",
  DOI=          "10.1101/2024.02.20.581273",
}

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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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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

@TECHREPORT{Oleskiw24b,
  AUTHOR=       "T D Oleskiw and J D Lieber and E P Simoncelli and J A  Movshon",
  TITLE=        "Foundations of visual form selectivity in macaque areas V1 and V2",
  JOURNAL=      "bioRxiv",
  INSTITUTION=  "bioRxiv",
  NUMBER=       "2024.03.04.583307",
  MONTH=        "Mar",
  YEAR=         2024,
  PUBLISHER=    {Cold Spring Harbor Laboratory},
  DOI=          "10.1101/2024.03.04.583307",
  PMID=         "38496618",
  URL=          "https://www.biorxiv.org/content/10.1101/2024.03.04.583307",
  PDF-URL=      "https://www.biorxiv.org/content/10.1101/2024.03.04.583307v2.full.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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Comparing noisy neural population dynamics using optimal transport distances

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

arXiv:2412.14421

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December 19, 2024

Comparing noisy neural population dynamics using optimal transport distances

@article{Nemajatbakhsh_Lipshutz_arxiv:2412.14421,
title = {{Comparing noisy neural population dynamics using optimal transport distances}},
author = {Amin Nejatbakhsh and Victor Geadah and Alex H. Williams and David Lipshutz},
year_posted = {2024},
Journal = {arXiv e-prints},
center = {CCN},
acrosscenters = {no},
external = {no},
highlight = {no},
year = {2024},
abstract = {Biological and artificial neural systems form high-dimensional neural representations that underpin their computational capabilities. Methods for quantifying geometric similarity in neural representations have become a popular tool for identifying computational principles that are potentially shared across neural systems. These methods generally assume that neural responses are deterministic and static. However, responses of biological systems, and some artificial systems, are noisy and dynamically unfold over time. Furthermore, these characteristics can have substantial influence on a system's computational capabilities. Here, we demonstrate that existing metrics can fail to capture key differences between neural systems with noisy dynamic responses. We then propose a metric for comparing the geometry of noisy neural trajectories, which can be derived as an optimal transport distance between Gaussian processes. We use the metric to compare models of neural responses in different regions of the motor system and to compare the dynamics of latent diffusion models for text-to-image synthesis.},
archivePrefix = {arXiv},
eprint = {2412.14421},
OpenAccessURL = {https://arxiv.org/abs/2412.14421}
}

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Corrections to: Mapping Spatial Frequency Preferences Across Human Primary Visual Cortex

B. Broderick, E. P. Simoncelli, J. Winawer

Journal of Vision, 24: 8

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December 1, 2024

Corrections to: Mapping Spatial Frequency Preferences Across Human Primary Visual Cortex

@article{Broderick_Winawer_JOV_2024,
year = {2024},
author = {Broderick, William F. and Simoncelli, Eero P. and Winawer, Jonathan},
title = {{Corrections to: Mapping Spatial Frequency Preferences Across Human Primary Visual Cortex}},
center = {CCN},
acrosscenters = {no},
external = {yes},
highlight = {no},
journal = {Journal of Vision},
volume = {24},
number = {13},
pages = {8-8},
issn = {1534-7362},
url = {https://doi.org/10.1167/jov.24.13.8},
eprint = {https://arvojournals.org/arvo/content\_public/journal/jov/938697/i1534-7362-24-13-8\_1733735869.09117.pdf},
abstract = {Neurons in primate visual cortex (area V1) are tuned for spatial frequency, in a manner that depends on their position in the visual field. Several studies have examined this dependency using fMRI, reporting preferred spatial frequencies (tuning curve peaks) of V1 voxels as a function of eccentricity, but their results differ by as much as two octaves, presumably due to differences in stimuli, measurements, and analysis methodology. Here, we characterize spatial frequency tuning at a millimeter resolution within human primary visual cortex, across stimulus orientation and visual field locations. We measured fMRI responses to a novel set of stimuli, constructed as sinusoidal gratings in log-polar coordinates, which include circular, radial, and spiral geometries. For each individual stimulus, the local spatial frequency varies inversely with eccentricity, and for any given location in the visual field, the full set of stimuli span a broad range of spatial frequencies and orientations. Over the measured range of eccentricities, the preferred spatial frequency is well-fit by a function that varies as the inverse of the eccentricity plus a small constant. We also find small but systematic effects of local stimulus orientation, defined in both absolute coordinates and relative to visual field location. Specifically, peak spatial frequency is higher for tangential than radial orientations and for horizontal than vertical orientations.}
}

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