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220 Publications

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

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

D. Lipshutz, E. P. Simoncelli

arXiv:2405.17745

Advances in Neural Information Processing Systems (NeurIPS), 38

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January 17, 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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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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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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Hippocampal neuronal activity is aligned with action plans

Ipshita Zutshi, Athina Apostolelli, Wannan Yang, S. Zheng, Tora Dohi, E. Balzani, A. Williams, C. Savin, György Buzsáki

Nature, 639:153–161

January 8, 2025

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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Soft Matching Distance: A metric on neural representations that captures single-neuron tuning

A. Williams, Meenakshi Khosla

arXiv:2311.09466

Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models, 243:326-341

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2024

Soft Matching Distance: A metric on neural representations that captures single-neuron tuning

@article{Williams_UR1_arxiv:2311.09466_UR1_2024,
title = {{Soft Matching Distance: A metric on neural representations that captures single-neuron tuning}},
author = {Khosla, Meenakshi and Williams, Alex H},
abstract = {Common measures of neural representational (dis)similarity are designed to be insensitive to rotations and reflections of the neural activation space. Motivated by the premise that the tuning of individual units may be important, there has been recent interest in developing stricter notions of representational (dis)similarity that require neurons to be individually matched across networks. When two networks have the same size (i.e. same number of neurons), a distance metric can be formulated by optimizing over neuron index permutations to maximize tuning curve alignment. However, it is not clear how to generalize this metric to measure distances between networks with different sizes. Here, we leverage a connection to optimal transport theory to derive a natural generalization based on “soft” permutations. The resulting metric is symmetric, satisfies the triangle inequality, and can be interpreted as a Wasserstein distance between two empirical distributions. Further, our proposed metric avoids counter-intuitive outcomes suffered by alternative approaches, and captures complementary geometric insights into neural representations that are entirely missed by rotation-invariant metrics.},
acrosscenters = {no},
external = {yes},
highlight = {no},
eprint = {2311.09466},
archivePrefix = {arXiv},
year_posted = {2023},
Journal = {Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models},
center = {CCN},
OpenAccessURL = {https://proceedings.mlr.press/v243/khosla24a.html},
year = {2024},
pages = {326--341},
editor = {Fumero, Marco and Rodolá, Emanuele and Domine, Clementine and Locatello, Francesco and Dziugaite, Karolina and Mathilde, Caron},
volume = {243},
series = {Proceedings of Machine Learning Research},
month = {15 Dec},
publisher = {Proceedings of Machine Learning Research}
}

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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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