CCN: Publications

Clear Filters

Filter by Group

Computational Vision

Neural Circuits and Algorithms

NeuroAI and Geometric Data Analysis

Statistical Analysis of Neural Data

214 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

Export Citation

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",
}

Download Citation (BibTex)

Comparing noisy neural population dynamics using optimal transport distances

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

arXiv:2412.14421

The Thirteenth International Conference on Learning Representations, 2025

Export Citation

March 1, 2025

Comparing noisy neural population dynamics using optimal transport distances

@inproceedings{
nejatbakhsh2025comparing,
title={Comparing noisy neural population dynamics using optimal transport distances},
author={Amin Nejatbakhsh and Victor Geadah and Alex H Williams and David Lipshutz},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=cNmu0hZ4CL}
}

Download Citation (BibTex)

Estimating Neural Representation Alignment from Sparsely Sampled Inputs and Features

C. Chun, A. Canatar, S. Chung , Daniel D. Lee

arXiv:2502.15104

February 24, 2025

Brain-Model Evaluations Need the NeuroAI Turing Test

J. Feather, Meenakshi Khosla, N. Apurva Ratan Murty, Aran Nayebi

arXiv:2502.16238

February 22, 2025

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

Export Citation

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",
}

Download Citation (BibTex)

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

Export Citation

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}
}

Download Citation (BibTex)

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

Export Citation

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",
}

Download Citation (BibTex)

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

Export Citation

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",
}

Download Citation (BibTex)

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

Export Citation

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}
}

Download Citation (BibTex)

Previous Page
Viewing
Next Page