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207 Publications

Geometry Linked to Untangling Efficiency Reveals Structure and Computation in Neural Populations

C. Chou , Royoung Kim, Luke A. Arend, Yao-Yuan Yang, Brett D. Mensh, Won Mok Shim, Matthew G. Perich, S. Chung

bioRxiv:2024.02.26.582157

March 31, 2025

Disentangling 3D Animal Pose Dynamics With Scrubbed Conditional Latent Variables

Joshua H Wu, Hari Koneru, J Russell Ravenel, Anshuman Sabath, James M Roach, Shaun SX Lim, Michael R Tadross, A. Williams, Timothy W Dunn

The Thirteenth International Conference on Learning Representations, 2025

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March 14, 2025

Disentangling 3D Animal Pose Dynamics With Scrubbed Conditional Latent Variables

@inproceedings{
wu2025disentangling,
title={Disentangling 3D Animal Pose Dynamics with Scrubbed Conditional Latent Variables},
author={Joshua Huang Wu and Hari Koneru and James Russell Ravenel and Anshuman Sabath and James Michael Roach and Shaun Sze-Xian Lim and MICHAEL R TADROSS and Alex H Williams and Timothy W DUNN},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=i7drKWhFCo}
}

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Nonlinear classification of neural manifolds with contextual information

F. Mignacco, C. Chou , S. Chung

arXiv:2405.06851

Phys. Rev. E , 111: 035302

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March 6, 2025

Nonlinear classification of neural manifolds with contextual information

@article{Mignacco_Chung_arXiv:2405.06851,
archivePrefix={arXiv},
author={Francesca Mignacco and Chi-Ning Chou and SueYeon Chung},
eprint={2405.06851},
journal = {arXiv e-prints},
title={{Nonlinear classification of neural manifolds with contextual information}},
OpenAccessUrl={https://arxiv.org/abs/2405.06851},
year_posted={2024},
Center = {CCN},
accrosscenters = {no},
external = {yes},
highlight = {no},
year = {2024},
abstract = {Understanding how neural systems efficiently process information through distributed representations is a fundamental challenge at the interface of neuroscience and machine learning. Recent approaches analyze the statistical and geometrical attributes of neural representations as population-level mechanistic descriptors of task implementation. In particular, manifold capacity has emerged as a promising framework linking population geometry to the separability of neural manifolds. However, this metric has been limited to linear readouts. Here, we propose a theoretical framework that overcomes this limitation by leveraging contextual input information. We derive an exact formula for the context-dependent capacity that depends on manifold geometry and context correlations, and validate it on synthetic and real data. Our framework's increased expressivity captures representation untanglement in deep networks at early stages of the layer hierarchy, previously inaccessible to analysis. As context-dependent nonlinearity is ubiquitous in neural systems, our data-driven and theoretically grounded approach promises to elucidate context-dependent computation across scales, datasets, and models.}
}

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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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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

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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}
}

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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

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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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