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181 Publications

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:2401.03376

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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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Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds

Michael Kuoch, C. Chou , N. Parthasarathy, Joel Dapello, J. DiCarlo, H. Sompolinsky, S. Chung

arXiv:2312.14285

Conference on Parsimony and Learning, PMLR, 234: 395-418

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January 6, 2024

Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds

@article{Kuoch_Chou_Chung_arXiv:2312.14285_CPAL24_2024,
title = {{Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds}},
author = {Michael Kuoch and Chou, Chi-Ning and Nikhil Parthasarathy and Joel Dapello and James J. DiCarlo and Haim Sompolinsky and Chung, SueYeon},
editor = {Chi, Yuejie and Dziugaite, Gintare Karolina and Qu, Qing and Wang, Atlas Wang and Zhu, Zhihui},
journal = {Conference on Parsimony and Learning, PMLR},
volume = {234},
pages =	{395-418},
Year_posted = {2023},
year = {2024},
month = Sep,
publisher = {Proceedings of Machine Learning Research},
doi = {10.48550/arXiv.2312.14285},
url = {https://proceedings.mlr.press/v234/kuoch24a.html},
Eprint = {2312.14285},
archivePrefix={arXiv},
OpenAccessURL = {https://arxiv.org/abs/2312.14285},
center = {CCN},
acrosscenters = {no},
external = {yes},
highlight = {no},
Abstract = {In recent years, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studies or global normative approaches. However, concrete methodologies for bridging the gap between these levels of abstraction remain elusive. In this work, we investigate the internal mechanisms of neural networks through the lens of neural population geometry, aiming to provide understanding at an intermediate level of abstraction, as a way to bridge that gap. Utilizing manifold capacity theory (MCT) from statistical physics and manifold alignment analysis (MAA) from high-dimensional statistics, we probe the underlying organization of task-dependent manifolds in deep neural networks and neural recordings from the macaque visual cortex. Specifically, we quantitatively characterize how different learning objectives lead to differences in the organizational strategies of these models and demonstrate how these geometric analyses are connected to the decodability of task-relevant information. Furthermore, these metrics show that macaque visual cortex data are more similar to unsupervised DNNs in terms of geometrical properties such as manifold position and manifold alignment. These analyses present a strong direction for bridging mechanistic and normative theories in neural networks through neural population geometry, potentially opening up many future research avenues in both machine learning and neuroscience.}
}

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Adaptive whitening with fast gain modulation and slow synaptic plasticity

L. Duong, E. P. Simoncelli, D. Chklovskii, D. Lipshutz

Advances in Neural Information Processing Systems, Vol. 36

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2023

Adaptive whitening with fast gain modulation and slow synaptic plasticity

@INPROCEEDINGS{Duong23f,
     TITLE= "Adaptive whitening with fast gain modulation and slow synaptic plasticity",
     AUTHOR= "L R Duong and E P Simoncelli and D B Chklovskii and D Lipshutz",
     BOOKTITLE= {Adv. Neural Information Processing Systems (NeurIPS*23)},
     VOLUME= 36,
     MONTH= "Dec",
     YEAR= 2023,
     ADDRESS= "New Orleans",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/duong23f.pdf",
     NOTE= "To appear",
}

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A polar prediction model for learning to represent visual transformations

P. Fiquet, E. P. Simoncelli

Advances in Neural Information Processing Systems, Vol. 36

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2023

A polar prediction model for learning to represent visual transformations

@INPROCEEDINGS{Fiquet23c,
     TITLE= "A polar prediction model for learning to represent visual transformations",
     AUTHOR= "P-E Fiquet and E P Simoncelli",
     BOOKTITLE= {Adv. Neural Information Processing Systems (NeurIPS*23)},
     VOLUME= 36,
     MONTH= "Dec",
     YEAR= 2023,
     ADDRESS= "New Orleans",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/fiquet23c.pdf",
     NOTE= "To appear",
}

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Efficient coding of natural images using maximum manifold capacity representations

Yilun Kuang, E. P. Simoncelli, S. Chung

Advances in Neural Information Processing Systems, Vol. 36

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2023

Efficient coding of natural images using maximum manifold capacity representations

@INPROCEEDINGS{Yerxa23c,
     TITLE= "Efficient coding of natural images using maximum manifold capacity representations",
     AUTHOR= "T E Yerxa and Y Kuang and E P Simoncelli and SY Chung",
     BOOKTITLE= {Adv. Neural Information Processing Systems (NeurIPS*23)},
     VOLUME= 36,
     MONTH= "Dec",
     YEAR= 2023,
     ADDRESS= "New Orleans",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/yerxa23c.pdf",
     NOTE= "To appear",
}

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Comparing neural models using their perceptual discriminability predictions

J. Zhou, Chanwoo Chun, Ajay Subramanian, E. P. Simoncelli

Advances in Neural Information Processing Systems, Vol. 36

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2023

Comparing neural models using their perceptual discriminability predictions

@INPROCEEDINGS{Zhou23b,
     TITLE= "Comparing neural models using their perceptual discriminability predictions",
     AUTHOR= "J Y Zhou and C Chun and A Subramanian and E P Simoncelli",
     % AUTHOR= "Jing Yang Zhou, Chanwoo Chun, Ajay Subramanian, Eero P. Simoncelli",
     BOOKTITLE= "Adv. Neural Information Processing Systems (NeurIPS),
     Workshop on UniReps: Unifying Representations in Neural Models",
     MONTH= "Dec",
     YEAR= 2023,
     URL= "https://openreview.net/forum?id=mDr0o2WU2n",
     PDF-URL= "https://openreview.net/pdf?id=mDr0o2WU2n",
     NOTE= "To appear",
}

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Targeted V1 comodulation supports task-adaptive sensory decisions

Caroline Haimerl, Douglas A. Ruff, Marlene Cohen, Cristina Savin, E. P. Simoncelli

Nature Communications, 14

2023

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models

Yixin Wang, Anthony Degleris, A. Williams, Scott W. Linderman

Journal of the American Statistical Association , 119: 2382-2395

November 9, 2023

Foveated metamers of the early visual system

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

bioRxiv 2023.05.18.541306

eLife

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2023

Foveated metamers of the early visual system

@ARTICLE{Broderick23,
  TITLE=        "Foveated metamers of the early visual system",
  AUTHOR=       "W F Broderick and G Rufo and J Winawer and E P Simoncelli",
  JOURNAL=      "eLife",
  MONTH=        "Nov",
  YEAR=         2023,
  DOI=          "10.7554/eLife.90554.1",
  URL=          "https://elifesciences.org/reviewed-preprints/90554",
  PDF-URL=      "https://www.cns.nyu.edu/pub/lcv/broderick23-preprint.pdf",
  PMID=         "35266962",
}

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Neural network adaptive coding efficiency and stochastic representational geometry

L. Duong

Dissertation

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2023

Neural network adaptive coding efficiency and stochastic representational geometry

@PHDTHESIS{Duong-phd,
     TITLE= "Neural network adaptive coding efficiency and stochastic
     representational geometry",
     AUTHOR= "Lyndon Duong",
     ADVISOR= "Eero P. Simoncelli and David J. Heeger",
     SCHOOL= "Center for Neural Science, New York University",
     ADDRESS= "New York, NY",
     YEAR= 2023,
     MONTH= "Aug",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/duong-phd-2023.pdf",
}

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