CCN: Publications

Filter by Group

Computational Vision

Neural Circuits and Algorithms

NeuroAI and Geometric Data Analysis

Statistical Analysis of Neural Data

190 Publications

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

Export Citation

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",
}

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)

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

Export Citation

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}
}

Download Citation (BibTex)

Comparing noisy neural population dynamics using optimal transport distances

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

arXiv:2412.14421

Export Citation

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}
}

Download Citation (BibTex)

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

Export Citation

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}
}

Download Citation (BibTex)

Statistical Mechanics of Support Vector Regression

A. Canatar, S. Chung

arXiv:2412.05439

Export Citation

December 6, 2024

Statistical Mechanics of Support Vector Regression

@article{Canatar_Chung_arxiv:2412.05439,
title = {{Statistical Mechanics of Support Vector Regression}},
author = {Abdulkadir Canatar and SueYeon Chung},
acrosscenters = {no},
external = {no},
abstract = {A key problem in deep learning and computational neuroscience is relating the geometrical properties of neural representations to task performance. Here, we consider this problem for continuous decoding tasks where neural variability may affect task precision. Using methods from statistical mechanics, we study the average-case learning curves for ε-insensitive Support Vector Regression (ε-SVR) and discuss its capacity as a measure of linear decodability. Our analysis reveals a phase transition in the training error at a critical load, capturing the interplay between the tolerance parameter ε and neural variability. We uncover a double-descent phenomenon in the generalization error, showing that ε acts as a regularizer, both suppressing and shifting these peaks. Theoretical predictions are validated both on toy models and deep neural networks, extending the theory of Support Vector Machines to continuous tasks with inherent neural variability.},
eprint = {2412.05439},
highlight = {no},
archivePrefix = {arXiv},
year_posted = {2024},
Journal = {arXiv e-prints},
center = {CCN},
year = {2024},
OpenAccessURL = {https://arxiv.org/abs/2412.05439}
}

Download Citation (BibTex)

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

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

Journal of Vision, 24: 8

Export Citation

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.}
}

Download Citation (BibTex)

Representational learning by optimization of neural manifolds in an olfactory memory network

Bo Hu, Nesibe Z. Temiz, C. Chou , Peter Rupprecht, Claire Meissner-Bernard, Benjamin Titze, S. Chung , Rainer W. Freidrich

bioRxiv:2024.11.17.623906

Export Citation

November 18, 2024

Representational learning by optimization of neural manifolds in an olfactory memory network

@article{Hu_Chou_Chung_bioRxiv:2024.11.17.623906,
title = {{Representational learning by optimization of neural manifolds in an olfactory memory network}},
author = {Bo Hu and Nesibe Z. Temiz and Chi-Ning Chou and Peter Rupprecht and Claire Meissner-Bernard and Benjamin Titze and SueYeon Chung and Rainer W. Friedrich},
year_posted = {2024},
Journal = {bioRxiv e-prints},
center = {CCN},
acrosscenters = {no},
external = {yes},
highlight = {no},
year = {2024},
abstract = {Higher brain functions depend on experience-dependent representations of relevant information that may be organized by attractor dynamics or by geometrical modifications of continuous “neural manifolds”. To explore these scenarios we analyzed odor-evoked activity in telencephalic area pDp of juvenile and adult zebrafish, the homolog of piriform cortex. No obvious signatures of attractor dynamics were detected. Rather, olfactory discrimination training selectively enhanced the separation of neural manifolds representing task-relevant odors from other representations, consistent with predictions of autoassociative network models endowed with precise synaptic balance. Analytical approaches using the framework of manifold capacity revealed multiple geometrical modifications of representational manifolds that supported the classification of task-relevant sensory information. Manifold capacity predicted odor discrimination across individuals, indicating a close link between manifold geometry and behavior. Hence, pDp and possibly related recurrent networks store information in the geometry of representational manifolds, resulting in joint sensory and semantic maps that may support distributed learning processes.},
archivePrefix = {biorxiv},
eprint = {2024.11.17.623906},
OpenAccessURL = {https://www.biorxiv.org/content/10.1101/2024.11.17.623906v1}
}

Download Citation (BibTex)

Task-Relevant Covariance from Manifold Capacity Theory Improves Robustness in Deep Networks

William Yang, C. Chou , S. Chung

UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural Models

Export Citation

November 3, 2024

Task-Relevant Covariance from Manifold Capacity Theory Improves Robustness in Deep Networks

@article{Chou_Chung_UR2_2024,
title = {{Task-Relevant Covariance from Manifold Capacity Theory Improves Robustness in Deep Networks}},
author = {William Yang and Chi-Ning Chou and SueYeon Chung}},
abstract = {Analysis of high-dimensional representations in neuroscience and deep learning traditionally places equal importance on all points in a representation, potentially leading to significant information loss. Recent advances in manifold capacity theory offer a principled framework for identifying the computationally relevant points on neural manifolds. In this work, we introduce the concept of task-relevant class covariance to identify directions in representation-space supporting class discriminability. We demonstrate that scaling representations along these directions markedly improves simulated accuracy under distribution shift. Building on these insights, we propose AnchorBlocks, architectural modules that use task-relevant class covariance to align representations with a task-relevant eigenspace. By appending one AnchorBlock onto ResNet18, we achieve competitive performance in a standard domain adaptation benchmark (CIFAR-10C) against much larger robustness-promoting architectures. Our findings provide insight into neural population geometry and methods to interpret/build robust deep learning systems.},
acrosscenters = {no},
external = {yes},
highlight = {no},
year_posted = {2024},
center = {CCN},
OpenAccessURL = {https://openreview.net/forum?id=1jHvHtbgRb},
Journal = {UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural Models},
year = {2024}
}

Download Citation (BibTex)

WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes

Yicong Li, Wanhua Li, Qi Chen, Wei Huang, Yuda Zou, Xin Xiao, K. Shinomiya, P. Gunn, Nishika Gupta, Alexey Polilov, Yongchao Xu, Yueyi Zhang, Zhiwei Xiong, Hanspeter Pfister, Donglai Wei, J. Wu

IEEE Transactions on Medical Imaging, 43: 11

Export Citation

November 1, 2024

WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes

@article{Li_Shinomiya_Wu_arxiv:2302.00545_IEEEMI_2024,
title = {{WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes}},
author = {Li, Yicong and Li, Wanhua and Chen, Qi and Huang, Wei and Zou, Yuda and Xiao, Xin and Shinomiya, Kazunori and Gunn, Pat and Gupta, Nishika and Polilov, Alexey and Xu, Yongchao and Zhang, Yueyi and Xiong, Zhiwei and Pfister, Hanspeter and Wei, Donglai and Wu, Jingpeng},
acrosscenters = {no},
external = {yes},
abstract = {The size of image volumes in connectomics studies now reaches terabyte and often petabyte scales with a great diversity of appearance due to different sample preparation procedures. However, manual annotation of neuronal structures (e.g., synapses) in these huge image volumes is time-consuming, leading to limited labeled training data often smaller than 0.001% of the large-scale image volumes in application. Methods that can utilize in-domain labeled data and generalize to out-of-domain unlabeled data are in urgent need. Although many domain adaptation approaches are proposed to address such issues in the natural image domain, few of them have been evaluated on connectomics data due to a lack of domain adaptation benchmarks. Therefore, to enable developments of domain adaptive synapse detection methods for large-scale connectomics applications, we annotated 14 image volumes from a biologically diverse set of Megaphragma viggianii brain regions originating from three different whole-brain datasets and organized the WASPSYN challenge at ISBI 2023. The annotations include coordinates of pre-synapses and post-synapses in the 3D space, together with their one-to-many connectivity information. This paper describes the dataset, the tasks, the proposed baseline, the evaluation method, and the results of the challenge. Limitations of the challenge and the impact on neuroscience research are also discussed. The challenge is and will continue to be available at https://codalab.lisn.upsaclay.fr/competitions/9169. Successful algorithms that emerge from our challenge may potentially revolutionize real-world connectomics research and further the cause that aims to unravel the complexity of brain structure and function.},
eprint = {2302.00545},
highlight = {no},
archivePrefix = {arXiv},
year_posted = {2023},
Journal = IEEEMI,
center = {CCN},
OpenAccessURL = {https://ieeexplore.ieee.org/document/10530168/authors#authors},
year = {2024},
volume={43},
number={11},
pages={3719-3730},
doi={10.1109/TMI.2024.3400276}
}

Download Citation (BibTex)

Previous Page
Viewing
Next Page