CCN: Publications

Clear Filters

Filter by Group

Computational Vision

Neural Circuits and Algorithms

NeuroAI and Geometric Data Analysis

NeuroRSE

Statistical Analysis of Neural Data

220 Publications

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)

Video prediction using score-based conditional density estimation

P. Fiquet, E. P. Simoncelli

arXiv:2411.00842

Export Citation

October 30, 2024

Video prediction using score-based conditional density estimation

@article{Fiquet_Simoncelli_arxiv:2411.008422024,
    title={{Video prediction using score-based conditional density estimation}},
    author={ Pierre-\'{{E}}tienne H. Fiquet and Eero P. Simoncelli},
    publisher={Cornell Tech},
    abstract={Temporal prediction is inherently uncertain, but representing the ambiguity in nat- ural image sequences is a challenging high-dimensional probabilistic inference problem. For natural scenes, the curse of dimensionality renders explicit den- sity estimation statistically and computationally intractable. Here, we describe an implicit regression-based framework for learning and sampling the conditional density of the next frame in a video given previous observed frames. We show that sequence-to-image deep networks trained on a simple resilience-to-noise objective function extract adaptive representations for temporal prediction. Synthetic exper- iments demonstrate that this score-based framework can handle occlusion bound- aries: unlike classical methods that average over bifurcating temporal trajectories, it chooses among likely trajectories, selecting more probable options with higher frequency. Furthermore, analysis of networks trained on natural image sequences reveals that the representation automatically weights predictive evidence by its reliability, which is a hallmark of statistical inference.},
    year_posted={2024},
     Journal = {arXiv e-prints},
 center = {CCN},
 acrosscenters = {no},
 external = {no},
 highlight = {no},
 eprint={2411.00842},
      archivePrefix={arXiv},
    year = {2024},
OpenAccessURL={https://arxiv.org/abs/2411.00842}
}

Download Citation (BibTex)

Unsupervised discovery of family specific vocal usage in the Mongolian gerbil

R. Peterson, A. Choudhri, Catalin Mitelut, A. Tanelus, Athena Capo-Battaglia, A. Williams, David M Schneider, Dan H Sanes

bioRxiv:2023.03.11.532197

eLife, 12

Export Citation

October 28, 2024

Unsupervised discovery of family specific vocal usage in the Mongolian gerbil

@article{Peterson_Williams_biorxiv:2023.03.11.532197_EL_2024,
title = {{Unsupervised discovery of family specific vocal usage in the Mongolian gerbil}},
author = {Peterson, Ralph E and Choudhri, Aman and Mitelut, Catalin and Tanelus, Aramis and Capo-Battaglia, Athena and Williams, Alex H and Schneider, David M and Sanes, Dan H},
acrosscenters = {no},
external = {yes},
abstract = {In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.},
eprint = {https://www.biorxiv.org/content/10.1101/2023.03.11.532197v1},
highlight = {no},
archivePrefix = {BioRxiv},
year_posted = {2023},
Journal = eLife,
center = {CCN},
OpenAccessURL = {https://elifesciences.org/articles/89892},
year = {2024},
editor = {Takahashi, Daniel Y and Shinn-Cunningham, Barbara G},
volume = 12,
year = 2024,
month = {dec},
pub_date = {2024-12-16},
pages = {RP89892},
citation = {eLife 2024;12:RP89892},
doi = {10.7554/eLife.89892},
issn = {2050-084X},
publisher = {eLife Sciences Publications, Ltd}
}

Download Citation (BibTex)

Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices

C. Chun, S. Chung , D. D. Lee

arXiv:2410.17998

Export Citation

October 23, 2024

Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices

@article{Chun_Chung_arxiv:2410.17998_2024,
title = {{Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices}},
author = {Chanwoo Chun and SueYeon Chung and Daniel D. Lee},
abstract = {Analyzing the structure of sampled features from an input data distribution is challenging when constrained by limited measurements in both the number of inputs and features. Traditional approaches often rely on the eigenvalue spectrum of the sample covariance matrix derived from finite measurement matrices; however, these spectra are sensitive to the size of the measurement matrix, leading to biased insights. In this paper, we introduce a novel algorithm that provides unbiased estimates of the spectral moments of the kernel integral operator in the limit of infinite inputs and features from finitely sampled measurement matrices. Our method, based on dynamic programming, is efficient and capable of estimating the moments of the operator spectrum. We demonstrate the accuracy of our estimator on radial basis function (RBF) kernels, highlighting its consistency with the theoretical spectra. Furthermore, we showcase the practical utility and robustness of our method in understanding the geometry of learned representations in neural networks.},
acrosscenters = {no},
external = {yes},
highlight = {no},
eprint = {2410.17998},
archivePrefix = {arxiv},
year_posted = {2024},
center = {ccn},
OpenAccessURL = {https://arxiv.org/abs/2410.17998},
Journal = {arXiv e-prints},
year = {2024}
}

Download Citation (BibTex)

High-fidelity Image Restoration of Large 3D Electron Microscopy Volume

Yuri Kreinin, P. Gunn, D. Chklovskii, J. Wu

bioRxiv:2023.09.14.557785

Microscopy and Microanalysis, 30: 889-902

Export Citation

October 18, 2024

High-fidelity Image Restoration of Large 3D Electron Microscopy Volume

"@article{Gunn_Wu_biorxiv:2023.09.14.557785_MIMI_2024,
title = {{High-fidelity Image Restoration of Large 3D Electron Microscopy Volume}},
author = {Kreinin, Yuri and Gunn, Pat and Chklovskii, Dmitri and Wu, Jingpeng},
abstract = {Volume electron microscopy (VEM) is an essential tool for studying biological structures. Due to the challenges of sample preparation and continuous volumetric imaging, image artifacts are almost inevitable. Such image artifacts complicate further processing both for automated computer vision methods and human experts. Unfortunately, the widely used contrast limited adaptive histogram equalization (CLAHE) can alter the essential relative contrast information about some biological structures. We developed an image-processing pipeline to remove the artifacts and enhance the images without CLAHE. We apply our method to VEM datasets of a Microwasp head. We demonstrate that our method restores the images with high fidelity while preserving the original relative contrast. This pipeline is adaptable to other VEM datasets.},
acrosscenters = {no},
external = {yes},
highlight = {no},
eprint = {https://www.biorxiv.org/content/10.1101/2023.09.14.557785v1},
archivePrefix = {BioRxiv},
year_posted = {2023},
center = {CCN},
OpenAccessURL = {https://www.biorxiv.org/content/early/2023/09/17/2023.09.14.557785},
year = {2024},
journal = MIMI,
volume = {30},
number = {5},
pages = {889-902},
month = {10}
}"

Download Citation (BibTex)

Neuronal and Behavioral Responses to Naturalistic Texture Images in Macaque Monkeys

Corey M. Ziemba, Robbe L.T. Goris, Gabriel M. Stine, Richard K. Perez, E. P. Simoncelli, J. A. Movshon

bioXriv:2024.02.22.581645

The Journal of Neuroscience, 44: e0349242024

Export Citation

October 16, 2024

Neuronal and Behavioral Responses to Naturalistic Texture Images in Macaque Monkeys

@article{Simoncelli_JON_2024,
title = {{Neuronal and Behavioral Responses to Naturalistic Texture Images in Macaque Monkeys}},
author = {Ziemba,  Corey M. and Goris,  Robbe L. T. and Stine,  Gabriel M. and Perez,  Richard K. and Simoncelli,  Eero P. and Movshon,  J. Anthony},
abstract = {The visual world is richly adorned with texture, which can serve to delineate important elements of natural scenes. In anesthetized macaque monkeys, selectivity for the statistical features of natural texture is weak in V1, but substantial in V2, suggesting that neuronal activity in V2 might directly support texture perception. To test this, we investigated the relation between single cell activity in macaque V1 and V2 and simultaneously measured behavioral judgments of texture. We generated stimuli along a continuum between naturalistic texture and phase-randomized noise and trained two macaque monkeys to judge whether a sample texture more closely resembled one or the other extreme. Analysis of responses revealed that individual V1 and V2 neurons carried much less information about texture naturalness than behavioral reports. However, the sensitivity of V2 neurons, especially those preferring naturalistic textures, was significantly closer to that of behavior compared with V1. The firing of both V1 and V2 neurons predicted perceptual choices in response to repeated presentations of the same ambiguous stimulus in one monkey, despite low individual neural sensitivity. However, neither population predicted choice in the second monkey. We conclude that neural responses supporting texture perception likely continue to develop downstream of V2. Further, combined with neural data recorded while the same two monkeys performed an orientation discrimination task, our results demonstrate that choice-correlated neural activity in early sensory cortex is unstable across observers and tasks, untethered from neuronal sensitivity, and therefore unlikely to directly reflect the formation of perceptual decisions.},
acrosscenters = {no},
external = {yes},
highlight = {no},
center = {ccn},
OpenAccessURL = {https://www.jneurosci.org/content/44/42/e0349242024},
Journal = JON,
publisher = {Society for Neuroscience},
volume = {44},
ISSN = {1529-2401},
DOI = {10.1523/jneurosci.0349-24.2024},
number = {42},
publisher = {Society for Neuroscience},
month = aug,
pages = {e0349242024},
year = {2024}
}

Download Citation (BibTex)

Feature-guided score diffusion for sampling conditional densities

Z. Kadkhodaie, S. Mallat, E. P. Simoncelli

arXiv:2410.11646

Export Citation

October 15, 2024

Feature-guided score diffusion for sampling conditional densities

@article{Kadkhodaie_Simoncelli_arxiv:2410.11646,
 title={{Feature-guided score diffusion for sampling conditional densities}},
 author={Kadkhodaie, Zahra and Mallat, St\'{{e}}phane and Simoncelli, Eero p},
 year_posted={2024},
 Journal = {arXiv e-prints},
 center = {CCN and CCM},
 acrosscenters = {yes},
 external = {no},
 highlight = {no},
year = {2024},
 abstract = {Score diffusion methods can learn probability densities from samples. The score of the noise-corrupted density is estimated using a deep neural network, which is then used to iteratively transport a Gaussian white noise density to a target density. Variants for conditional densities have been developed, but correct estimation of the corresponding scores is difficult. We avoid these difficulties by introducing an algorithm that guides the diffusion with a projected score. The projection pushes the image feature vector towards the feature vector centroid of the target class. The projected score and the feature vectors are learned by the same network. Specifically, the image feature vector is defined as the spatial averages of the channels activations in select layers of the network. Optimizing the projected score for denoising loss encourages image feature vectors of each class to cluster around their centroids. It also leads to the separations of the centroids. We show that these centroids provide a low-dimensional Euclidean embedding of the class conditional densities. We demonstrate that the algorithm can generate high quality and diverse samples from the conditioning class. Conditional generation can be performed using feature vectors interpolated between those of the training set, demonstrating out-of-distribution generalization.},
eprint={2410.11646},
archivePrefix={arXiv},
OpenAccessURL={https://arxiv.org/abs/2410.11646}
}

Download Citation (BibTex)

Comparing the local information geometry across several image representations

D. Lipshutz, J. Feather, S. Harvey, A. Williams, E. P. Simoncelli

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

Export Citation

October 10, 2024

Comparing the local information geometry across several image representations

@INPROCEEDINGS{Lipshutz24d,
    AUTHOR=     "D Lipshutz* and J Feather* and S E Harvey and A H Williams and  E P Simoncelli",
  TITLE=        "Comparing the local information geometry across several image representations",
  BOOKTITLE=    "Proc. UniReps: Unifying Representations in Neural Models,
                 A workshop at Neural Information Processing Systems (NeurIPS)",
  ADDRESS=      "Vancouver",
  MONTH=        "Dec",
  YEAR=         2024,
  URL=          "https://openreview.net/forum?id=qwVRKFj8dM",
}

Download Citation (BibTex)

Previous Page
Viewing
Next Page