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Neural Circuits and Algorithms

133 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

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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}
}

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Video prediction using score-based conditional density estimation

P. Fiquet, E. P. Simoncelli

arXiv:2411.00842

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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}
}

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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

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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}
}"

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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

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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}
}

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Feature-guided score diffusion for sampling conditional densities

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

arXiv:2410.11646

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October 15, 2024

Feature-guided score diffusion for sampling conditional densities

@TECHREPORT{Kadkhodaie24b,
  AUTHOR=       "Z Kadkhodaie and S Mallat and E P Simoncelli",
  TITLE=        "Feature-guided score diffusion for sampling conditional densities",
  MONTH=        "Oct",
  YEAR=         "2024",
  INSTITUTION=  "arXiv.org e-prints",
  NUMBER=       "2410.11646",
  DOI=          "10.48550/arXiv.2410.11646",
  URL=          "https://arxiv.org/abs/2410.11646",
  PDF-URL=      "https://arxiv.org/pdf/2410.11646",
}

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Feature-guided score diffusion for sampling conditional densities

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

arXiv:2410.11646

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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}
}

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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

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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",
}

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Classification-Denoising Networks

Louis Thiry, F. Guth

arXiv:2410.03505

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October 4, 2024

Classification-Denoising Networks

@article{Thiry_Guth_arXiv:2410.03505,
      title={{Classification-Denoising Networks}},
      author={Louis Thiry and Florentin Guth},
      year={2024},
    publisher={Cornell Tech},
    journal = {arXiv e-prints},
    doi={10.48550/arXiv.2410.03505},
    abstract={Image classification and denoising suffer from complementary issues of lack of robustness or partially ignoring conditioning information. We argue that they can be alleviated by unifying both tasks through a model of the joint probability of (noisy) images and class labels. Classification is performed with a forward pass followed by conditioning. Using the Tweedie-Miyasawa formula, we evaluate the denoising function with the score, which can be computed by marginalization and back-propagation. The training objective is then a combination of cross-entropy loss and denoising score matching loss integrated over noise levels. Numerical experiments on CIFAR-10 and ImageNet show competitive classification and denoising performance compared to reference deep convolutional classifiers/denoisers, and significantly improves efficiency compared to previous joint approaches. Our model shows an increased robustness to adversarial perturbations compared to a standard discriminative classifier, and allows for a novel interpretation of adversarial gradients as a difference of denoisers.},
      eprint={2410.03505},
      archivePrefix={arXiv},
      OpenAccessUrl={https://arxiv.org/abs/2410.03505}
}

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A Rainbow in Deep Network Black Boxes

F. Guth, Brice Ménard, MD, Gaspar Rochette, S. Mallat

arXiv:2305.18512

Journal of Machine Learning Research, 25: 1-59

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2024

A Rainbow in Deep Network Black Boxes

@article{Guth_Mallat_arxiv:2305.18512_JMLR_2024,
      title={{A Rainbow in Deep Network Black Boxes}},
      author={\text{Guth, Florentin} and M\'{e}nard, Brice and Rochette, Gaspar and \text{Mallat, St\'{{e}}phane}},
doi={10.48550/arXiv.2305.18512},
Journal = {Journal of Machine Learning Research},
Publisher= {Journal of Machine Learning Research},
abstract={A central question in deep learning is to understand the functions learned by deep networks. What is their approximation class? Do the learned weights and representations depend on initialization? Previous empirical work has evidenced that kernels defined by network acti- vations are similar across initializations. For shallow networks, this has been theoretically studied with random feature models, but an extension to deep networks has remained elu- sive. Here, we provide a deep extension of such random feature models, which we call the rainbow model. We prove that rainbow networks define deterministic (hierarchical) kernels in the infinite-width limit. The resulting functions thus belong to a data-dependent RKHS which does not depend on the weight randomness. We also verify numerically our mod- eling assumptions on deep CNNs trained on image classification tasks, and show that the trained networks approximately satisfy the rainbow hypothesis. In particular, rainbow net- works sampled from the corresponding random feature model achieve similar performance as the trained networks. Our results highlight the central role played by the covariances of network weights at each layer, which are observed to be low-rank as a result of feature learning.},
    Volume={25},
Issue={350},
year_posted={2023},
year={2024},
month={Oct},
      eprint={2305.18512},
pages={1-59},
     url= {http://jmlr.org/papers/v25/23-1573.html},
      archivePrefix={arXiv},
      Open Access Url={https://arxiv.org/abs/2305.18512}, center={CCN}, acrosscenters={yes}, external={yes}, highlight={no}
}

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Video prediction using score-based conditional density estimation

P. Fiquet, E. P. Simoncelli

arXiv:2411.00842

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2024

Video prediction using score-based conditional density estimation

@TECHREPORT{Kadkhodaie24b,
  AUTHOR=       "Z Kadkhodaie and S Mallat and E P Simoncelli",
  TITLE=        "Feature-guided score diffusion for sampling conditional densities",
  MONTH=        "Oct",
  YEAR=         "2024",
  INSTITUTION=  "arXiv.org e-prints",
  NUMBER=       "2410.11646",
  DOI=          "10.48550/arXiv.2410.11646",
  URL=          "https://arxiv.org/abs/2410.11646",
  PDF-URL=      "https://arxiv.org/pdf/2410.11646",
}

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