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190 Publications

Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex of male macaques

Jean-Paul Noel, E. Balzani, Cristina Savin, D. Angelaki

bioRxiv:2023.07.30.551169

Nature Communications, 15:12

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July 9, 2024

Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex of male macaques

@article{Balzani_bioxiv2023.07.30.551169_NATCOM_2024,
Archiveprefix = {bioXiv},
Author = {Noel, Jean-Paul and \text{Balzani, Edoardo} and Savin, Cristina and Angelaki, Dora E.},
Doi = {10.1038/s41467-024-50203-5},
Eprint = {2023/07/31/2023.07.30.551169},
Journal = NATCOM,
Issue = {1},
month = {Jul},
Pages = {12},
Publisher = {Nature Portfolio},
Title = {{Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex of male macaques}},
Url = {https://doi.org/10.1038/s41467-024-50203-5},
OpenAccessURL = {https://www.biorxiv.org/content/early/2023/07/31/2023.07.30.551169},
Volume = {15},
Year = {2024},
year_posted = {2023},
center = {CCN},
acrosscenters = {no},
external = {yes},
highlight = {no},
abstract = {Natural behaviors occur in closed action-perception loops and are supported by dynamic and flexible beliefs abstracted away from our immediate sensory milieu. How this real-world flexibility is instantiated in neural circuits remains unknown. Here, we have male macaques navigate in a virtual environment by primarily leveraging sensory (optic flow) signals, or by more heavily relying on acquired internal models. We record single-unit spiking activity simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and the dorso-lateral prefrontal cortex (dlPFC). Results show that while animals were able to maintain adaptive task-relevant beliefs regardless of sensory context, the fine-grain statistical dependencies between neurons, particularly in 7a and dlPFC, dynamically remapped with the changing computational demands. In dlPFC, but not 7a, destroying these statistical dependencies abolished the area’s ability for cross-context decoding. Lastly, correlational analyses suggested that the more unit-to-unit couplings remapped in dlPFC, and the less they did so in MSTd, the less were population codes and behavior impacted by the loss of sensory evidence. We conclude that dynamic functional connectivity between neurons in prefrontal cortex maintain a stable population code and context-invariant beliefs during naturalistic behavior.}
}

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Open Data In Neurophysiology: Advancements, Solutions & Challenges

Colleen J Gillon, Cody Baker, Ryan Ly, E. Balzani, Bingni W Brunton, Manuel Schottdorf, Satrajit Ghosh, Noma Dehghani

PMCID: PMC11247910

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July 1, 2024

Open Data In Neurophysiology: Advancements, Solutions & Challenges

@article{Gillon_Balzani_arXiv:2407.00976v1,
Archiveprefix = {arXiv},
Author = {Colleen J. Gillon and Cody Baker and Ryan Ly and Edoardo Balzani and Bingni W. Brunton and Manuel Schottdorf and Satrajit Ghosh and Nima Dehghani},
Eprint = {2407.00976},
Journal = {arXiv e-prints},
Title = {{Open Data In Neurophysiology: Advancements, Solutions \& Challenges.}},
OpenAccessURL = {https://pmc.ncbi.nlm.nih.gov/articles/PMC11247910/},
year_posted = {2024},
center = {CCN},
acrosscenters = {no},
external = {yes},
highlight = {no},
year = {2024},
abstract = {Across the life sciences, an ongoing effort over the last 50 years has made data and methods more reproducible and transparent. This openness has led to transformative insights and vastly accelerated scientific progress(1,2). For example, structural biology(3) and genomics(4,5) have undertaken systematic collection and publication of protein sequences and structures over the past half-century, and these data have led to scientific breakthroughs that were unthinkable when data collection first began (e.g.(6)). We believe that neuroscience is poised to follow the same path, and that principles of open data and open science will transform our understanding of the nervous system in ways that are impossible to predict at the moment. To this end, new social structures along with active and open scientific communities are essential(7) to facilitate and expand the still limited adoption of open science practices in our field(8). Unified by shared values of openness, we set out to organize a symposium for Open Data in Neuroscience (ODIN) to strengthen our community and facilitate transformative neuroscience research at large. In this report, we share what we learned during this first ODIN event. We also lay out plans for how to grow this movement, document emerging conversations, and propose a path toward a better and more transparent science of tomorrow.}
}

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The neuron as a direct data-driven controller

J. Moore, A. Genkin, Magnus Tournoy, J. Pughe-Sanford, Rob R. de Ruyter van Steveninck, D. Chklovskii

arXiv:2401.01489

Proceedings of the National Academy of Sciences of the United States of America, 121: e2311893121

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June 24, 2024

The neuron as a direct data-driven controller

@article{Moore_Chklovskii_arxiv:2401.01489_PNAS_2024,
title = {{The neuron as a direct data-driven controller}},
author = {Jason J. Moore  and Alexander Genkin  and Magnus Tournoy  and Joshua L. Pughe-Sanford  and Rob R. de Ruyter van Steveninck  and Dmitri B. Chklovskii},
acrosscenters = {no},
external = {yes},
abstract = {Building upon the efficient coding and predictive information theories, we present a perspective that neurons not only predict but may also actively influence their future inputs through their outputs. We model neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing an advanced data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers. This innovative approach enables us to coherently explain various experimental findings that previously seemed unrelated. Our research has multiple potential implications, from the modeling of neuronal circuits to enabling biologically inspired artificial intelligence systems. In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch–Pitts–Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks.},
eprint = {2401.01489},
highlight = {no},
archivePrefix = {arXiv},
year_posted = {2024},
Journal = {Proceedings of the National Academy of Sciences of the United States of America},
center = {CCN},
OpenAccessURL = {https://www.pnas.org/doi/10.1073/pnas.2311893121},
year = {2024},
volume = {121},
number = {27},
pages = {e2311893121},
doi = {10.1073/pnas.2311893121}
}

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Privileged representational axes in biological and artificial neural networks

Meenakshi Khosla, A. Williams, Josh McDermott, N. Kanwisher

bioRxiv:2024.06.20.599957

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June 20, 2024

Privileged representational axes in biological and artificial neural networks

@article{Khosla_Williams_biorxiv:2024.06.20.599957,
title = {{Privileged representational axes in biological and artificial neural networks}},
author = {Khosla, Meenakshi and Williams, Alex H and McDermott, Josh and Kanwisher, Nancy},
abstract = {How do neurons code information? Recent work emphasizes properties of population codes, such as their geometry and decodable information, using measures that are blind to the native tunings (or {\textquoteleft}axes{\textquoteright}) of neural responses. But might these representational axes matter, with some privileged systematically over others? To find out, we developed methods to test for alignment of neural tuning across brains and deep convolutional neural networks (DCNNs). Across both vision and audition, both brains and DCNNs consistently favored certain axes for representing the natural world. Moreover, the representational axes of DCNNs trained on natural inputs were aligned to those in perceptual cortices, such that axis-sensitive model-brain similarity metrics better differentiated competing models of biological sensory systems. We further show that coding schemes that privilege certain axes can reduce downstream wiring costs and improve generalization. These results motivate a new framework for understanding neural tuning in biological and artificial networks and its computational benefits.Competing Interest StatementThe authors have declared no competing interest.},
acrosscenters = {no},
external = {yes},
highlight = {no},
eprint = {https://www.biorxiv.org/content/early/2024/06/20/2024.06.20.599957.full.pdf},
archivePrefix = {biorXiv},
journal = {biorXiv e-prints},
year_posted = {2024},
center = {CCN},
OpenAccessURL = {https://www.biorxiv.org/content/early/2024/06/20/2024.06.20.599957},
year = {2024},
}

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Disparate nonlinear neural dynamics measured with different techniques in macaque and human V1

J. Zhou, Matt Whitmire, Yuzhi Chen, Eyal Seidemann

Scientific Reports, 14:13193

June 8, 2024

Layerwise complexity-matched learning yields an improved model of cortical area {V2}

N. Parthasarathy, O J Henaff, E. P. Simoncelli

arXiv: 2312.11436

Trans. Machine Learning Research

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2024

Layerwise complexity-matched learning yields an improved model of cortical area {V2}

@ARTICLE{Parthasarathy24,
  TITLE=        "Layerwise complexity-matched learning yields an improved model of
                 cortical area {V2}",
  AUTHOR=       "N Parthasarathy and O J H\'{e}naff and E P Simoncelli",
  JOURNAL=      "Trans. Machine Learning Research",
  MONTH=        "Jun",
  YEAR=         2024,
  PUBLISHER=    "https://jmlr.org/tmlr/index.html",
  PDF-URL=      "https://www.cns.nyu.edu/pub/lcv/parthasarathy24.pdf",
  URL=          "https://openreview.net/forum?id=lQBsLfAWhj",
  NOTE=         "Featured Certification",
}

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HybridBNN: Joint Training of Deterministic and Stochastic Layers in Bayesian Neural Nets

A. Nejatbakhsh, Julien Boussard

Sixth Symposium on Advances in Approximate Bayesian Inference - Non Archival Track, 2024:1-11

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May 27, 2024

HybridBNN: Joint Training of Deterministic and Stochastic Layers in Bayesian Neural Nets

"@article{Nejatbakhsh_AABI24_20224,
title = {{HybridBNN: Joint Training of Deterministic and Stochastic Layers in Bayesian Neural Nets}},
author = {Amin Nejatbakhsh and Julien Boussard},
abstract = {Bayesian Neural Nets are proposed as flexible models that can provide calibrated uncertainty estimates for out-of-distribution data. Due to the high dimensionality of BNN posteriors and the intractability of exact inference, numerous approximate inference techniques have been proposed. However, issues persist. Some approaches lack a proper Bayesian formulation while others result in inexpressive or uncalibrated posteriors, defeating the primary purpose of BNNs. Recently, subspace inference has been proposed to overcome these challenges by running the inference on a lower-dimensional subspace of network parameters. While achieving promising results, these methods are mathematically involved and therefore extending them to general architectures and problems is challenging. Here, we propose a new subspace inference method---called HybridBNN---that divides the network weights into deterministic and stochastic subsets before training. We develop an expectation-maximization algorithm for the joint inference of the posterior over the stochastic weights as well as the optimization of the deterministic ones. HybridBNN achieves competitive prediction and calibration performance on two regression and classification toy datasets and a benchmark dataset for in and out-of-domain distributions. The simplicity and flexibility of HybridBNN make it a favorable candidate for developing generic calibrated models.},
acrosscenters = {no},
external = {yes},
highlight = {no},
center = {CCN},
OpenAccessURL = {https://openreview.net/forum?id=ea3ERGY8bm},
Journal = {Sixth Symposium on Advances in Approximate Bayesian Inference - Non Archival Track},
year = {2024}
}"

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Optimal compressed sensing for image reconstruction with diffusion probabilistic models

Ling-Qi Zhang, Z. Kadkhodaie, E. P. Simoncelli, D. H. Brainard

arXiv:2405.17456

Transactions on Machine Learning

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May 22, 2024

Optimal compressed sensing for image reconstruction with diffusion probabilistic models

@article{Zhang_Kadkhodaie_Simoncelli_arXiv:2405.17456,
title = {{Optimal compressed sensing for image reconstruction with diffusion probabilistic models}},
author = {Ling-Qi Zhang and Zahra Kadkhodaie and Eero P. Simoncelli and David H. Brainard},
year_posted = {2024},
Journal = {Transactions On Machine Learning},
center = {CCN},
acrosscenters = {no},
external = {yes},
highlight = {no},
year = {2025},
abstract = {We examine the problem of selecting a small set of linear measurements for reconstructing high-dimensional signals. Well-established methods for optimizing such measurements include principal component analysis (PCA), independent component analysis (ICA) and compressed sensing (CS) based on random projections, all of which rely on axis- or subspace-aligned statistical characterization of the signal source. However, many naturally occurring signals, including photographic images, contain richer statistical structure. To exploit such structure, we introduce a general method for obtaining an optimized set of linear measurements for efficient image reconstruction, where the signal statistics are expressed by the prior implicit in a neural network trained to perform denoising (known as a ``diffusion model''). We demonstrate that the optimal measurements derived for two natural image datasets differ from those of PCA, ICA, or CS, and result in substantially lower mean squared reconstruction error. Interestingly, the marginal distributions of the measurement values are asymmetrical (skewed), substantially more so than those of previous methods. We also find that optimizing with respect to perceptual loss, as quantified by structural similarity (SSIM), leads to measurements different from those obtained when optimizing for MSE. Our results highlight the importance of incorporating the specific statistical regularities of natural signals when designing effective linear measurements.},
archivePrefix = {arXiv},
eprint = {2405.17456},
OpenAccessURL = {https://openreview.net/forum?id=lmHh4FmPWZ}
}

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Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration

James Ryu, A. Nejatbakhsh, Mahdi Torkashvand, Sahana Gangadharan, Maedeh Seyedolmohadesin, Jinmahn Kim, L. Paninski, Vivek Venkatachalam

bioRxiv:2022.07.18.500485v1

PLOS Computational Biology, 20: 1*5

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May 20, 2024

Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration

"@article{Nejatbakhsh_biorxiv:2022.07.18.500485_PLOSCB_2024,
title = {{Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration}},
author = {Ryu, James AND Nejatbakhsh, Amin AND Torkashvand, Mahdi AND Gangadharan, Sahana AND Seyedolmohadesin, Maedeh AND Kim, Jinmahn AND Paninski, Liam AND Venkatachalam, Vivek},
acrosscenters = {no},
external = {yes},
abstract = {Tracking body parts in behaving animals, extracting fluorescence signals from cells embedded in deforming tissue, and analyzing cell migration patterns during development all require tracking objects with partially correlated motion. As dataset sizes increase, manual tracking of objects becomes prohibitively inefficient and slow, necessitating automated and semi-automated computational tools. Unfortunately, existing methods for multiple object tracking (MOT) are either developed for specific datasets and hence do not generalize well to other datasets, or require large amounts of training data that are not readily available. This is further exacerbated when tracking fluorescent sources in moving and deforming tissues, where the lack of unique features and sparsely populated images create a challenging environment, especially for modern deep learning techniques. By leveraging technology recently developed for spatial transformer networks, we propose ZephIR, an image registration framework for semi-supervised MOT in 2D and 3D videos. ZephIR can generalize to a wide range of biological systems by incorporating adjustable parameters that encode spatial (sparsity, texture, rigidity) and temporal priors of a given data class. We demonstrate the accuracy and versatility of our approach in a variety of applications, including tracking the body parts of a behaving mouse and neurons in the brain of a freely moving C. elegans. We provide an open-source package along with a web-based graphical user interface that allows users to provide small numbers of annotations to interactively improve tracking results.},
eprint = {https://www.biorxiv.org/content/10.1101/2022.07.18.500485v1.full},
highlight = {no},
archivePrefix = {biorxiv},
year_posted = {2022},
Journal = {PLOS Computational Biology},
center = {CCN},
OpenAccessURL = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012075},
year = {2024},
publisher = {Public Library of Science},
month = {05},
    volume = {20},
    number = {5},
    pages = {1-23},
doi = {10.1371/journal.pcbi.1012075}
}"

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Nonlinear classification of neural manifolds with contextual information

F. Mignacco, C. Chou , S. Chung

arXiv:2405.06851

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May 10, 2024

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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