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181 Publications

Normative theories of synaptic plasticity for representation and perceptual discrimination

Colin Bredenberg

Dissertation

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2022

Normative theories of synaptic plasticity for representation and perceptual discrimination

@PHDTHESIS{Bredenberg-phd,
     TITLE= "Normative theories of synaptic plasticity for representation
     and perceptual discrimination",
     AUTHOR= "Colin Bredenberg",
     ADVISOR= "Cristina Savin and Eero P. Simoncelli",
     SCHOOL= "Center for Neural Science, New York University",
     ADDRESS= "New York, NY",
     YEAR= 2022,
     MONTH= "Sep",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/bredenberg-phd.pdf",
}

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Neural Circuits for Dynamics-Based Segmentation of Time Serie

T. Tesileanu, S. Golkar, Samaneh Nasiri, A. Sengupta, D. Chklovskii

Neural Computation, 34 (4): 891–938

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2022

Neural Circuits for Dynamics-Based Segmentation of Time Serie

@article{10.1162/neco_a_01476,
    author = {Teşileanu, Tiberiu and Golkar, Siavash and Nasiri, Samaneh and Sengupta, Anirvan M. and Chklovskii, Dmitri B.},
    title = "{Neural Circuits for Dynamics-Based Segmentation of Time Series}",
    journal = {Neural Computation},
    volume = {34},
    number = {4},
    pages = {891-938},
    year = {2022},
    month = {03},
    abstract = "{The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on data sets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series.}",
    issn = {0899-7667},
    doi = {10.1162/neco_a_01476},
    url = {https://doi.org/10.1162/neco\_a\_01476},
    eprint = {https://direct.mit.edu/neco/article-pdf/34/4/891/2003154/neco\_a\_01476.pdf},
}

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Deep denoising for scientific discovery: A case study in electron microscopy

S. Mohan, Ramon Manzorro, Joshua L. Vincent, Binh Tang, Dev Y. Sheth, E. P. Simoncelli, David S. Matteson, Peter A. Crozier, Carlos Fernandez-Granda

IEEE Transactions on Computational Imaging, 8:585-597

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2022

Deep denoising for scientific discovery: A case study in electron microscopy

@ARTICLE{Mohan22,
     TITLE= "Deep denoising for scientific discovery: A case study in electron microscopy",
     AUTHOR= "S Mohan and R Manzorro and J L Vincent and B Tang and D Y
     Sheth and E P Simoncelli and D S Matteson and P A Crozier and
     C Fernandez-Granda",
     JOURNAL= "IEEE Trans. Computational Imaging",
     MONTH= "Jul",
     VOLUME= 8,
     PAGES= "585--597",
     YEAR= 2022,
     DOI= "10.1109/TCI.2022.3176536",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/mohan22-reprint.pdf",
}

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A common framework for discriminability and perceived intensity of sensory stimuli

J. Zhou, L. Duong, E. P. Simoncelli

bioRxiv 2022.04.30.490146

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2022

A common framework for discriminability and perceived intensity of sensory stimuli

@TECHREPORT{Zhou22a,
     TITLE= "A common framework for discriminability and perceived intensity of sensory stimuli",
     AUTHOR= "J Y Zhou and L R Duong and E P Simoncelli",
     MONTH= "May",
     YEAR= 2022,
     NUMBER= "2022.04.30.490146",
     JOURNAL= "bioRxiv",
     INSTITUTION= "bioRxiv",
     PUBLISHER= {Cold Spring Harbor Laboratory},
     URL= {https://www.biorxiv.org/content/10.1101/2022.04.30.490146v1},
     DOI= "10.1101/2022.04.30.490146",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/zhou22a.pdf",
}

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Effects of foveation on early visual representations

B. Broderick

Dissertation

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2022

Effects of foveation on early visual representations

@PHDTHESIS{Broderick-phd,
     TITLE= "Effects of foveation on early visual representations",
     AUTHOR= "William F. Broderick",
     ADVISOR= "Eero P. Simoncelli and Jonathan Winawer",
     SCHOOL= "Center for Neural Science, New York University",
     ADDRESS= "New York, NY",
     YEAR= 2022,
     MONTH= "May",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/broderick-phd.pdf",
}

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Flexible sensory information processing through targeted stochastic co-modulation

Caroline Haimerl

Dissertation

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2022

Flexible sensory information processing through targeted stochastic co-modulation

@PHDTHESIS{Haimerl-phd,
     TITLE= "Flexible sensory information processing through targeted stochastic co-modulation",
     AUTHOR= "Caroline Haimerl",
     ADVISOR= "Eero P. Simoncelli and Cristina Savin",
     SCHOOL= "Center for Neural Science, New York University",
     ADDRESS= "New York, NY",
     YEAR= 2022,
     MONTH= "May",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/haimerl-phd.pdf",
}

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Robust and interpretable denoising via deep learning

S. Mohan

Dissertation

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2022

Robust and interpretable denoising via deep learning

@PHDTHESIS{Mohan-phd,
     TITLE= "Robust and interpretable denoising via deep learning",
     AUTHOR= "Sreyas Mohan",
     ADVISOR= "Carlos Fernandez-Granda and Eero P. Simoncelli",
     SCHOOL= "Center for Data Science, New York University",
     ADDRESS= "New York, NY",
     YEAR= 2022,
     MONTH= "May",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/mohan-phd.pdf",
}

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Mapping spatial frequency preferences across human primary visual cortex

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

Journal of Vision, 22(4): 3

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2022

Mapping spatial frequency preferences across human primary visual cortex

@ARTICLE{Broderick22,
     TITLE= "Mapping spatial frequency preferences across human primary visual cortex",
     AUTHOR= "W F Broderick and E P Simoncelli and J Winawer",
     JOURNAL= "Journal of Vision",
     MONTH= "March",
     YEAR= 2022,
     VOLUME= "22",
     NUMBER= "3",
     DOI= "https://doi.org/10.1167/jov.22.4.3",
     URL= "https://jov.arvojournals.org/article.aspx?articleid=2778653",
     PDF-URL= "http://www.cns.nyu.edu/pub/lcv/broderick22-reprint.pdf",
}

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Computational mechanisms of distributed value representations and mixed learning strategies

S. Farashahi, A. Soltani

Nature Communications

2021

Neural optimal feedback control with local learning rules

D. Chklovskii, S. Golkar, S. Farashahi, J. Friedrich, A. Genkin, A. Sengupta

NeurIPS

2021

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