Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Structure and variability of optogenetic responses identify the operating regime of cortex

Agostina Palmigiano, Francesco Fumarola, Daniel P. Mossing, Nataliya Kraynyukova, View ORCID ProfileHillel Adesnik, Kenneth D. Miller
doi: https://doi.org/10.1101/2020.11.11.378729
Agostina Palmigiano
1Center for Theoretical Neuroscience, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: ap3676@columbia.edu
Francesco Fumarola
3Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, Japan
1Center for Theoretical Neuroscience, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Daniel P. Mossing
5Biophysics Graduate Group, University of California, Berkeley, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nataliya Kraynyukova
4Max Planck Institute for Brain Research, Frankfurt, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hillel Adesnik
6Department of Molecular and Cell Biology, University of California, Berkeley, United States
7The Helen Wills Neuroscience Institute
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Hillel Adesnik
Kenneth D. Miller
1Center for Theoretical Neuroscience, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY
2Dept. of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Predicting the response of the cortical microcircuit to perturbations is a prerequisite to determine the mechanisms that mediate its response to stimulus; yet, an encompassing perspective that describes the full ensemble of the network’s response in models that accurately recapitulate recorded data is still lacking. Here we develop a class of mathematically tractable models that exactly describe the modulation of the distribution of cell-type-specific calcium-imaging activity with the contrast of a visual stimulus. The inferred parameters recover signatures of the connectivity structure found in mouse visual cortex. Analysis of this structure subsequently reveals parameter-independent relations between the responses of different cell types to perturbations and each interneuron’s role in circuit-stabilization. Leveraging recent theoretical approaches, we derive explicit expressions for the distribution of responses to partial perturbations which reveal a novel, counter intuitive effect in the sign of response functions. Finally applying the theory to inferring feedback to V1 during locomotion, we find that it is predominantly mediated by both SOM and VIP modulation.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Contact Information: Agostina Palmigiano, Center for Theoretical Neuroscience, Zuckerman Institute, 3227 Broadway, New York,10027 NY, Ken Miller, Center for Theoretical Neuroscience, Zuckerman Institute, 3227 Broadway, New York,10027 NY, e-mail: kdm2103{at}columbia.edu

  • Nomenclature

    “i
    Short for population to which cell i belongs
    χ
    Linear response matrix of the Low-dimensional circuit
    Δα
    Variance of the input to population α
    κ and v
    low rank vectors that compose σ
    Embedded Image
    Variance in the population α
    ω
    Low-dimensional connectivity matrix
    ΠL
    Diagonal matrix with entries κ
    ΠR
    Diagonal matrix with entries v
    Σ
    Optogenetic targeting matrix
    σαβ
    matrix of the standard deviations of the weight matrix W
    τ
    Time constant
    ξ
    Power in a threshold power law input-output function
    A
    Diagonal matrix with factors to transform calcium to rates
    B
    Measuring matrix
    c
    Contrast value, usually normalized to 1
    E
    Error function
    F
    Diagonal matrix with the derivatives of f at the fixed point of the high-dimensional circuit
    f
    Input-output function /nonlinearity
    f′
    Derivative of f
    h
    External inputs to the network
    J
    Jacobian
    k
    Normalized entries of the Low-dimensional linear response matrix χ
    mα
    Mean firing rate in population α for HD model
    N
    Number of neurons in the HD system
    n
    Number of populations (different cell-types) in the network
    Nα
    Number of neurons in population α
    Pα
    Distribution of activity over population α
    qα
    Fraction of cells in population α: Nα /N
    R
    Linear response of the HD system
    r
    Activity, rα is the activity in population α
    R0
    Linear response of the HD system in the absence of disorder
    T
    Diagonal matrix of time constants
    uα
    Mean input to population α
    vα
    Second moment of the activity distributions in population α
    W
    Weight matrix of the high-dimensional model
    wαβ
    Mean connection strength form population β to population α
    Embedded Image
    Weight connecting neuron j in population β to neuron i in population α
    W0
    matrix of entries wαβ
    z
    Input current
    f′
    Diagonal matrix with the derivatives of f at the fixed point of the Low-dimensional circuit
    HD
    high-dimensional (i.e. N dimensional) model, with 4 populations
    HFP
    Homogeneous fixed point
    LD
    Low-dimensional (i.e. 4-dimensional) model
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
    Back to top
    PreviousNext
    Posted February 23, 2021.
    Download PDF
    Email

    Thank you for your interest in spreading the word about bioRxiv.

    NOTE: Your email address is requested solely to identify you as the sender of this article.

    Enter multiple addresses on separate lines or separate them with commas.
    Structure and variability of optogenetic responses identify the operating regime of cortex
    (Your Name) has forwarded a page to you from bioRxiv
    (Your Name) thought you would like to see this page from the bioRxiv website.
    CAPTCHA
    This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
    Share
    Structure and variability of optogenetic responses identify the operating regime of cortex
    Agostina Palmigiano, Francesco Fumarola, Daniel P. Mossing, Nataliya Kraynyukova, Hillel Adesnik, Kenneth D. Miller
    bioRxiv 2020.11.11.378729; doi: https://doi.org/10.1101/2020.11.11.378729
    Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
    Citation Tools
    Structure and variability of optogenetic responses identify the operating regime of cortex
    Agostina Palmigiano, Francesco Fumarola, Daniel P. Mossing, Nataliya Kraynyukova, Hillel Adesnik, Kenneth D. Miller
    bioRxiv 2020.11.11.378729; doi: https://doi.org/10.1101/2020.11.11.378729

    Citation Manager Formats

    • BibTeX
    • Bookends
    • EasyBib
    • EndNote (tagged)
    • EndNote 8 (xml)
    • Medlars
    • Mendeley
    • Papers
    • RefWorks Tagged
    • Ref Manager
    • RIS
    • Zotero
    • Tweet Widget
    • Facebook Like
    • Google Plus One

    Subject Area

    • Neuroscience
    Subject Areas
    All Articles
    • Animal Behavior and Cognition (4231)
    • Biochemistry (9124)
    • Bioengineering (6774)
    • Bioinformatics (23979)
    • Biophysics (12115)
    • Cancer Biology (9517)
    • Cell Biology (13772)
    • Clinical Trials (138)
    • Developmental Biology (7625)
    • Ecology (11682)
    • Epidemiology (2066)
    • Evolutionary Biology (15500)
    • Genetics (10636)
    • Genomics (14317)
    • Immunology (9476)
    • Microbiology (22825)
    • Molecular Biology (9087)
    • Neuroscience (48940)
    • Paleontology (355)
    • Pathology (1480)
    • Pharmacology and Toxicology (2566)
    • Physiology (3844)
    • Plant Biology (8323)
    • Scientific Communication and Education (1470)
    • Synthetic Biology (2295)
    • Systems Biology (6184)
    • Zoology (1300)