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    A variational bayes approach to inferring neuronal network connectivity

    Author
    Smith, Devin, Cody
    ORCID
    https://orcid.org/0000-0001-5604-0499
    View/Open
    Smith_rpi_0185E_12145.pdf (1.702Mb)
    Other Contributors
    Kovacic, Gregor; Kramer, Peter; Holmes, Mark; Zhou, Douglas;
    Date Issued
    2022-12
    Subject
    Mathematics
    Degree
    PhD;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute (RPI), Troy, NY. Copyright of original work retained by author.;
    Metadata
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    URI
    https://hdl.handle.net/20.500.13015/6353
    Abstract
    Uncovering a neuronal circuit's structure is an important step toward developing a mechanistic understanding of that circuit's function. In this thesis, we develop an algorithm which can accurately reconstruct an integrate-and-fire neuronal network's connectivity from observations of its spike-train data. Our network reconstruction algorithm is designed to achieve two primary goals: efficiently recover network connectivity given minimal observed data, and quantify our uncertainty about each connection in the network. Pursuant to our first goal, we define a novel Bayesian statistical model for neuronal spike train data, uniquely structured around empirically observed characteristics of spike-trains from conductance based integrate-and-fire neuronal networks. In particular, the model directly accounts for the spike-reset-refractory-period dynamics characteristic of spiking neurons; additionally, the model's dimensionality is reduced by flexibly encoding the typical response a neuron has to presynaptic spikes. We show that building a model which accounts for the structure inherent in neuronal network spiking dynamics makes recovering network connectivity more data-efficient, especially for large networks. Furthermore, our Bayesian model has parameters which directly correspond to structural properties of the observed neuronal circuit. Most importantly, the model contains a connection-type parameter for each pair of observed neurons $(c \leftarrow j)$, which indicates if neuron $j$ is excitatorily presynaptic, inhibitorily presynaptic to, or disconnected from neuron $c$. Thus, the posterior distribution over these parameters facilitates direct inference of the network's connectivity, direct inference of the connection type (excitatory or inhibitory) between each connected pair, and local, pairwise estimates of our uncertainty about each inferred connection. An approximation to the Bayesian model's posterior distribution is derived using a variational Bayes algorithm which is carefully designed to retain meaningful probabilities over the model's connection type parameters.;
    Description
    December 2022; School of Science
    Department
    Dept. of Mathematical Sciences;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 license. No commercial use or derivatives are permitted without the explicit approval of the author.;
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