Artificial Neural Networks for Nonlinear System Identification of Neuronal Microcircuits

Author: Bagherian, Dawna Paria

Year: 2021

Degree: Dissertation (Ph.D.)

Advisor: Meister, Markus

Committee Members: Yue, Yisong; Tsao, Doris Y.; Perona, Pietro; Meister, Markus

Option: Bioengineering

DOI: 10.7907/rj2p-8g11

Abstract

This thesis explores the application of artificial neural networks (ANNs) to nonlinear system identification. We use neuronal microcircuits in the retina as a testbed for our technique, which relies upon the marriage of partial anatomical information with large electrophysiological datasets. Rather than a typical application of machine learning, our primary goal is not to predict the output of retinal circuits, but rather to uncover their structure. We begin with a theoretical exploration in a toy problem and provide a proof of unique identifiability under a specific set of conditions. We then perform empirical simulations in a number of different circuit architectures and explore the space of constraints and regularizers to demonstrate that this technique is feasible in a hyperparametric regime that lends itself well to neuroscience datasets. We then apply the technique to mouse retinal datasets and show that we can both recover known biological information as well as discover new hypotheses for biological exploration. We end with an exploration of active stimulus design algorithms to distinguish between circuit hypotheses.

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