Doctor of Philosophy in Quantitative Biosciences

in the School of Mathematics

Zach Mobille

Will defend his dissertation

Information coding and structural motifs in spiking neural networks

Thursday, August 20, 2026

At 2:00pm EST
Price Gilbert Room 4222

Zoom Link: Zach Mobille Defense Link

Thesis Advisor:

Hannah Choi, Ph.D.

School of Mathematics

Georgia Institute of Technology

Committee Members:

Simon Sponberg, Ph.D.

School of Physics / School of Biological Sciences

Georgia Institute of Technology                                                

Bilal Haider, Ph.D.

Department of Biomedical Engineering

Georgia Institute of Technology & Emory University

Samuel Sober, Ph.D.
Department of Biomedical Engineering / Department of Biology

Georgia Institute of Technology & Emory University

Flavio Fenton, Ph.D.

School of Physics

Georgia Institute of Technology

Abstract:

The nervous systems of animals comprise complex network structures with units that interact nonlinearly at discrete times of activity, known as action potentials or “spikes”. How the topological network structure of the brain is related to its computational function in the form of precisely-timed spiking dynamics is a subject of ongoing research in computational and systems neuroscience. In this dissertation defense, I use mathematical models and methods to describe and understand the relationship between spiking activity and non-random network structure.

In the first part of my thesis, I will describe how macroscopic cascades of feedforward network convergence, a ubiquitous connectivity motif found in brain areas like the visual system, olfactory system, cerebellum-like circuits, and visuomotor pathways, promotes temporal coding strategies of dynamic stimuli in downstream populations of spiking neurons. In the second part, I will discuss a mean-field theory for the dynamics of 1- and 2-synapse connectivity motifs of excitatory and inhibitory neurons in large recurrent networks that evolve with excitatory and inhibitory spike timing dependent plasticity. Overall, this thesis deepens our understanding of how non-random connectivity and spiking activity are related in ways that reshape information processing in biological neural networks.