Date of Award
7-3-2025
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Neural spike trains are shaped by both extrinsic inputs and intrinsic structural constraints. While prior work has focused on how spike timing encodes externally driven variables, such as stimuli or behavior, this dissertation explores whether principles that organize neural activity in space and time—such as genetic cell type, anatomical location, and arousal state—are also embedded in the spiking output of individual neurons. I hypothesize that these organizing principles are not quenched as irrelevant variability, but rather multiplexed within the neural code itself. To test this, I attempt to decode these principles from the spike trains of neurons recorded in behaving mice, and show that, like signals of stimulus or behavior, these principles are reliably embedded in the neural code. I demonstrate that the spike timing of individual neurons carries enough information to classify: (1) genetic cell type, using a novel deep-learning architecture (LOLCAT) on datasets from the Allen Institute; (2) anatomical location, across diverse brain regions, structures, and cortical layers in datasets spanning multiple labs and behavioral paradigms; and (3) arousal state, revealing a surprisingly local scale of this presumed global signal in freely-behaving mice. These signatures are consistently observed across neurons, indicating that any individual neuron may carry this embedded information. These findings expand beyond a moment-to-moment, stimulus-and-response-centric view of the neural code, revealing that individual spike trains carry high-dimensional, temporally embedded signatures of the principles that organize the brain. The spike train reveals not just what the brain does, but what it is. In doing so, this work reframes neural coding as the simultaneous representation of both function and form—suggesting that these embedded signatures of structure and state may be foundational, though often overlooked, dimensions of the neural code.
Language
English (en)
Chair and Committee
Keith Hengen
Committee Members
Bruce Carlson; Eva Dyer; Keith Hengen; Shinung Ching; Timothy Holy
Recommended Citation
Schneider, Aidan, "Robust Embeddings of Genetics, Anatomy, and State Decoded from a Neuron’s Activity" (2025). Arts & Sciences Theses and Dissertations. 3612.
https://openscholarship.wustl.edu/art_sci_etds/3612