Learning Representations from Neural Population Dynamics: Addressing Neural Variability Across Scales

Abstract

Interactions between individual neurons, each characterized by distinct intrinsic physiological properties, collectively give rise to population responses underlying complex animal behaviors. These responses exhibit variable dynamics across trials, recording sessions, and behavioral contexts—arising from stochastic spiking at the trial level, electrode drift and neural plasticity across sessions, and task- or state-dependent modulation across behavioral contexts. This multiscale variability complicates the reliable extraction of scientific insights from population activity. Consequently, modeling and decoding from population activity necessitate methods capable of learning stable representations that capture the underlying structure of neuronal activity in the presence of neural variability caused by noise, partial observability, and domain shifts inherent in population recordings. In this dissertation, I present my studies that aim to extract useful information from population dynamics while addressing neural variability across different scales: single trials, recording sessions, and behavioral contexts. In the first study, I developed a spatiotemporal transformer to learn stable neural representations underlying stochastic firing activity of neural population on the single-trial basis. In the second study, I introduced a self-supervised framework for extracting time-invariant representations of individual neurons by modeling their dynamics across partially overlapping populations over multiple recording sessions. In the third study, I developed a lightweight adaptive framework for online neural decoding, enabling rapid and robust generalization in unseen sessions with minimal unlabeled calibration trials and no model fine-tuning. In the fourth study, I exploited the dependence of population dynamics on behavioral contexts and presented a decoding framework leveraging context-aware representations for effective decoding of speech from population activity. Together, these studies advance a representation-centric paradigm for neural population analysis—delivering generalizable abstractions that are robust across contexts, scale to large recordings, and leverage inductive biases embedded in the population—thereby enabling effective extraction of scientific insights from population analysis and paving a way towards high-performing and robust brain–computer interfaces.