Neural Circuit Discovery via Representation and Dynamics by Savik Kinger

Abstract: Neuroscience and AI share a bottleneck: while one can build (artificial) or record (biological) complex networks, we struggle to explain their functional circuitry; i.e., how they compute. In this talk I use whole-brain recordings from C. elegans, a canonical neurobiological system, as a concrete testbed for “circuit interpretability.” I then introduce two complementary inference approaches for turning high-dimensional activity data into mechanistic structure. Approach 1 treats circuit discovery as a representation problem: learn time-varying functional structure and uncover recurring, stimulus-dependent modules rather than a single static connectivity map. Approach 2 treats circuit discovery as a dynamics problem: go beyond correlation to estimate directed, time-lagged influence—i.e., which units appear to drive others and over what delays—using modern score-based generative modeling ideas. I will show how these ML methods produce testable hypotheses for biologists and, potentially, offer new avenues for understanding complex networks in AI.

Bio: Savik Kinger is a PhD candidate in Computer Science at Yale University, advised by Steven Zucker. His research focuses on developing methods to analyze biological and artificial neural networks, integrating ideas from machine learning, dynamical systems, and causal inference. He received Bachelor’s degrees in Math and Computer Science from Columbia University. He is supported by a Nathan Hale fellowship.