Probing experience-dependent transformation of… (F31DC024171) – Project Grant to University Of Utah

Abstract: The ability to generalize past experience to new situations is fundamental to learning, yet the neural mechanisms that enable the brain to transform sensory inputs into abstract, reusable representations remain poorly understood. In the olfactory system, stimulus information is initially encoded in distributed population patterns in piriform cortex (PC) and then relayed to downstream regions such as lateral entorhinal cortex (LEC), where computations more closely tied to memory and task structure are thought to emerge. Although this pathway suggests a progression from sensory encoding to abstraction, how these representations evolve through learning-and how prior experience shapes their expression across related tasks-remains largely unknown. This project investigates how odor representations are transformed from perception to memory across the PC to LEC axis and how these transformations support learning and generalization across related tasks. Studying these processes has been difficult for two reasons: IT has been technically challenging to record single-unit activity across sensory and associative regions simultaneously, and the parameter space of possible training curricula is vast and difficult to systematically explore in animals. I address these challenges by combining large- scale, dual-site neuropixels recordings with computational modeling using recurrent neural networks (rnns). This approach allows me to track how odor representations evolve from PC to LEC during learning, while rnns enable systematic exploration of curriculum and shaping parameters at scale. In my first year in the lab, i successfully implemented this integrated approach, using RNN modeling to guide experimental design and explore how training structure shapes neural dynamics. This work led to my CO- authorship on a study currently in revision at nature neuroscience (bowler, ET AL. 2025, biorxiv), which demonstrated that structured training curricula shape both strategy learning and neural dynamics in medial entorhinal cortex. Building on this foundation, my proposed research has two aims. In aim 1, i will determine how odor representations are transformed from PC to LEC at each stage learning by recording simultaneous neural activity from these two regions using chronic neuropixels probes during an olfactory-based task. These experiments will determine how sensory representations are shaped by curriculum and recurrent connectivity within and between PC and LEC. In aim 2, i will apply the same approaches as in aim 1 but will investigate how sensory representations change with prior experience to be generalizable to new cognitive demands, by training mice on two tasks. Complementary RNN modeling will be used to systematically explore how different curriculum structures influence generalization, providing testable predictions for in-vivo experiments. Together, this work will establish a mechanistic framework linking sensory representations, learning history, and memory-guided behavior, revealing how structured experience shapes the ability to flexibly generalize information across tasks.

Action	Obligated	Date
Amendment	$40,114	Jul 6, 2026
Amendment	$40,114	Jul 6, 2026
Original award	$0	Jul 6, 2026

Probing experience-dependent transformation of odor representations along the olfactory-medial temporal lobe axis using in-vivo and in-silico approaches

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