[WTI-trainee] Dissertation Defense: April 17, 2025 - Lianghui Peng, "Adaptation of learning dynamics and feature representations via the neural kernel"

[Guerrero-Medina, Giovanna]

Hi everyone,
See below for an invitation to join in a Phsyics Dissertation Thesis Defense that might be of interest.
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Giovanna Guerrero-Medina, PhD [A button for name playback in email signature]  <https://www.name-coach.com/giovanna-guerrero-medina>
She/Her/Ella
Director for Diversity Equity & Inclusion,
Wu Tsai Institute at Yale
giovanna.guerrero-medina at yale.edu<mailto:giovanna.guerrero-medina at yale.edu>
203.785.2915 (office), 616.643.7666 (cell)
wti.yale.edu<https://wti.yale.edu/>

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Date: Sat, Apr 12, 2025 at 5:30 PM
Subject: Dissertation Defense: April 17, 2025 - Lianghui Peng, "Adaptation of learning dynamics and feature representations via the neural kernel"
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Physics Dissertation Defense

[https://physics.yale.edu/sites/default/files/event-images/54445655713_c9491749f4_n.jpg]
Lianghui Peng
Yale University

Thursday, April 17, 2025
1:00 p.m.

Location: SPL 57

Zoom connection: 99649308503<https://click.message.yale.edu/?qs=2bcc820431f12b4fbbe7933481c0a9b521429e3c072f499361ef051aa88439b3538d6fd174f5abd315809a97e95daf3492133ea78d27716a>


Adaptation of learning dynamics and feature representations via the neural kernel

The ability to learn from experience is essential for both biological and artificial agents. In complex environments where experience is sparse relative to the multitude of features, agents must efficiently generalize by focusing on features relevant for the task. The generalization strategy, known as inductive bias, shapes the dynamics of learning. We introduce a neural kernel framework to characterize inductive biases of humans and artificial neural networks in category learning, linking neural representations with learning behavior. Our kernel models captured the learning trajectories of human subjects across two experiments, and elucidated the learning strategies of neural networks through feature modes. We developed methods for fitting kernels to behavioral data, revealing the adaptation of inductive bias in human subjects. We also implemented a neural network model with feature-based gain modulation, capable of adapting representations and inductive bias. In summary, we established a novel perspective for understanding learning and generalization in relation to neural representations, providing testable predictions for future neural and behavioral experiments.

Thesis Advisor: John Murray
Committee: Damon Clark, Ilker Yildirim, and Thierry Emonet.

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