Research

Overview

Our research explores how neural signals give rise to cognition, behavior, and disease-related states across multiple biological scales. We combine experimental systems neuroscience, computational modeling, and large-scale clinical data analysis to investigate how neural circuits encode, transform, and adapt information during learning, decision-making, and pathological conditions.

By integrating in vivo neural recordings, circuit perturbation, behavioral models, and population-level health data, we aim to bridge mechanistic neuroscience with clinically relevant insights.

1. Experimental Systems Neuroscience

We use in vivo optical recording and circuit-level manipulation to study neural dynamics during behavior. Our approaches include cellular-resolution calcium imaging, miniature fluorescence microscopy, fiber photometry, optogenetic and chemogenetic modulation, and behavioral paradigms designed to probe sensory processing, learning, motivation, and disease-related behavioral states.

These platforms allow us to observe how neural activity patterns emerge, reorganize, and contribute to cognition and behavior, while also testing the biological mechanisms that link circuit dynamics to behavioral outcomes.

2. Computational and Data-Driven Neuroscience

We develop and apply computational approaches to analyze complex neural and behavioral data. Our work combines machine learning, deep learning, interpretable AI, mathematical statistics, and computational neuroscience to characterize population-level neural dynamics, latent behavioral states, and learning-dependent circuit reorganization.

Beyond descriptive analysis, we aim to identify biologically meaningful patterns and principles that explain how neural systems process information and support adaptive behavior.

3. Clinical and Population Data Integration

To extend mechanistic findings toward human health, we integrate experimental neuroscience with large-scale clinical, healthcare claims, and population health data. By linking circuit-level mechanisms with real-world clinical phenotypes and health outcomes, we aim to build a translational framework that connects neural dynamics with disease mechanisms and population-level patterns.

This cross-scale approach enables us to explore how insights from neural circuits may inform clinically relevant biomarkers, disease stratification, and future therapeutic strategies.

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