Research vision:

The core of our work is the effort to understand the principles by which biological neural networks process information — principles that underlie perception, decision-making, learning, and memory.

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This question is not only central to neuroscience but is also becoming increasingly important for the future of artificial intelligence. It is now widely believed that major advances in AI will not emerge solely from scaling larger models and datasets, but from a deeper scientific understanding of how intelligent systems learn, generalize, adapt, and fail. By studying the brain — the most powerful and efficient learning system known — we aim to uncover principles that can inspire both neuroscience and next-generation AI systems.

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At the same time, our research has direct implications for human health. Understanding how neural circuits change during learning and disease may help reveal how abnormal experiences, aging, and neurological disorders disrupt brain function. In the long term, this work contributes to the search for effective treatments — and ultimately cures — for devastating neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease.

Our laboratory combines systems neuroscience, behavior, computational analysis, and neural circuit methods to address these questions across multiple levels of organization, from single neurons to large-scale neural networks.

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Our research is organized around three major themes:

• Decision-making, Learning and memory — investigating the cellular and circuit mechanisms that enable learning and decision-making.

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• Neural circuits of odor processing — studying how olfactory information is encoded, transformed, and linked to perception and behavior.

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• Neural mechanisms of Alzheimer’s disease — examining how neurodegenerative pathology alters neural activity, circuit function, and cognition and this can help detect these changes as early as possible.