Research direction
Neural coding
How visual information becomes stable, flexible population representations
Explore this directionWe study how visual systems transform variable sensory input into representations that remain reliable enough for perception yet flexible enough for behavior.
Our work connects neural coding, network dynamics, and learning with emerging tools for reading, modeling, and controlling brain activity. Select a direction below to see the questions we pursue, our related publications, and a concise view of where the field is heading.
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Dynamic networks
How brain state and context reshape communication across the visual hierarchy
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Learning rules
How experience organizes complex networks through simple, local mechanisms
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Brain reading
How neural activity can be translated into interpretable visual content
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Neural control
How closed-loop interfaces can test and shape neural computation
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NeuroAI
How neuroscience and artificial intelligence can constrain one another
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