Living organisms and intelligent machines operate in environments that are constantly changing. A fundamental challenge is therefore to maintain stable function while remaining flexible enough to learn, adjust, and respond to new situations.

Our research investigates the mechanisms that enable adaptive behavior across biological and artificial systems. We study how agents perceive environmental changes, evaluate their significance, and modify their internal state or actions to achieve their goals under uncertainty.

We investigate adaptation across multiple levels of organization, from neural circuits and brain dynamics to cognition, behavior, and artificial intelligence systems. A central question is how intelligent systems balance stability and flexibility: preserving acquired knowledge while remaining open to new information.

By combining neuroscience, robotics, machine learning, and cognitive science, we aim to uncover the principles that allow natural and artificial agents to adapt effectively in complex and uncertain environments.

Adaptation becomes even more challenging when the environment includes other adaptive agents. Humans, animals, and intelligent machines must continuously adjust not only to the physical world, but also to the behaviors, intentions, and expectations of others.

Our research explores the mechanisms that support successful social interaction, coordination, and collaboration. We investigate how agents perceive and predict the actions of others, establish shared understanding, and adapt their behavior in real time.

These processes rely on continuous feedback loops in which each agent adapts to others while simultaneously influencing their behavior. Such mechanisms support the emergence of shared representations, joint goals, and robust collaboration under uncertainty.

Understanding social adaptation is essential for explaining human interaction and for developing artificial systems capable of natural, trustworthy, and effective collaboration with people.