Research
In our lab we study neural networks underlying different behaviors, such as navigation, decision making and sensory processing. We investigate the dynamics and connectivity patterns of these networks, with a focus on the flexibility of neural networks and how it supports these complex processes. We study these networks in zebrafish larvae and juvenile, small transparent vertebrates ideally suited to investigate distributed networks.
Integration of spatial signals for navigation
Navigation is a complex cognitive process that requires the integration of multiple spatial signals. In order to navigate from one point to another, an animal must track its own position, monitor its heading direction, and maintain a representation of its desired destination. These signals must then be integrated to generate the appropriate motor commands that will guide the animal towards its goal. In this project, we aim to reveal the network architecture of the navigation network to understand how different spatial signals are integrated to guide behavior.
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In previous work, we already characterized the representation of several spatial signals in different brain regions. We revealed that several signals are topographically organized and align to one another in a specific brain region, the interpeduncular nucleus (IPN), which we identify as a potential site for integration of spatial signals for navigation. Currently, we aim to understand how this integration occurs.
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Short-term modulation in network computation
Navigation is a fundamental cognitive ability essential for survival in most animals. From the remarkable path integration and celestial navigation strategies observed in insects to the extraordinary spatial memory of caching birds, the ability to navigate efficiently through complex environments represents one of the most sophisticated computational challenges solved by neural systems. In the lab, we study the role of short-term plasticity in these computations and how it enables adaptive navigation.
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Many neural circuits can change their activity depending on available information. For example, when animals estimate their position and orientation in the absence of correct sensory feedback, they will quickly learn to ignore the sensory input and will rely on their own motor actions. When sensory feedback is present, animals will use it to better estimate their orientation and position. Zebrafish larvae, like other animals, change their behavior and neural circuits based on different contexts such as hunger level or sleep deprivation. Here we aim to uncover how short-term modulation enables neural networks to generate complex sensory representations of the environment, and how this process supports more sophisticated navigation strategies.
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Long-term plasticity and adaptive behaviors
Animal behavior and the neural circuits underlying it must be flexible and change based on context and past experience. Indeed, neural circuits can be modulated by past experience. Short-term changes in neural activity result in habituation or sensitization while long-term changes result in other forms of learning, as seen in classical conditioning. In the lab, we investigate long-term modulation of neural activity and how it supports adaptive behaviors. By developing novel behavior paradigms and tracking neural activity over days, we aim to uncover how long-term modulation of sensory representations allows neural networks to learn new rules and adapt to changing environments.
