To survive and succeed in the world, animals have evolved rich behavioral repertoires that enable them, for example, to navigate their surroundings, detect and classify relevant objects, locate conspecifics, pursue prey, and avoid predators. To be able to execute such behaviors, the brain must continuously solve a range of complex computational problems – such as filtering relevant signals from noisy sensory input, integrating information across modalities, assigning valence to different stimuli (e.g., conspecifics vs. predators), tracking changes in a dynamic environment, and selecting the best possible actions under uncertainty or different internal states.
My research interest is to study the computational algorithms that the brain uses to enable flexible behaviors and to understand how these algorithms are embedded in the synaptic circuitry of the vertebrate brain. We study these questions in the larval zebrafish, a small vertebrate with a transparent brain that displays a rich repertoire of robust, innate behaviors. To dissect the underlying neuronal circuits, our lab uses a comprehensive battery of experimental methods, including optical tools (functional imaging and optogenetics), electrophysiology, and connectomics. This enables us to study neuronal circuits across many scales, from synapse to behavior.

Questions that are of particular interest to us include:
– How can we infer the computational algorithms used by the zebrafish brain from studying behavior?
– How does the synaptic circuitry of the zebrafish brain implement these computational algorithms?
– How do the biophysical properties of neurons and of synapses contribute to these computations?